{"id":11089,"date":"2026-02-09T14:08:02","date_gmt":"2026-02-09T14:08:02","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics\/?page_id=11089"},"modified":"2026-04-09T14:15:14","modified_gmt":"2026-04-09T14:15:14","slug":"technology-showcases","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics\/technology-showcases\/","title":{"rendered":"Technology Showcases"},"content":{"rendered":"<!--themify_builder_content-->\n<div id=\"themify_builder_content-11089\" data-postid=\"11089\" class=\"themify_builder_content themify_builder_content-11089 themify_builder tf_clear\">\n                    <div  data-css_id=\"6s9x426\" id=\"homepage-intro\" data-lazy=\"1\" class=\"module_row themify_builder_row fullwidth_row_container tb_6s9x426 tb_first tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_vssg426 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_sbc2426\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col-full tb_9trz426 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_9nje426   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h1>Technology Showcases<\/h1>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module text -->\n<div  class=\"module module-text tb_q338185   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Please note<\/strong>: Technology Showcase presentations are part of the conference Track Sessions. As such, they are educational (not commercial) and can feature <br>case studies which may include use of the exhibitor&#8217;s products and services.\u00a0Technology Showcase content should also relate to the 7 Domains of the <br>INFORMS Analytics Framework.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_hxm5717 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_idtf718 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_59ng927   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h2>Monday, April 13<\/h2>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"gurobi\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-gurobi tb_cif4683 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_kyh3683 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_c7rq898 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-260x65.jpg\" width=\"260\" height=\"65\" class=\"wp-post-image wp-image-12869\" title=\"600x150px_Gurobi_Logo_Blue\" alt=\"600x150px_Gurobi_Logo_Blue\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-260x65.jpg 260w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-300x75.jpg 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-250x62.jpg 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-275x68.jpg 275w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue.jpg 600w\" sizes=\"auto, (max-width: 260px) 100vw, 260px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_4k58683 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_rkov683   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>10-10:50am |\u00a0Camellia 2<\/strong><\/p>\n<p><strong>AI Innovations in Optimization<\/strong><br><strong>Presented by: Caroline Weinberg and Jerry Yurchisin<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_3sio922 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-3sio922-0\" class=\"tb_title_accordion\" aria-controls=\"acc-3sio922-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-3sio922-0-content\" data-id=\"acc-3sio922-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_qida940\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_jlwr940 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_1fw7940   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>The Gurobi AI Innovation Lab is developing numerous methods to integrate AI and Optimization.\u00a0\u00a0 These include Optimization Proxies to dramatically accelerate times to find good solutions and tools to facilitate both the model creation process and the interpretation of the subsequent optimization results.\u00a0\u00a0 This presentation will describe results from methods already underway, as well as early findings from methods still in development.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"jmp\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-jmp tb_gbpf21 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_2jvs21 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_nk4n359 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/JMP-web_ready_company_logo-250x68.png\" width=\"250\" height=\"68\" class=\"wp-post-image wp-image-11714\" title=\"JMP web_ready_company_logo\" alt=\"JMP web_ready_company_logo\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/JMP-web_ready_company_logo-250x68.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/JMP-web_ready_company_logo-300x82.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/JMP-web_ready_company_logo.png 557w\" sizes=\"auto, (max-width: 250px) 100vw, 250px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_3vxd21 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_u3bp21   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>10-10:50am | Camellia 3<\/strong><\/p>\n<p><strong>Bayesian Optimization: Iterative Learning for Optimizing Complex Products and Processes<\/strong><br><strong>Presented by: Ross Metusalem<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_hui4319 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-hui4319-0\" class=\"tb_title_accordion\" aria-controls=\"acc-hui4319-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-hui4319-0-content\" data-id=\"acc-hui4319-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_wotw338\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_wpf0338 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_awdg338   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Bayesian Optimization (BayesOpt) is an iterative learning method for efficiently optimizing black-box or expensive-to-evaluate functions, with broad applications in process and product optimization. Starting with a small set of observations, BayesOpt fits a flexible machine learning-style model to the data and uses that model\u2019s predictions and uncertainty in those predictions to determine the best next observation to collect. Iterating on this process, BayesOpt balances exploration the factor space with exploitation of potential optima to identify a globally optimal solution. This Technology Showcase will establish the foundational concepts underlying BayesOpt, discuss when to consider using it, and present a case study of the technique in action.<\/p>\n<p><strong>Related Domains:\u00a0<\/strong><\/p>\n<ul>\n<li>Domain III: Data<\/li>\n<li>Domain IV: Methodology (Approach) Framing<\/li>\n<li>Domain V: Analytics\/Model Development<\/li>\n<\/ul>\n<p><strong>Relevant to:<\/strong><\/p>\n<ul>\n<li>Essential (Early Career)<\/li>\n<li>Professional (Mid-Career)<\/li>\n<li>Executive (Senior Level)<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"decisionbrain\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-decisionbrain tb_i5cl926 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_osbi926 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_lx42926 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <a href=\"https:\/\/decisionbrain.com\/\">\n                   <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color-1024x251-250x61.png\" width=\"250\" height=\"61\" class=\"wp-post-image wp-image-11153\" title=\"DB-Logo-Color\" alt=\"DB-Logo-Color\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color-1024x251-250x61.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color-300x73.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color-1024x251.png 1024w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color-768x188.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color.png 1500w\" sizes=\"auto, (max-width: 250px) 100vw, 250px\" \/>        <\/a>\n    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_q2t5926 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_qj11926   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>11-11:50am | Camellia 3<\/strong><\/p>\n<p><strong>The Last Mile of OR: Building Production-Ready Decision Tools with DB Gene Studio<br>Presented by: Justin Clark<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_i1w9326 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-i1w9326-0\" class=\"tb_title_accordion\" aria-controls=\"acc-i1w9326-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-i1w9326-0-content\" data-id=\"acc-i1w9326-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_grcr343\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_ypyq343 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_4xbh343   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>You have a strong OR team. They&#8217;ve built sophisticated planning and scheduling models, tuned constraints, and found solutions that genuinely improve operations. But the models live in notebooks, the results get copy-pasted into spreadsheets, and the business stakeholders who need to act on the outputs can&#8217;t access them without going through a developer. The math is solved. The deployment isn&#8217;t.<\/p>\n<p>This session presents a practical framework for closing that gap. Using a workforce planning problem as the working example, you&#8217;ll learn how to take a Python model all the way to a production-ready decision-support application \u2014 with interactive UIs business users can operate independently, AI-assisted constraint modeling using Claude, and seamless integration with existing data systems and solvers.<\/p>\n<p>The development environment used throughout is DB Gene Studio, purpose-built for OR teams who want to own the full journey from model to deployment without dedicated engineering support. DB Gene Studio is solver-agnostic and designed to complement the tools and systems organizations already use.<\/p>\n<p><strong>Related Domain: <\/strong><\/p>\n<ul>\n<li>Domain V: Analytics\/Model Development<\/li>\n<\/ul>\n<p><strong>Relevant to:<\/strong><\/p>\n<ul>\n<li>Professional (Mid-Career),<\/li>\n<li>Executive (Senior Level) and<\/li>\n<li>Associate (Early Career).<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"gams\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-gams tb_hyg0968 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_9dub968 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_lfie968 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <a href=\"https:\/\/www.gams.com\/\">\n                   <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo-250x100.png\" width=\"250\" height=\"100\" class=\"wp-post-image wp-image-10582\" title=\"GAMS-Logo\" alt=\"GAMS-Logo\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo-250x99.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo-300x120.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo-275x110.png 275w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo-200x80.png 200w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo.png 500w\" sizes=\"auto, (max-width: 250px) 100vw, 250px\" \/>        <\/a>\n    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_i123968 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_1sb7968   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>11-11:50am |\u00a0Camellia 2<\/strong><\/p>\n<p><strong>GAMSPy: Powering Mathematical Optimization and Machine Learning in Python<br>Presented by: Adam Christensen &amp; Steven Dirkse<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_ch5j306 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-ch5j306-0\" class=\"tb_title_accordion\" aria-controls=\"acc-ch5j306-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-ch5j306-0-content\" data-id=\"acc-ch5j306-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_3ydr331\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_l235331 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_vb1x331   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>This showcase offers a glimpse into the world of modeling with GAMSPy \u2013 a powerful mathematical optimization package that merges the high-performance GAMS execution system with the flexibility of the Python language.\u00a0 The result is a complete mathematical optimization package that can be deployed anywhere \u2013 effortlessly.\u00a0<\/p>\n<p>Join us to explore GAMSPy&#8217;s fundamental functionalities through practical examples. We&#8217;ll cover everything from defining sets, parameters, variables, and equations to solving models and retrieving results, all within a familiar Python environment. Beyond the basics, we&#8217;ll also provide a glimpse into more advanced Machine Learning features, demonstrating how GAMSPy can streamline complex modeling workflows and enhance your analytical capabilities.<\/p>\n<p>Whether you&#8217;re a seasoned GAMS user looking to integrate with Python or a Python user curious about optimization, this workshop will equip you with essential skills needed to get started and demonstrate what is possible with GAMSPy.<\/p>\n<p><strong>Related Domains:<\/strong><\/p>\n<ul>\n<li>Domain III: Data<\/li>\n<li>Domain V: Analytics\/Model Development<\/li>\n<li>Domain VI: Deployment<\/li>\n<\/ul>\n<p><strong>Relevant to:<\/strong><\/p>\n<ul>\n<li>Essential (Early Career)<\/li>\n<li>Professional (Mid-Career)<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"bayesia\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-bayesia tb_w4df37 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_cx0637 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_565e747 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/Bayesia_Logo_Transparent_jparaa.svg\" width=\"250\" height=\"92\" class=\"wp-post-image wp-image-12168\" title=\"Bayesia_Logo_Transparent_jparaa\" alt=\"Bayesia_Logo_Transparent_jparaa\">    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_c9oy37 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_p37837   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>1-1:50pm |\u00a0Camellia 2<\/strong><\/p>\n<p><strong>Overcoming the Limits of LLMs: Decision-Making Under Uncertainty with Causal Models<br><\/strong><strong>Presented by: Stefan Conrady<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_3iot512 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-3iot512-0\" class=\"tb_title_accordion\" aria-controls=\"acc-3iot512-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Descripton<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-3iot512-0-content\" data-id=\"acc-3iot512-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_31i5533\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_ik0l533 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_n5lq533   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Large language models (LLMs) have rapidly become central tools for analysis, explanation, and recommendation. Their apparent generality has led to a widespread perception that they can support virtually any analytics task. However, in real-world decision-making contexts, especially those involving uncertainty, interventions, and trade-offs, additional modeling capabilities are required.<\/p>\n<p>This Technology Showcase introduces a practical framework for selecting and combining modeling approaches based on three key dimensions: learning granularity, representational abstraction, and inference semantics. Using this framework, we demonstrate how different analytics technologies, including statistical models, neural networks, LLMs, and Bayesian networks, serve distinct roles in the analytics workflow rather than acting as substitutes.<\/p>\n<p>The session focuses on how Bayesian networks and influence diagrams enable explicit representation of uncertainty, causal reasoning, and decision optimization. Through live demonstrations and case examples, we show how these models support scenario analysis, intervention planning, and value-of-information calculations, helping organizations determine not only what is likely to happen, but what actions should be taken and what additional information is worth acquiring.<\/p>\n<p>We also illustrate how LLMs can be integrated into this workflow to support knowledge elicitation, model structuring, and communication, while relying on structured probabilistic models for transparent and accountable decision support.<\/p>\n<p>Attendees will leave with a practical understanding of how to position different modeling technologies within their analytics stack and how to build hybrid solutions that combine generative AI with causal and decision-theoretic modeling for high-stakes applications.<\/p>\n<p><strong>Related Domain:<\/strong><\/p>\n<ul>\n<li>Domain IV: Methodology<\/li>\n<\/ul>\n<p><strong>Relevant to:<\/strong><\/p>\n<ul>\n<li>Professional (Mid-Career)<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"ormae\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-ormae tb_rozk693 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_7tss693 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_brah427 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-723x1024-150x212.png\" width=\"150\" height=\"212\" class=\"wp-post-image wp-image-12192\" title=\"ORMAE web_ready_company_logo V\" alt=\"ORMAE web_ready_company_logo V\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-723x1024-150x212.png 150w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-212x300.png 212w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-723x1024.png 723w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-768x1087.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-723x1024-100x141.png 100w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-723x1024-140x198.png 140w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V.png 1013w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_pc5z693 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_if1e693   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>1-1:50pm | Camellia 3<\/strong><\/p>\n<p><strong>The Smart Plant: <\/strong><strong>AI-Powered Real-Time Optimization for Complex Industrial Operations<br>Presented by: Amit Garg and William Lopez<\/strong><br><br><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_g582258 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-g582258-0\" class=\"tb_title_accordion\" aria-controls=\"acc-g582258-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-g582258-0-content\" data-id=\"acc-g582258-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_fz4m273\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_wbmn273 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_2l45273   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>In process-based industries, operational leadership faces a fundamental question: How can organizations continuously determine the optimal combination of raw materials to maximize product value, maintain asset health, control costs, and meet regulatory requirements\u2014while operating in real time and at enterprise scale?<\/p>\n<p>This presentation describes the architecture and implementation of an AI-driven Decision Intelligence platform\u00a0developed by ORMAE\u00a0that transforms this problem into a measurable source of operational advantage. The platform was initially designed to address copper concentrate blend optimization\u2014one of the most computationally intensive problems in base metal smelting. Its design principles and technology architecture are broadly applicable to industrial operations where feed composition, procurement decisions, and process stability are tightly interconnected.<\/p>\n<p>The system integrates combinatorial optimization, physics-based metallurgy and process simulation, AI agent orchestration, and enterprise system integration. It evaluates millions of possible input combinations within minutes and dynamically responds to changing operating conditions, supply disruptions, and commercial constraints. High-performance computing enables large-scale sourcing scenario analysis, while integration with plant control systems creates a closed-loop link between digital recommendations and physical operations.<\/p>\n<p>Deployment results demonstrate significant improvements across smelting operations. Decision time for concentrate blending was reduced from 72 hours to less than 6 hours, procurement efficiency improved substantially, plant stability increased, and cross-functional collaboration strengthened across procurement, operations, finance, and commercial teams.<\/p>\n<p>Attendees from metals, refining, chemicals, cement, fertilizers, battery materials, and advanced manufacturing will gain a replicable framework for implementing AI-driven decision intelligence in complex industrial environments.<\/p>\n<p><strong>Related Domains:<\/strong><\/p>\n<ul>\n<li>Domain I: Business Problem (Question) Framing<\/li>\n<li>Domain II: Analytics Problem Framing<\/li>\n<li>Domain III: Data<\/li>\n<li>Domain IV: Methodology (Approach) Framing<\/li>\n<li>Domain V: Analytics\/Model Development<\/li>\n<li>Domain VI: Deployment<\/li>\n<li>Domain VII: Analytics Solution Lifecycle Management<\/li>\n<\/ul>\n<p><strong>Relevant to:<\/strong><\/p>\n<ul>\n<li>Essential (Early Career)<\/li>\n<li>Professional (Mid-Career)<\/li>\n<li>Executive (Senior Level)<\/li>\n<\/ul>\n<p>\u00a0<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"artelys\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-artelys tb_bj6919 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_zpiv19 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_zi0o866 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/Arteyls2-250x90.png\" width=\"250\" height=\"90\" class=\"wp-post-image wp-image-11268\" title=\"Arteyls2\" alt=\"Arteyls2\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/Arteyls2-250x90.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/Arteyls2-300x109.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/Arteyls2-768x279.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/Arteyls2.png 787w\" sizes=\"auto, (max-width: 250px) 100vw, 250px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_oymq19 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_mhw119   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>2-2:50pm | Camellia 3<\/strong><\/p>\n<p><strong>An AI You Can Trust: Driving Analytics Adoption in Complex Manufacturing through Acceptability<br><\/strong><strong>Presented by: Louis-Pierre Campeau<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_2xa6581 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-2xa6581-0\" class=\"tb_title_accordion\" aria-controls=\"acc-2xa6581-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-2xa6581-0-content\" data-id=\"acc-2xa6581-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_w2g0601\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_m5o6601 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_m5m7601   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>In the production of highly customized goods with extensive production chains, traditional predictive models face the &#8220;black box&#8221; barrier. While technical accuracy is often the focus of analytics developments, the ultimate success of an initiative strongly depends on user acceptability and integration into daily decision-making.<\/p>\n<p>This presentation details the development and implementation of a predictive model designed for a complex manufacturing environment. We shift the focus from pure algorithmic performance to a methodology rooted in collaborative design and transparency. By involving end users throughout the development lifecycle, we ensured the tool was not only technically robust but also intuitive and trustworthy for those managing the actual work.<\/p>\n<p>We will explore the specific methodological framework used to bridge the gap between data science and operational reality. Along the way, we will investigate the concept of acceptability, its implication for the success of a project and common biases. This case study demonstrates how non-technical aspects of a technical project can truly be the driving force of adoption.<\/p>\n<p><strong>Related Domains:<\/strong><\/p>\n<ul>\n<li>Domain I: Business Problem (Question) Framing<\/li>\n<li>Domain II: Analytics Problem Framing<\/li>\n<li>Domain III: Data<\/li>\n<li>Domain IV: Methodology (Approach) Framing<\/li>\n<li>Domain V: Analytics\/Model Development<\/li>\n<\/ul>\n<p><strong>Relevant to:<\/strong><\/p>\n<ul>\n<li>Essential (Early Career)<\/li>\n<li>Professional (Mid-Career)<\/li>\n<li>Executive (Senior Level)<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"nextmv\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-nextmv tb_pian162 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_207m162 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_e8f5781 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <a href=\"https:\/\/www.nextmv.io\/\">\n                   <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/nextmv-web-ready-logo-horizontal-color-250x63.png\" width=\"250\" height=\"63\" class=\"wp-post-image wp-image-11171\" title=\"nextmv-web ready-logo-horizontal-color\" alt=\"nextmv-web ready-logo-horizontal-color\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/nextmv-web-ready-logo-horizontal-color-250x63.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/nextmv-web-ready-logo-horizontal-color-300x76.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/nextmv-web-ready-logo-horizontal-color-768x195.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/nextmv-web-ready-logo-horizontal-color.png 920w\" sizes=\"auto, (max-width: 250px) 100vw, 250px\" \/>        <\/a>\n    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_9hdz162 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_5r44162   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>2-2:50pm |\u00a0Camellia 2<\/strong><\/p>\n<p><strong>Best Practices for Decision Model Management: Versioning, Rollout, CI\/CD, DecisionOps, and More<\/strong><br><strong>Presented by: Ryan O&#8217;Neil<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_y016751 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-y016751-0\" class=\"tb_title_accordion\" aria-controls=\"acc-y016751-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-y016751-0-content\" data-id=\"acc-y016751-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_6cd5769\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_yohd769 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_obe2769   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>What input was used to run the model? What model version did it run on? Where are we tracking new model development? How long will it take to reproduce the experiment setup that generated the most recent test results? Is it safe to roll out the newest model iteration? And do we have a fallback model we can leverage just in case?<\/p>\n<p>All of these questions are part of everyday decision model development. Answering them efficiently is critical to innovating within the discipline and driving sustained project success within teams and organizations. Too often teams rely on fragile or incomplete infrastructure to support these workflows, if they\u2019re lucky to have them at all. Harness the DecisionOps habits that leading teams have used to drive greater and more sustained project success.\u00a0<\/p>\n<p>Join this session to learn about best practices for managing the full decision model lifecycle: from versioning, environment setup, CI\/CD integration, model drift monitoring, rollout strategies, and more.\u00a0<\/p>\n<p><strong>Related Domains:\u00a0<\/strong><\/p>\n<ul>\n<li>Domain V &#8211; Analytics\/Model Development\u00a0<\/li>\n<li>Domain VI &#8211; Deployment<\/li>\n<li>Domain VII &#8211; Analytics Solution Lifecycle Management<\/li>\n<\/ul>\n<p><strong>Relevant to:<\/strong><\/p>\n<ul>\n<li>Essential (Early Career),<\/li>\n<li>Professional (Mid-Career), and<\/li>\n<li>Executive (Senior Level).<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"ampl\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-ampl tb_oo9m938 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_vvav938 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_87px204 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/391989549-ampl_logo_inline-250x77.png\" width=\"250\" height=\"77\" class=\"wp-post-image wp-image-12160\" title=\"391989549-ampl_logo_inline\" alt=\"391989549-ampl_logo_inline\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/391989549-ampl_logo_inline-250x77.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/391989549-ampl_logo_inline-300x93.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/391989549-ampl_logo_inline-768x238.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/391989549-ampl_logo_inline.png 863w\" sizes=\"auto, (max-width: 250px) 100vw, 250px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_v2a6938 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_yxcg938   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>4-4:50pm | Camellia 3<\/strong><\/p>\n<p><strong>Optimization in Action: Decision Systems Across Industries<\/strong><br><strong>Presented by: Christian Valente and Juan Bohorquez<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_u6cs40 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-u6cs40-0\" class=\"tb_title_accordion\" aria-controls=\"acc-u6cs40-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-u6cs40-0-content\" data-id=\"acc-u6cs40-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_bzdr57\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_2iwr57 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_8n2k57   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Optimization plays a critical role in operational decision systems across industries such as energy, transportation, logistics, retail, and finance. These systems combine predictive analytics, data pipelines, and mathematical optimization models to support decisions involving scheduling, resource allocation, routing, and planning.<\/p>\n<p>This technology showcase highlights how optimization is used in real-world applications across a range of industries. We begin by illustrating where optimization fits within modern decision architectures and the role it plays as the optimization layer connecting data, models, and operational systems.<\/p>\n<p>The session then explores several industry applications that use optimization to support complex operational decisions. Examples will demonstrate how organizations structure optimization models, integrate them into analytics environments, and deploy them within production systems.<\/p>\n<p>Throughout the presentation, we highlight features and modeling approaches that enable scalable decision applications and explain why optimization continues to be a core technology for solving large operational problems across industries.<\/p>\n<p>This talk is intended for operations researchers, data scientists, and analytics practitioners interested in how optimization is applied in modern decision systems.<\/p>\n<p><strong>Related Domains:\u00a0<\/strong><\/p>\n<ul>\n<li>Domain I: Business Problem (Question) Framing<\/li>\n<li>Domain V &#8211; Analytics\/Model Development\u00a0<\/li>\n<li>Domain VI &#8211; Deployment<\/li>\n<\/ul>\n<p><strong>Relevant to:<\/strong><\/p>\n<ul>\n<li>Essential (Early Career),<\/li>\n<li>Professional (Mid-Career), and<\/li>\n<li>Executive (Senior Level).<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"fico\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-fico tb_16gr632 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_vzbw632 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_51kc765 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1-250x88.png\" width=\"250\" height=\"88\" class=\"wp-post-image wp-image-10810\" title=\"FICO_RGB_Blue (1)\" alt=\"FICO_RGB_Blue (1)\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1-250x88.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1-300x107.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1-200x71.png 200w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1-170x60.png 170w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1.png 1001w\" sizes=\"auto, (max-width: 250px) 100vw, 250px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_9nuo632 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_2uar632   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>4-4:50pm |\u00a0Camellia 2<\/strong><\/p>\n<p><strong>Stop Optimizing Point Forecasts: Robust Decision Modeling with PyMC and FICO\u00ae Xpress<br>Presented by:\u00a0Daniel Saunders and\u00a0Jay Laramore\u00a0<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_tbzd761 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-tbzd761-0\" class=\"tb_title_accordion\" aria-controls=\"acc-tbzd761-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-tbzd761-0-content\" data-id=\"acc-tbzd761-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_rbf2779\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_a8ez779 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_2j9v779   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Optimization models often rely on point forecasts that ignore uncertainty. Yet many operational decisions must remain robust against unpredictable demand, weather, and market conditions.<\/p>\n<p>This talk presents a practical framework for building optimization models in the face of uncertainty by combining Bayesian probabilistic forecasting with mathematical optimization. Using PyMC, we generate posterior demand distributions that capture seasonality, trends, and extreme events. These posterior samples form a distribution of possible outcomes that are incorporated directly into optimization models that are solved using FICO Xpress.<\/p>\n<p>We\u2019ll demonstrate how this approach enables risk-aware decision-making using chance-constrained optimization and Conditional Value-at-Risk (CVaR). Through an electricity generation planning example, attendees will learn how probabilistic forecasts can be incorporated into FICO Xpress to support practical decision-making despite uncertainty.<\/p>\n<p><strong>Related Domains<\/strong>:<\/p>\n<ul>\n<li>Domain IV<\/li>\n<li>Domain V<\/li>\n<\/ul>\n<p><strong>Relevant to<\/strong>:<\/p>\n<ul>\n<li>Essential (Early Career)<\/li>\n<li>Professional (Mid-Career)<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_iq20412 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_9tho412 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_r0ml412   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h2>Tuesday, April 14<\/h2>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"hexaly\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-hexaly tb_9fyk983 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_23nr983 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_ea5i983 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <a href=\"https:\/\/www.hexaly.com\/\">\n                   <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/hexaly-orange.svg\" width=\"250\" height=\"81\" class=\"wp-post-image wp-image-11161\" title=\"hexaly-orange\" alt=\"hexaly-orange\">        <\/a>\n    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_orzf983 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_jgkv983   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>9:30-10:20am |\u00a0Camellia 2<\/strong><\/p>\n<p><strong>Nonlinear Programming with Hexaly<\/strong><br><strong>Presented by: Fred Gardi<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_qsxz253 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-qsxz253-0\" class=\"tb_title_accordion\" aria-controls=\"acc-qsxz253-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-qsxz253-0-content\" data-id=\"acc-qsxz253-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_uws8272\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_gsv5273 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_uzyn273   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Hexaly 14.5 is a hybrid optimization solver that combines exact and heuristic methods in a model-and-run framework. This work presents its approaches for continuous nonlinear problems min f(x) s.t. g_i(x) \u2264 0, h_j(x) = 0, bounds on x, with analytical functions modeled via an expression graph. New features in 14.5 version include: automatic structure detection and convexity classification by propagating properties through the graph to select suitable algorithms; a modular interior-point solver offering predictor-corrector, trust-region, and filter methods (chosen dynamically, with parallel multi-solve runs), falling back to derivative-free search or augmented Lagrangian when Hessians are unavailable; and an exact MINLP solver that uses model reformulation, RLT cuts, branch-and-reduce for tight bounds, and subproblem solves for primal solutions. In 60-second benchmarks, Hexaly 14.5 finds more feasible solutions and proves optimality more often than SCIP 9.2 and Ipopt 3.14 on convex QP portfolio instances, nonconvex QCQP pooling, and CUTEst nonlinear problems.<\/p>\n<p><strong>Related to:<\/strong><\/p>\n<ul>\n<li>Domain V: Analytics\/Model Development<\/li>\n<\/ul>\n<p><strong>Relevant to:<\/strong><\/p>\n<ul>\n<li>Essential (Early Career)<\/li>\n<li>Professional (Mid-Career)<\/li>\n<li>Executive (Senior Level)<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"princetonconsultants\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-princetonconsultants tb_w5xi108 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_xuqv108 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_uy6v108 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <a href=\"https:\/\/princeton.com\/\">\n                   <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-1024x314-260x79.png\" width=\"260\" height=\"79\" class=\"wp-post-image wp-image-11111\" title=\"PCI-2024.logo.NO-URL_300dpi_Transparent\" alt=\"PCI-2024.logo.NO-URL_300dpi_Transparent\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-1024x314-260x79.png 260w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-300x92.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-1024x314.png 1024w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-768x236.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-1536x472.png 1536w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-2048x629.png 2048w\" sizes=\"auto, (max-width: 260px) 100vw, 260px\" \/>        <\/a>\n    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_dmz3108 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_13yv108   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>9:30-10:20am | Camellia 3<\/strong><\/p>\n<p><strong>Optimization Solution Development and Deployment &#8211; A Framework for Success<br>Presented by: Irv Lustig<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_bivi96 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-bivi96-0\" class=\"tb_title_accordion\" aria-controls=\"acc-bivi96-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-bivi96-0-content\" data-id=\"acc-bivi96-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_6ghb112\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_wg2y112 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_4y25112   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>For over 44 years, Princeton Consultants has developed and deployed numerous applications that leverage optimization to make better decisions for our clients. We have developed a set of best practices over that time that are also reflected in the recently published INFORMS Analytics Framework (IAF), for which Irv was a key contributor.<\/p>\n<p>The IAF provides a roadmap for successful analytics projects, including optimization, and contains specific tasks related to risk management. Irv will describe both the IAF and the Princeton 20, a set of 10 environmental and 10 technical risks to be evaluated at the commencement of any project. Understanding these risks in the context of the framework leads to best practices that improve the chances of success for your next optimization project.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"mosimtec\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-mosimtec tb_wqbc319 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_642p319 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_rvoo956 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/web_ready_company_logo-mosimtec-250x66.jpg\" width=\"250\" height=\"66\" class=\"wp-post-image wp-image-12594\" title=\"web_ready_company_logo-mosimtec\" alt=\"web_ready_company_logo-mosimtec\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/web_ready_company_logo-mosimtec-250x66.jpg 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/web_ready_company_logo-mosimtec-300x80.jpg 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/web_ready_company_logo-mosimtec.jpg 600w\" sizes=\"auto, (max-width: 250px) 100vw, 250px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_ot0j319 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_uvcr319   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>11-11:50am | Camellia 3<\/strong><\/p>\n<p><strong>Selecting Appropriate Simulation User Interfaces<\/strong><br><strong>Presented by: Saurabh Parakh and Martin Franklin<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_8d0c349 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-8d0c349-0\" class=\"tb_title_accordion\" aria-controls=\"acc-8d0c349-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-8d0c349-0-content\" data-id=\"acc-8d0c349-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_7sog371\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_cl4d371 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_b26t371   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>A simulation user interface often takes a model from code to a tool from which organizations gain value.\u00a0 This presentation focuses on why companies should invest in user interfaces for simulation models, what the different types of user interfaces are, and how an appropriate user interface option can be selected.<\/p>\n<p>When selecting the appropriate user interface, several dimensions should be considered, including the number of users, user sophistication, data feeds to and from other applications, the frequency of model use, and the significance of the decision being made with the simulation model.\u00a0 This presentation will share examples of real-world projects with a variety of user interfaces to discuss why they were selected for their particular project.<\/p>\n<p>At a minimum, a simulation user interface, sometimes referred to as the Front End, provides the user with a means to change model inputs and view model outputs.\u00a0 Additional functionality may include scenario management or optimization to increase the usefulness of a simulation model.\u00a0<\/p>\n<p>While there may be several reasons to create a user interface for a model, there are 3 significant reasons justifying the time required to design a good user interface:<\/p>\n<ul>\n<li>It saves time and time is money. If users can reach the same decisions faster, it allows for a lower long-term cost of ownership for using the simulation model.<\/li>\n<li>It prevents mistakes. If users are making the wrong decisions from a simulation model, then the model has done more harm than good.<\/li>\n<li>It increases model adoption. If a model has the power to enable profit-optimal decisions, but no one wants to use it because it is too difficult, then the model is not useful to the organization.<\/li>\n<\/ul>\n<p><strong>Related Domains:<\/strong><\/p>\n<ul>\n<li>Domain I: Business Problem (Question) Framing<\/li>\n<li>Domain IV: Methodology (Approach) Framing<\/li>\n<li>Domain VI: Deployment<\/li>\n<\/ul>\n<p><strong>Relevant to:<\/strong><\/p>\n<ul>\n<li>Professional (Mid-Career)<\/li>\n<li>Executive (Senior Level)<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"sas\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-sas tb_5vpa730 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_0yn3730 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_9yn9831 image-center   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/sas-logo-blue-250x102.jpg\" width=\"250\" height=\"102\" class=\"wp-post-image wp-image-12041\" title=\"sas-logo-blue\" alt=\"sas-logo-blue\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/sas-logo-blue-250x102.jpg 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/sas-logo-blue-300x124.jpg 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/sas-logo-blue.jpg 600w\" sizes=\"auto, (max-width: 250px) 100vw, 250px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_bg7x730 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_k237730   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>11-11:50am |\u00a0Camellia 2<\/strong><\/p>\n<p><strong>Building and Solving Optimization Models with SAS<\/strong><br><strong>Presented by: Rob Pratt<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_sr71794 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-sr71794-0\" class=\"tb_title_accordion\" aria-controls=\"acc-sr71794-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Description<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-sr71794-0-content\" data-id=\"acc-sr71794-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_1mdw825\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_yk10825 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_vk0c825   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>SAS offers extensive analytic capabilities, including machine learning, deep learning, natural language processing, statistical analysis, optimization, and simulation. SAS analytic functionality is also available through the open, cloud-enabled design of SAS\u00ae Viya\u00ae. You can program in SAS or in other languages \u2013 Python, Lua, Java, and R.<\/p>\n<p>OPTMODEL from SAS provides a powerful and intuitive algebraic optimization modeling language and unified support for building and solving LP, MILP, QP, conic, NLP, constraint programming, network-oriented, and black-box models. This showcase will include an overview of the optimization capabilities and demonstrate recently added features.<\/p>\n<p><strong>Related Domain:<\/strong><\/p>\n<ul>\n<li>Domain V: Analytics\/Model Development<\/li>\n<\/ul>\n<p><strong>Relevant to:<\/strong><\/p>\n<ul>\n<li>Essential (Early Career)<\/li>\n<li>Professional (Mid-Career)<\/li>\n<li>Executive (Senior Level)<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n        <\/div>\n<!--\/themify_builder_content-->","protected":false},"excerpt":{"rendered":"<p>Technology Showcases<\/p>\n","protected":false},"author":1001137,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"class_list":["post-11089","page","type-page","status-publish","hentry","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.0 (Yoast SEO v26.0) - 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2026 INFORMS Analytics+ Conference","isPartOf":{"@id":"https:\/\/meetings.informs.org\/wordpress\/analytics\/#website"},"primaryImageOfPage":{"@id":"https:\/\/meetings.informs.org\/wordpress\/analytics\/technology-showcases\/#primaryimage"},"image":{"@id":"https:\/\/meetings.informs.org\/wordpress\/analytics\/technology-showcases\/#primaryimage"},"thumbnailUrl":"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-260x65.jpg","datePublished":"2026-02-09T14:08:02+00:00","dateModified":"2026-04-09T14:15:14+00:00","breadcrumb":{"@id":"https:\/\/meetings.informs.org\/wordpress\/analytics\/technology-showcases\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/meetings.informs.org\/wordpress\/analytics\/technology-showcases\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/meetings.informs.org\/wordpress\/analytics\/technology-showcases\/#primaryimage","url":"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue.jpg","contentUrl":"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue.jpg","width":600,"height":150},{"@type":"BreadcrumbList","@id":"https:\/\/meetings.informs.org\/wordpress\/analytics\/technology-showcases\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/meetings.informs.org\/wordpress\/analytics\/"},{"@type":"ListItem","position":2,"name":"Technology Showcases"}]},{"@type":"WebSite","@id":"https:\/\/meetings.informs.org\/wordpress\/analytics\/#website","url":"https:\/\/meetings.informs.org\/wordpress\/analytics\/","name":"2026 INFORMS Analytics+ Conference","description":"April 12 \u2013 14","publisher":{"@id":"https:\/\/meetings.informs.org\/wordpress\/analytics\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/meetings.informs.org\/wordpress\/analytics\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/meetings.informs.org\/wordpress\/analytics\/#organization","name":"INFORMS","url":"https:\/\/meetings.informs.org\/wordpress\/analytics\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/meetings.informs.org\/wordpress\/analytics\/#\/schema\/logo\/image\/","url":"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2022\/03\/INFORMS_Logo_full_color.jpg","contentUrl":"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2022\/03\/INFORMS_Logo_full_color.jpg","width":300,"height":72,"caption":"INFORMS"},"image":{"@id":"https:\/\/meetings.informs.org\/wordpress\/analytics\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/INFORMSpage\/","https:\/\/x.com\/INFORMS","http:\/\/instagram.com\/informs_orms","https:\/\/www.linkedin.com\/groups\/82644\/profile","https:\/\/www.pinterest.com\/informs\/_saved\/","https:\/\/www.youtube.com\/user\/INFORMSonline","https:\/\/en.wikipedia.org\/wiki\/Institute_for_Operations_Research_and_the_Management_Sciences"]}]}},"builder_content":"<h1>Technology Showcases<\/h1>\n<p><strong>Please note<\/strong>: Technology Showcase presentations are part of the conference Track Sessions. As such, they are educational (not commercial) and can feature <br>case studies which may include use of the exhibitor's products and services.\u00a0Technology Showcase content should also relate to the 7 Domains of the <br>INFORMS Analytics Framework.<\/p>\n<h2>Monday, April 13<\/h2>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-260x65.jpg\" width=\"260\" height=\"65\" title=\"600x150px_Gurobi_Logo_Blue\" alt=\"600x150px_Gurobi_Logo_Blue\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-260x65.jpg 260w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-300x75.jpg 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-250x62.jpg 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-275x68.jpg 275w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue.jpg 600w\" sizes=\"(max-width: 260px) 100vw, 260px\" \/>\n<p><strong>10-10:50am |\u00a0Camellia 2<\/strong><\/p> <p><strong>AI Innovations in Optimization<\/strong><br><strong>Presented by: Caroline Weinberg and Jerry Yurchisin<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>The Gurobi AI Innovation Lab is developing numerous methods to integrate AI and Optimization.\u00a0\u00a0 These include Optimization Proxies to dramatically accelerate times to find good solutions and tools to facilitate both the model creation process and the interpretation of the subsequent optimization results.\u00a0\u00a0 This presentation will describe results from methods already underway, as well as early findings from methods still in development.<\/p><\/li><\/ul>\n<p>The Gurobi AI Innovation Lab is developing numerous methods to integrate AI and Optimization.\u00a0\u00a0 These include Optimization Proxies to dramatically accelerate times to find good solutions and tools to facilitate both the model creation process and the interpretation of the subsequent optimization results.\u00a0\u00a0 This presentation will describe results from methods already underway, as well as early findings from methods still in development.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/JMP-web_ready_company_logo-250x68.png\" width=\"250\" height=\"68\" title=\"JMP web_ready_company_logo\" alt=\"JMP web_ready_company_logo\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/JMP-web_ready_company_logo-250x68.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/JMP-web_ready_company_logo-300x82.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/JMP-web_ready_company_logo.png 557w\" sizes=\"(max-width: 250px) 100vw, 250px\" \/>\n<p><strong>10-10:50am | Camellia 3<\/strong><\/p> <p><strong>Bayesian Optimization: Iterative Learning for Optimizing Complex Products and Processes<\/strong><br><strong>Presented by: Ross Metusalem<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Bayesian Optimization (BayesOpt) is an iterative learning method for efficiently optimizing black-box or expensive-to-evaluate functions, with broad applications in process and product optimization. Starting with a small set of observations, BayesOpt fits a flexible machine learning-style model to the data and uses that model\u2019s predictions and uncertainty in those predictions to determine the best next observation to collect. Iterating on this process, BayesOpt balances exploration the factor space with exploitation of potential optima to identify a globally optimal solution. This Technology Showcase will establish the foundational concepts underlying BayesOpt, discuss when to consider using it, and present a case study of the technique in action.<\/p> <p><strong>Related Domains:\u00a0<\/strong><\/p> <ul> <li>Domain III: Data<\/li> <li>Domain IV: Methodology (Approach) Framing<\/li> <li>Domain V: Analytics\/Model Development<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul><\/li><\/ul>\n<p>Bayesian Optimization (BayesOpt) is an iterative learning method for efficiently optimizing black-box or expensive-to-evaluate functions, with broad applications in process and product optimization. Starting with a small set of observations, BayesOpt fits a flexible machine learning-style model to the data and uses that model\u2019s predictions and uncertainty in those predictions to determine the best next observation to collect. Iterating on this process, BayesOpt balances exploration the factor space with exploitation of potential optima to identify a globally optimal solution. This Technology Showcase will establish the foundational concepts underlying BayesOpt, discuss when to consider using it, and present a case study of the technique in action.<\/p> <p><strong>Related Domains:\u00a0<\/strong><\/p> <ul> <li>Domain III: Data<\/li> <li>Domain IV: Methodology (Approach) Framing<\/li> <li>Domain V: Analytics\/Model Development<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul>\n<a href=\"https:\/\/decisionbrain.com\/\"> <img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color-1024x251-250x61.png\" width=\"250\" height=\"61\" title=\"DB-Logo-Color\" alt=\"DB-Logo-Color\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color-1024x251-250x61.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color-300x73.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color-1024x251.png 1024w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color-768x188.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/DB-Logo-Color.png 1500w\" sizes=\"(max-width: 250px) 100vw, 250px\" \/> <\/a>\n<p><strong>11-11:50am | Camellia 3<\/strong><\/p> <p><strong>The Last Mile of OR: Building Production-Ready Decision Tools with DB Gene Studio<br>Presented by: Justin Clark<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>You have a strong OR team. They've built sophisticated planning and scheduling models, tuned constraints, and found solutions that genuinely improve operations. But the models live in notebooks, the results get copy-pasted into spreadsheets, and the business stakeholders who need to act on the outputs can't access them without going through a developer. The math is solved. The deployment isn't.<\/p> <p>This session presents a practical framework for closing that gap. Using a workforce planning problem as the working example, you'll learn how to take a Python model all the way to a production-ready decision-support application \u2014 with interactive UIs business users can operate independently, AI-assisted constraint modeling using Claude, and seamless integration with existing data systems and solvers.<\/p> <p>The development environment used throughout is DB Gene Studio, purpose-built for OR teams who want to own the full journey from model to deployment without dedicated engineering support. DB Gene Studio is solver-agnostic and designed to complement the tools and systems organizations already use.<\/p> <p><strong>Related Domain: <\/strong><\/p> <ul> <li>Domain V: Analytics\/Model Development<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Professional (Mid-Career),<\/li> <li>Executive (Senior Level) and<\/li> <li>Associate (Early Career).<\/li> <\/ul><\/li><\/ul>\n<p>You have a strong OR team. They've built sophisticated planning and scheduling models, tuned constraints, and found solutions that genuinely improve operations. But the models live in notebooks, the results get copy-pasted into spreadsheets, and the business stakeholders who need to act on the outputs can't access them without going through a developer. The math is solved. The deployment isn't.<\/p> <p>This session presents a practical framework for closing that gap. Using a workforce planning problem as the working example, you'll learn how to take a Python model all the way to a production-ready decision-support application \u2014 with interactive UIs business users can operate independently, AI-assisted constraint modeling using Claude, and seamless integration with existing data systems and solvers.<\/p> <p>The development environment used throughout is DB Gene Studio, purpose-built for OR teams who want to own the full journey from model to deployment without dedicated engineering support. DB Gene Studio is solver-agnostic and designed to complement the tools and systems organizations already use.<\/p> <p><strong>Related Domain: <\/strong><\/p> <ul> <li>Domain V: Analytics\/Model Development<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Professional (Mid-Career),<\/li> <li>Executive (Senior Level) and<\/li> <li>Associate (Early Career).<\/li> <\/ul>\n<a href=\"https:\/\/www.gams.com\/\"> <img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo-250x100.png\" width=\"250\" height=\"100\" title=\"GAMS-Logo\" alt=\"GAMS-Logo\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo-250x99.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo-300x120.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo-275x110.png 275w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo-200x80.png 200w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo.png 500w\" sizes=\"(max-width: 250px) 100vw, 250px\" \/> <\/a>\n<p><strong>11-11:50am |\u00a0Camellia 2<\/strong><\/p> <p><strong>GAMSPy: Powering Mathematical Optimization and Machine Learning in Python<br>Presented by: Adam Christensen &amp; Steven Dirkse<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>This showcase offers a glimpse into the world of modeling with GAMSPy \u2013 a powerful mathematical optimization package that merges the high-performance GAMS execution system with the flexibility of the Python language.\u00a0 The result is a complete mathematical optimization package that can be deployed anywhere \u2013 effortlessly.\u00a0<\/p> <p>Join us to explore GAMSPy's fundamental functionalities through practical examples. We'll cover everything from defining sets, parameters, variables, and equations to solving models and retrieving results, all within a familiar Python environment. Beyond the basics, we'll also provide a glimpse into more advanced Machine Learning features, demonstrating how GAMSPy can streamline complex modeling workflows and enhance your analytical capabilities.<\/p> <p>Whether you're a seasoned GAMS user looking to integrate with Python or a Python user curious about optimization, this workshop will equip you with essential skills needed to get started and demonstrate what is possible with GAMSPy.<\/p> <p><strong>Related Domains:<\/strong><\/p> <ul> <li>Domain III: Data<\/li> <li>Domain V: Analytics\/Model Development<\/li> <li>Domain VI: Deployment<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <\/ul><\/li><\/ul>\n<p>This showcase offers a glimpse into the world of modeling with GAMSPy \u2013 a powerful mathematical optimization package that merges the high-performance GAMS execution system with the flexibility of the Python language.\u00a0 The result is a complete mathematical optimization package that can be deployed anywhere \u2013 effortlessly.\u00a0<\/p> <p>Join us to explore GAMSPy's fundamental functionalities through practical examples. We'll cover everything from defining sets, parameters, variables, and equations to solving models and retrieving results, all within a familiar Python environment. Beyond the basics, we'll also provide a glimpse into more advanced Machine Learning features, demonstrating how GAMSPy can streamline complex modeling workflows and enhance your analytical capabilities.<\/p> <p>Whether you're a seasoned GAMS user looking to integrate with Python or a Python user curious about optimization, this workshop will equip you with essential skills needed to get started and demonstrate what is possible with GAMSPy.<\/p> <p><strong>Related Domains:<\/strong><\/p> <ul> <li>Domain III: Data<\/li> <li>Domain V: Analytics\/Model Development<\/li> <li>Domain VI: Deployment<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <\/ul>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/Bayesia_Logo_Transparent_jparaa.svg\" width=\"250\" height=\"92\" title=\"Bayesia_Logo_Transparent_jparaa\" alt=\"Bayesia_Logo_Transparent_jparaa\">\n<p><strong>1-1:50pm |\u00a0Camellia 2<\/strong><\/p> <p><strong>Overcoming the Limits of LLMs: Decision-Making Under Uncertainty with Causal Models<br><\/strong><strong>Presented by: Stefan Conrady<\/strong><\/p>\n<ul><li><h4>Descripton<\/h4><p>Large language models (LLMs) have rapidly become central tools for analysis, explanation, and recommendation. Their apparent generality has led to a widespread perception that they can support virtually any analytics task. However, in real-world decision-making contexts, especially those involving uncertainty, interventions, and trade-offs, additional modeling capabilities are required.<\/p> <p>This Technology Showcase introduces a practical framework for selecting and combining modeling approaches based on three key dimensions: learning granularity, representational abstraction, and inference semantics. Using this framework, we demonstrate how different analytics technologies, including statistical models, neural networks, LLMs, and Bayesian networks, serve distinct roles in the analytics workflow rather than acting as substitutes.<\/p> <p>The session focuses on how Bayesian networks and influence diagrams enable explicit representation of uncertainty, causal reasoning, and decision optimization. Through live demonstrations and case examples, we show how these models support scenario analysis, intervention planning, and value-of-information calculations, helping organizations determine not only what is likely to happen, but what actions should be taken and what additional information is worth acquiring.<\/p> <p>We also illustrate how LLMs can be integrated into this workflow to support knowledge elicitation, model structuring, and communication, while relying on structured probabilistic models for transparent and accountable decision support.<\/p> <p>Attendees will leave with a practical understanding of how to position different modeling technologies within their analytics stack and how to build hybrid solutions that combine generative AI with causal and decision-theoretic modeling for high-stakes applications.<\/p> <p><strong>Related Domain:<\/strong><\/p> <ul> <li>Domain IV: Methodology<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Professional (Mid-Career)<\/li> <\/ul><\/li><\/ul>\n<p>Large language models (LLMs) have rapidly become central tools for analysis, explanation, and recommendation. Their apparent generality has led to a widespread perception that they can support virtually any analytics task. However, in real-world decision-making contexts, especially those involving uncertainty, interventions, and trade-offs, additional modeling capabilities are required.<\/p> <p>This Technology Showcase introduces a practical framework for selecting and combining modeling approaches based on three key dimensions: learning granularity, representational abstraction, and inference semantics. Using this framework, we demonstrate how different analytics technologies, including statistical models, neural networks, LLMs, and Bayesian networks, serve distinct roles in the analytics workflow rather than acting as substitutes.<\/p> <p>The session focuses on how Bayesian networks and influence diagrams enable explicit representation of uncertainty, causal reasoning, and decision optimization. Through live demonstrations and case examples, we show how these models support scenario analysis, intervention planning, and value-of-information calculations, helping organizations determine not only what is likely to happen, but what actions should be taken and what additional information is worth acquiring.<\/p> <p>We also illustrate how LLMs can be integrated into this workflow to support knowledge elicitation, model structuring, and communication, while relying on structured probabilistic models for transparent and accountable decision support.<\/p> <p>Attendees will leave with a practical understanding of how to position different modeling technologies within their analytics stack and how to build hybrid solutions that combine generative AI with causal and decision-theoretic modeling for high-stakes applications.<\/p> <p><strong>Related Domain:<\/strong><\/p> <ul> <li>Domain IV: Methodology<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Professional (Mid-Career)<\/li> <\/ul>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-723x1024-150x212.png\" width=\"150\" height=\"212\" title=\"ORMAE web_ready_company_logo V\" alt=\"ORMAE web_ready_company_logo V\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-723x1024-150x212.png 150w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-212x300.png 212w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-723x1024.png 723w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-768x1087.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-723x1024-100x141.png 100w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-723x1024-140x198.png 140w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V.png 1013w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/>\n<p><strong>1-1:50pm | Camellia 3<\/strong><\/p> <p><strong>The Smart Plant: <\/strong><strong>AI-Powered Real-Time Optimization for Complex Industrial Operations<br>Presented by: Amit Garg and William Lopez<\/strong><br><br><\/p>\n<ul><li><h4>Description<\/h4><p>In process-based industries, operational leadership faces a fundamental question: How can organizations continuously determine the optimal combination of raw materials to maximize product value, maintain asset health, control costs, and meet regulatory requirements\u2014while operating in real time and at enterprise scale?<\/p> <p>This presentation describes the architecture and implementation of an AI-driven Decision Intelligence platform\u00a0developed by ORMAE\u00a0that transforms this problem into a measurable source of operational advantage. The platform was initially designed to address copper concentrate blend optimization\u2014one of the most computationally intensive problems in base metal smelting. Its design principles and technology architecture are broadly applicable to industrial operations where feed composition, procurement decisions, and process stability are tightly interconnected.<\/p> <p>The system integrates combinatorial optimization, physics-based metallurgy and process simulation, AI agent orchestration, and enterprise system integration. It evaluates millions of possible input combinations within minutes and dynamically responds to changing operating conditions, supply disruptions, and commercial constraints. High-performance computing enables large-scale sourcing scenario analysis, while integration with plant control systems creates a closed-loop link between digital recommendations and physical operations.<\/p> <p>Deployment results demonstrate significant improvements across smelting operations. Decision time for concentrate blending was reduced from 72 hours to less than 6 hours, procurement efficiency improved substantially, plant stability increased, and cross-functional collaboration strengthened across procurement, operations, finance, and commercial teams.<\/p> <p>Attendees from metals, refining, chemicals, cement, fertilizers, battery materials, and advanced manufacturing will gain a replicable framework for implementing AI-driven decision intelligence in complex industrial environments.<\/p> <p><strong>Related Domains:<\/strong><\/p> <ul> <li>Domain I: Business Problem (Question) Framing<\/li> <li>Domain II: Analytics Problem Framing<\/li> <li>Domain III: Data<\/li> <li>Domain IV: Methodology (Approach) Framing<\/li> <li>Domain V: Analytics\/Model Development<\/li> <li>Domain VI: Deployment<\/li> <li>Domain VII: Analytics Solution Lifecycle Management<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul> <p>\u00a0<\/p><\/li><\/ul>\n<p>In process-based industries, operational leadership faces a fundamental question: How can organizations continuously determine the optimal combination of raw materials to maximize product value, maintain asset health, control costs, and meet regulatory requirements\u2014while operating in real time and at enterprise scale?<\/p> <p>This presentation describes the architecture and implementation of an AI-driven Decision Intelligence platform\u00a0developed by ORMAE\u00a0that transforms this problem into a measurable source of operational advantage. The platform was initially designed to address copper concentrate blend optimization\u2014one of the most computationally intensive problems in base metal smelting. Its design principles and technology architecture are broadly applicable to industrial operations where feed composition, procurement decisions, and process stability are tightly interconnected.<\/p> <p>The system integrates combinatorial optimization, physics-based metallurgy and process simulation, AI agent orchestration, and enterprise system integration. It evaluates millions of possible input combinations within minutes and dynamically responds to changing operating conditions, supply disruptions, and commercial constraints. High-performance computing enables large-scale sourcing scenario analysis, while integration with plant control systems creates a closed-loop link between digital recommendations and physical operations.<\/p> <p>Deployment results demonstrate significant improvements across smelting operations. Decision time for concentrate blending was reduced from 72 hours to less than 6 hours, procurement efficiency improved substantially, plant stability increased, and cross-functional collaboration strengthened across procurement, operations, finance, and commercial teams.<\/p> <p>Attendees from metals, refining, chemicals, cement, fertilizers, battery materials, and advanced manufacturing will gain a replicable framework for implementing AI-driven decision intelligence in complex industrial environments.<\/p> <p><strong>Related Domains:<\/strong><\/p> <ul> <li>Domain I: Business Problem (Question) Framing<\/li> <li>Domain II: Analytics Problem Framing<\/li> <li>Domain III: Data<\/li> <li>Domain IV: Methodology (Approach) Framing<\/li> <li>Domain V: Analytics\/Model Development<\/li> <li>Domain VI: Deployment<\/li> <li>Domain VII: Analytics Solution Lifecycle Management<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul> <p>\u00a0<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/Arteyls2-250x90.png\" width=\"250\" height=\"90\" title=\"Arteyls2\" alt=\"Arteyls2\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/Arteyls2-250x90.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/Arteyls2-300x109.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/Arteyls2-768x279.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/Arteyls2.png 787w\" sizes=\"(max-width: 250px) 100vw, 250px\" \/>\n<p><strong>2-2:50pm | Camellia 3<\/strong><\/p> <p><strong>An AI You Can Trust: Driving Analytics Adoption in Complex Manufacturing through Acceptability<br><\/strong><strong>Presented by: Louis-Pierre Campeau<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>In the production of highly customized goods with extensive production chains, traditional predictive models face the \"black box\" barrier. While technical accuracy is often the focus of analytics developments, the ultimate success of an initiative strongly depends on user acceptability and integration into daily decision-making.<\/p> <p>This presentation details the development and implementation of a predictive model designed for a complex manufacturing environment. We shift the focus from pure algorithmic performance to a methodology rooted in collaborative design and transparency. By involving end users throughout the development lifecycle, we ensured the tool was not only technically robust but also intuitive and trustworthy for those managing the actual work.<\/p> <p>We will explore the specific methodological framework used to bridge the gap between data science and operational reality. Along the way, we will investigate the concept of acceptability, its implication for the success of a project and common biases. This case study demonstrates how non-technical aspects of a technical project can truly be the driving force of adoption.<\/p> <p><strong>Related Domains:<\/strong><\/p> <ul> <li>Domain I: Business Problem (Question) Framing<\/li> <li>Domain II: Analytics Problem Framing<\/li> <li>Domain III: Data<\/li> <li>Domain IV: Methodology (Approach) Framing<\/li> <li>Domain V: Analytics\/Model Development<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul><\/li><\/ul>\n<p>In the production of highly customized goods with extensive production chains, traditional predictive models face the \"black box\" barrier. While technical accuracy is often the focus of analytics developments, the ultimate success of an initiative strongly depends on user acceptability and integration into daily decision-making.<\/p> <p>This presentation details the development and implementation of a predictive model designed for a complex manufacturing environment. We shift the focus from pure algorithmic performance to a methodology rooted in collaborative design and transparency. By involving end users throughout the development lifecycle, we ensured the tool was not only technically robust but also intuitive and trustworthy for those managing the actual work.<\/p> <p>We will explore the specific methodological framework used to bridge the gap between data science and operational reality. Along the way, we will investigate the concept of acceptability, its implication for the success of a project and common biases. This case study demonstrates how non-technical aspects of a technical project can truly be the driving force of adoption.<\/p> <p><strong>Related Domains:<\/strong><\/p> <ul> <li>Domain I: Business Problem (Question) Framing<\/li> <li>Domain II: Analytics Problem Framing<\/li> <li>Domain III: Data<\/li> <li>Domain IV: Methodology (Approach) Framing<\/li> <li>Domain V: Analytics\/Model Development<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul>\n<a href=\"https:\/\/www.nextmv.io\/\"> <img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/nextmv-web-ready-logo-horizontal-color-250x63.png\" width=\"250\" height=\"63\" title=\"nextmv-web ready-logo-horizontal-color\" alt=\"nextmv-web ready-logo-horizontal-color\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/nextmv-web-ready-logo-horizontal-color-250x63.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/nextmv-web-ready-logo-horizontal-color-300x76.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/nextmv-web-ready-logo-horizontal-color-768x195.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/nextmv-web-ready-logo-horizontal-color.png 920w\" sizes=\"(max-width: 250px) 100vw, 250px\" \/> <\/a>\n<p><strong>2-2:50pm |\u00a0Camellia 2<\/strong><\/p> <p><strong>Best Practices for Decision Model Management: Versioning, Rollout, CI\/CD, DecisionOps, and More<\/strong><br><strong>Presented by: Ryan O'Neil<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>What input was used to run the model? What model version did it run on? Where are we tracking new model development? How long will it take to reproduce the experiment setup that generated the most recent test results? Is it safe to roll out the newest model iteration? And do we have a fallback model we can leverage just in case?<\/p> <p>All of these questions are part of everyday decision model development. Answering them efficiently is critical to innovating within the discipline and driving sustained project success within teams and organizations. Too often teams rely on fragile or incomplete infrastructure to support these workflows, if they\u2019re lucky to have them at all. Harness the DecisionOps habits that leading teams have used to drive greater and more sustained project success.\u00a0<\/p> <p>Join this session to learn about best practices for managing the full decision model lifecycle: from versioning, environment setup, CI\/CD integration, model drift monitoring, rollout strategies, and more.\u00a0<\/p> <p><strong>Related Domains:\u00a0<\/strong><\/p> <ul> <li>Domain V - Analytics\/Model Development\u00a0<\/li> <li>Domain VI - Deployment<\/li> <li>Domain VII - Analytics Solution Lifecycle Management<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career),<\/li> <li>Professional (Mid-Career), and<\/li> <li>Executive (Senior Level).<\/li> <\/ul><\/li><\/ul>\n<p>What input was used to run the model? What model version did it run on? Where are we tracking new model development? How long will it take to reproduce the experiment setup that generated the most recent test results? Is it safe to roll out the newest model iteration? And do we have a fallback model we can leverage just in case?<\/p> <p>All of these questions are part of everyday decision model development. Answering them efficiently is critical to innovating within the discipline and driving sustained project success within teams and organizations. Too often teams rely on fragile or incomplete infrastructure to support these workflows, if they\u2019re lucky to have them at all. Harness the DecisionOps habits that leading teams have used to drive greater and more sustained project success.\u00a0<\/p> <p>Join this session to learn about best practices for managing the full decision model lifecycle: from versioning, environment setup, CI\/CD integration, model drift monitoring, rollout strategies, and more.\u00a0<\/p> <p><strong>Related Domains:\u00a0<\/strong><\/p> <ul> <li>Domain V - Analytics\/Model Development\u00a0<\/li> <li>Domain VI - Deployment<\/li> <li>Domain VII - Analytics Solution Lifecycle Management<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career),<\/li> <li>Professional (Mid-Career), and<\/li> <li>Executive (Senior Level).<\/li> <\/ul>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/391989549-ampl_logo_inline-250x77.png\" width=\"250\" height=\"77\" title=\"391989549-ampl_logo_inline\" alt=\"391989549-ampl_logo_inline\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/391989549-ampl_logo_inline-250x77.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/391989549-ampl_logo_inline-300x93.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/391989549-ampl_logo_inline-768x238.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/391989549-ampl_logo_inline.png 863w\" sizes=\"(max-width: 250px) 100vw, 250px\" \/>\n<p><strong>4-4:50pm | Camellia 3<\/strong><\/p> <p><strong>Optimization in Action: Decision Systems Across Industries<\/strong><br><strong>Presented by: Christian Valente and Juan Bohorquez<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Optimization plays a critical role in operational decision systems across industries such as energy, transportation, logistics, retail, and finance. These systems combine predictive analytics, data pipelines, and mathematical optimization models to support decisions involving scheduling, resource allocation, routing, and planning.<\/p> <p>This technology showcase highlights how optimization is used in real-world applications across a range of industries. We begin by illustrating where optimization fits within modern decision architectures and the role it plays as the optimization layer connecting data, models, and operational systems.<\/p> <p>The session then explores several industry applications that use optimization to support complex operational decisions. Examples will demonstrate how organizations structure optimization models, integrate them into analytics environments, and deploy them within production systems.<\/p> <p>Throughout the presentation, we highlight features and modeling approaches that enable scalable decision applications and explain why optimization continues to be a core technology for solving large operational problems across industries.<\/p> <p>This talk is intended for operations researchers, data scientists, and analytics practitioners interested in how optimization is applied in modern decision systems.<\/p> <p><strong>Related Domains:\u00a0<\/strong><\/p> <ul> <li>Domain I: Business Problem (Question) Framing<\/li> <li>Domain V - Analytics\/Model Development\u00a0<\/li> <li>Domain VI - Deployment<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career),<\/li> <li>Professional (Mid-Career), and<\/li> <li>Executive (Senior Level).<\/li> <\/ul><\/li><\/ul>\n<p>Optimization plays a critical role in operational decision systems across industries such as energy, transportation, logistics, retail, and finance. These systems combine predictive analytics, data pipelines, and mathematical optimization models to support decisions involving scheduling, resource allocation, routing, and planning.<\/p> <p>This technology showcase highlights how optimization is used in real-world applications across a range of industries. We begin by illustrating where optimization fits within modern decision architectures and the role it plays as the optimization layer connecting data, models, and operational systems.<\/p> <p>The session then explores several industry applications that use optimization to support complex operational decisions. Examples will demonstrate how organizations structure optimization models, integrate them into analytics environments, and deploy them within production systems.<\/p> <p>Throughout the presentation, we highlight features and modeling approaches that enable scalable decision applications and explain why optimization continues to be a core technology for solving large operational problems across industries.<\/p> <p>This talk is intended for operations researchers, data scientists, and analytics practitioners interested in how optimization is applied in modern decision systems.<\/p> <p><strong>Related Domains:\u00a0<\/strong><\/p> <ul> <li>Domain I: Business Problem (Question) Framing<\/li> <li>Domain V - Analytics\/Model Development\u00a0<\/li> <li>Domain VI - Deployment<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career),<\/li> <li>Professional (Mid-Career), and<\/li> <li>Executive (Senior Level).<\/li> <\/ul>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1-250x88.png\" width=\"250\" height=\"88\" title=\"FICO_RGB_Blue (1)\" alt=\"FICO_RGB_Blue (1)\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1-250x88.png 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1-300x107.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1-200x71.png 200w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1-170x60.png 170w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1.png 1001w\" sizes=\"(max-width: 250px) 100vw, 250px\" \/>\n<p><strong>4-4:50pm |\u00a0Camellia 2<\/strong><\/p> <p><strong>Stop Optimizing Point Forecasts: Robust Decision Modeling with PyMC and FICO\u00ae Xpress<br>Presented by:\u00a0Daniel Saunders and\u00a0Jay Laramore\u00a0<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Optimization models often rely on point forecasts that ignore uncertainty. Yet many operational decisions must remain robust against unpredictable demand, weather, and market conditions.<\/p> <p>This talk presents a practical framework for building optimization models in the face of uncertainty by combining Bayesian probabilistic forecasting with mathematical optimization. Using PyMC, we generate posterior demand distributions that capture seasonality, trends, and extreme events. These posterior samples form a distribution of possible outcomes that are incorporated directly into optimization models that are solved using FICO Xpress.<\/p> <p>We\u2019ll demonstrate how this approach enables risk-aware decision-making using chance-constrained optimization and Conditional Value-at-Risk (CVaR). Through an electricity generation planning example, attendees will learn how probabilistic forecasts can be incorporated into FICO Xpress to support practical decision-making despite uncertainty.<\/p> <p><strong>Related Domains<\/strong>:<\/p> <ul> <li>Domain IV<\/li> <li>Domain V<\/li> <\/ul> <p><strong>Relevant to<\/strong>:<\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <\/ul><\/li><\/ul>\n<p>Optimization models often rely on point forecasts that ignore uncertainty. Yet many operational decisions must remain robust against unpredictable demand, weather, and market conditions.<\/p> <p>This talk presents a practical framework for building optimization models in the face of uncertainty by combining Bayesian probabilistic forecasting with mathematical optimization. Using PyMC, we generate posterior demand distributions that capture seasonality, trends, and extreme events. These posterior samples form a distribution of possible outcomes that are incorporated directly into optimization models that are solved using FICO Xpress.<\/p> <p>We\u2019ll demonstrate how this approach enables risk-aware decision-making using chance-constrained optimization and Conditional Value-at-Risk (CVaR). Through an electricity generation planning example, attendees will learn how probabilistic forecasts can be incorporated into FICO Xpress to support practical decision-making despite uncertainty.<\/p> <p><strong>Related Domains<\/strong>:<\/p> <ul> <li>Domain IV<\/li> <li>Domain V<\/li> <\/ul> <p><strong>Relevant to<\/strong>:<\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <\/ul>\n<h2>Tuesday, April 14<\/h2>\n<a href=\"https:\/\/www.hexaly.com\/\"> <img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/hexaly-orange.svg\" width=\"250\" height=\"81\" title=\"hexaly-orange\" alt=\"hexaly-orange\"> <\/a>\n<p><strong>9:30-10:20am |\u00a0Camellia 2<\/strong><\/p> <p><strong>Nonlinear Programming with Hexaly<\/strong><br><strong>Presented by: Fred Gardi<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Hexaly 14.5 is a hybrid optimization solver that combines exact and heuristic methods in a model-and-run framework. This work presents its approaches for continuous nonlinear problems min f(x) s.t. g_i(x) \u2264 0, h_j(x) = 0, bounds on x, with analytical functions modeled via an expression graph. New features in 14.5 version include: automatic structure detection and convexity classification by propagating properties through the graph to select suitable algorithms; a modular interior-point solver offering predictor-corrector, trust-region, and filter methods (chosen dynamically, with parallel multi-solve runs), falling back to derivative-free search or augmented Lagrangian when Hessians are unavailable; and an exact MINLP solver that uses model reformulation, RLT cuts, branch-and-reduce for tight bounds, and subproblem solves for primal solutions. In 60-second benchmarks, Hexaly 14.5 finds more feasible solutions and proves optimality more often than SCIP 9.2 and Ipopt 3.14 on convex QP portfolio instances, nonconvex QCQP pooling, and CUTEst nonlinear problems.<\/p> <p><strong>Related to:<\/strong><\/p> <ul> <li>Domain V: Analytics\/Model Development<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul><\/li><\/ul>\n<p>Hexaly 14.5 is a hybrid optimization solver that combines exact and heuristic methods in a model-and-run framework. This work presents its approaches for continuous nonlinear problems min f(x) s.t. g_i(x) \u2264 0, h_j(x) = 0, bounds on x, with analytical functions modeled via an expression graph. New features in 14.5 version include: automatic structure detection and convexity classification by propagating properties through the graph to select suitable algorithms; a modular interior-point solver offering predictor-corrector, trust-region, and filter methods (chosen dynamically, with parallel multi-solve runs), falling back to derivative-free search or augmented Lagrangian when Hessians are unavailable; and an exact MINLP solver that uses model reformulation, RLT cuts, branch-and-reduce for tight bounds, and subproblem solves for primal solutions. In 60-second benchmarks, Hexaly 14.5 finds more feasible solutions and proves optimality more often than SCIP 9.2 and Ipopt 3.14 on convex QP portfolio instances, nonconvex QCQP pooling, and CUTEst nonlinear problems.<\/p> <p><strong>Related to:<\/strong><\/p> <ul> <li>Domain V: Analytics\/Model Development<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul>\n<a href=\"https:\/\/princeton.com\/\"> <img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-1024x314-260x79.png\" width=\"260\" height=\"79\" title=\"PCI-2024.logo.NO-URL_300dpi_Transparent\" alt=\"PCI-2024.logo.NO-URL_300dpi_Transparent\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-1024x314-260x79.png 260w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-300x92.png 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-1024x314.png 1024w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-768x236.png 768w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-1536x472.png 1536w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/02\/PCI-2024.logo_.NO-URL_300dpi_Transparent-2048x629.png 2048w\" sizes=\"(max-width: 260px) 100vw, 260px\" \/> <\/a>\n<p><strong>9:30-10:20am | Camellia 3<\/strong><\/p> <p><strong>Optimization Solution Development and Deployment - A Framework for Success<br>Presented by: Irv Lustig<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>For over 44 years, Princeton Consultants has developed and deployed numerous applications that leverage optimization to make better decisions for our clients. We have developed a set of best practices over that time that are also reflected in the recently published INFORMS Analytics Framework (IAF), for which Irv was a key contributor.<\/p> <p>The IAF provides a roadmap for successful analytics projects, including optimization, and contains specific tasks related to risk management. Irv will describe both the IAF and the Princeton 20, a set of 10 environmental and 10 technical risks to be evaluated at the commencement of any project. Understanding these risks in the context of the framework leads to best practices that improve the chances of success for your next optimization project.<\/p><\/li><\/ul>\n<p>For over 44 years, Princeton Consultants has developed and deployed numerous applications that leverage optimization to make better decisions for our clients. We have developed a set of best practices over that time that are also reflected in the recently published INFORMS Analytics Framework (IAF), for which Irv was a key contributor.<\/p> <p>The IAF provides a roadmap for successful analytics projects, including optimization, and contains specific tasks related to risk management. Irv will describe both the IAF and the Princeton 20, a set of 10 environmental and 10 technical risks to be evaluated at the commencement of any project. Understanding these risks in the context of the framework leads to best practices that improve the chances of success for your next optimization project.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/web_ready_company_logo-mosimtec-250x66.jpg\" width=\"250\" height=\"66\" title=\"web_ready_company_logo-mosimtec\" alt=\"web_ready_company_logo-mosimtec\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/web_ready_company_logo-mosimtec-250x66.jpg 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/web_ready_company_logo-mosimtec-300x80.jpg 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/web_ready_company_logo-mosimtec.jpg 600w\" sizes=\"(max-width: 250px) 100vw, 250px\" \/>\n<p><strong>11-11:50am | Camellia 3<\/strong><\/p> <p><strong>Selecting Appropriate Simulation User Interfaces<\/strong><br><strong>Presented by: Saurabh Parakh and Martin Franklin<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>A simulation user interface often takes a model from code to a tool from which organizations gain value.\u00a0 This presentation focuses on why companies should invest in user interfaces for simulation models, what the different types of user interfaces are, and how an appropriate user interface option can be selected.<\/p> <p>When selecting the appropriate user interface, several dimensions should be considered, including the number of users, user sophistication, data feeds to and from other applications, the frequency of model use, and the significance of the decision being made with the simulation model.\u00a0 This presentation will share examples of real-world projects with a variety of user interfaces to discuss why they were selected for their particular project.<\/p> <p>At a minimum, a simulation user interface, sometimes referred to as the Front End, provides the user with a means to change model inputs and view model outputs.\u00a0 Additional functionality may include scenario management or optimization to increase the usefulness of a simulation model.\u00a0<\/p> <p>While there may be several reasons to create a user interface for a model, there are 3 significant reasons justifying the time required to design a good user interface:<\/p> <ul> <li>It saves time and time is money. If users can reach the same decisions faster, it allows for a lower long-term cost of ownership for using the simulation model.<\/li> <li>It prevents mistakes. If users are making the wrong decisions from a simulation model, then the model has done more harm than good.<\/li> <li>It increases model adoption. If a model has the power to enable profit-optimal decisions, but no one wants to use it because it is too difficult, then the model is not useful to the organization.<\/li> <\/ul> <p><strong>Related Domains:<\/strong><\/p> <ul> <li>Domain I: Business Problem (Question) Framing<\/li> <li>Domain IV: Methodology (Approach) Framing<\/li> <li>Domain VI: Deployment<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul><\/li><\/ul>\n<p>A simulation user interface often takes a model from code to a tool from which organizations gain value.\u00a0 This presentation focuses on why companies should invest in user interfaces for simulation models, what the different types of user interfaces are, and how an appropriate user interface option can be selected.<\/p> <p>When selecting the appropriate user interface, several dimensions should be considered, including the number of users, user sophistication, data feeds to and from other applications, the frequency of model use, and the significance of the decision being made with the simulation model.\u00a0 This presentation will share examples of real-world projects with a variety of user interfaces to discuss why they were selected for their particular project.<\/p> <p>At a minimum, a simulation user interface, sometimes referred to as the Front End, provides the user with a means to change model inputs and view model outputs.\u00a0 Additional functionality may include scenario management or optimization to increase the usefulness of a simulation model.\u00a0<\/p> <p>While there may be several reasons to create a user interface for a model, there are 3 significant reasons justifying the time required to design a good user interface:<\/p> <ul> <li>It saves time and time is money. If users can reach the same decisions faster, it allows for a lower long-term cost of ownership for using the simulation model.<\/li> <li>It prevents mistakes. If users are making the wrong decisions from a simulation model, then the model has done more harm than good.<\/li> <li>It increases model adoption. If a model has the power to enable profit-optimal decisions, but no one wants to use it because it is too difficult, then the model is not useful to the organization.<\/li> <\/ul> <p><strong>Related Domains:<\/strong><\/p> <ul> <li>Domain I: Business Problem (Question) Framing<\/li> <li>Domain IV: Methodology (Approach) Framing<\/li> <li>Domain VI: Deployment<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/sas-logo-blue-250x102.jpg\" width=\"250\" height=\"102\" title=\"sas-logo-blue\" alt=\"sas-logo-blue\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/sas-logo-blue-250x102.jpg 250w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/sas-logo-blue-300x124.jpg 300w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/sas-logo-blue.jpg 600w\" sizes=\"(max-width: 250px) 100vw, 250px\" \/>\n<p><strong>11-11:50am |\u00a0Camellia 2<\/strong><\/p> <p><strong>Building and Solving Optimization Models with SAS<\/strong><br><strong>Presented by: Rob Pratt<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>SAS offers extensive analytic capabilities, including machine learning, deep learning, natural language processing, statistical analysis, optimization, and simulation. SAS analytic functionality is also available through the open, cloud-enabled design of SAS\u00ae Viya\u00ae. You can program in SAS or in other languages \u2013 Python, Lua, Java, and R.<\/p> <p>OPTMODEL from SAS provides a powerful and intuitive algebraic optimization modeling language and unified support for building and solving LP, MILP, QP, conic, NLP, constraint programming, network-oriented, and black-box models. This showcase will include an overview of the optimization capabilities and demonstrate recently added features.<\/p> <p><strong>Related Domain:<\/strong><\/p> <ul> <li>Domain V: Analytics\/Model Development<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul><\/li><\/ul>\n<p>SAS offers extensive analytic capabilities, including machine learning, deep learning, natural language processing, statistical analysis, optimization, and simulation. SAS analytic functionality is also available through the open, cloud-enabled design of SAS\u00ae Viya\u00ae. You can program in SAS or in other languages \u2013 Python, Lua, Java, and R.<\/p> <p>OPTMODEL from SAS provides a powerful and intuitive algebraic optimization modeling language and unified support for building and solving LP, MILP, QP, conic, NLP, constraint programming, network-oriented, and black-box models. This showcase will include an overview of the optimization capabilities and demonstrate recently added features.<\/p> <p><strong>Related Domain:<\/strong><\/p> <ul> <li>Domain V: Analytics\/Model Development<\/li> <\/ul> <p><strong>Relevant to:<\/strong><\/p> <ul> <li>Essential (Early Career)<\/li> <li>Professional (Mid-Career)<\/li> <li>Executive (Senior Level)<\/li> <\/ul>","_links":{"self":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/pages\/11089","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/users\/1001137"}],"replies":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/comments?post=11089"}],"version-history":[{"count":219,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/pages\/11089\/revisions"}],"predecessor-version":[{"id":12875,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/pages\/11089\/revisions\/12875"}],"wp:attachment":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/media?parent=11089"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}