{"id":10539,"date":"2026-02-09T14:18:21","date_gmt":"2026-02-09T14:18:21","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics\/?page_id=10539"},"modified":"2026-04-09T14:14:25","modified_gmt":"2026-04-09T14:14:25","slug":"exhibitor-workshops","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics\/exhibitor-workshops\/","title":{"rendered":"Exhibitor Workshops"},"content":{"rendered":"<!--themify_builder_content-->\n<div id=\"themify_builder_content-10539\" data-postid=\"10539\" class=\"themify_builder_content themify_builder_content-10539 themify_builder tf_clear\">\n                    <div  data-css_id=\"31se912\" id=\"homepage-intro\" data-lazy=\"1\" class=\"module_row themify_builder_row fullwidth_row_container tb_31se912 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_tou8912 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_vun2912\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col-full tb_g9yg912 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_jtd9912   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h1>Exhibitor Workshops<\/h1>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module text -->\n<div  class=\"module module-text tb_c8im590   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Please note<\/strong>: Exhibitor Workshops are part of the preconference programming. As such, they may include product demonstrations and be commercial.<\/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_6jd1707 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_k3j1707 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_joy8342\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col-full tb_z1kn342 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_nszz537   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h2>Sunday, April 12<\/h2>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                <\/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_uykh331 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_30yn331 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_s5y7983 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_oxdu637 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_iy7a266   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>From Complexity to Clarity: Driving Better Decisions with AnyLogic Simulations<\/strong><br><strong>Presented by: Saurabh Parakh and Martin Franklin<\/strong><\/p>\n<p><strong>9-10:45am | Magnolia 3<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_gqro894 \" 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-gqro894-0\" class=\"tb_title_accordion\" aria-controls=\"acc-gqro894-0-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-gqro894-0-content\" data-id=\"acc-gqro894-0\" 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_k2jd914\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_gcpf914 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_2c74495   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>MOSIMTEC will demonstrate how AnyLogic Simulation Software enables organizations to make faster, smarter, and more confident decisions in increasingly complex environments. As businesses face growing uncertainty across supply chains, manufacturing, logistics, and service operations, traditional analysis methods often fall short. This session will showcase how multimethod simulation: combining discrete event (DES), agent-based (ABS), and system dynamics (SD) modeling, provides a powerful, flexible approach to understanding real-world systems.<\/p>\n<p>Through practical examples and live demonstrations, attendees will see how AnyLogic models capture variability, interdependencies, and human behavior to reveal insights that static tools cannot. We will highlight how companies are using simulation to reduce risk, optimize performance, and evaluate strategic scenarios before making costly investments.<\/p>\n<p>Participants will leave with a clear understanding of why AnyLogic stands out as a leading simulation platform; scalable, visually intuitive, and capable of integrating with real data and enterprise systems. Whether you are new to simulation or looking to enhance your current analytics toolkit, this session will provide actionable insights into leveraging simulation as a competitive advantage.<\/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=\"ampl\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-ampl tb_ovzc464 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_ylx4464 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_e13s464 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_6wvi464 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_kaqs464   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Getting Optimization into Production: From Prototype Models to Operational Decision Systems<\/strong><br><strong>Presented by: Christian Valente and Juan Bohorquez<\/strong><\/p>\n<p><strong>11am-12:45pm | Camellia 3<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_iuyi464 \" 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-iuyi464-0\" class=\"tb_title_accordion\" aria-controls=\"acc-iuyi464-0-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-iuyi464-0-content\" data-id=\"acc-iuyi464-0\" 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_ac3y464\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_rttl464 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_bcko464   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Optimization models often begin as small analytical prototypes but become much harder to deploy as they grow into operational decision systems. As models scale, teams encounter new challenges related to model structure, system integration, solver performance, and maintaining reliable workflows in production environments.<\/p>\n<p>This exhibitor workshop demonstrates how optimization applications evolve from simple models to scalable decision systems. Using a power generation portfolio optimization example, we begin with a basic linear programming model and progressively expand it toward a more realistic industry application.<\/p>\n<p>As the model grows in complexity, we highlight common barriers that arise when moving optimization into production environments, including model maintainability, integration with analytics systems, solver performance and feasibility management, and stakeholder trust in decision models.<\/p>\n<p>Participants will see how these challenges can be addressed through practical modeling patterns, solver strategies, and system integration approaches. The session will also illustrate accessible on-ramps for getting started with optimization and how those approaches can scale into robust production deployments.<\/p>\n<p>By the end of the workshop, attendees will have seen how a simple optimization model can evolve into a more realistic decision application while overcoming several common barriers to operational deployment.<\/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_mf8b397 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_ufbu397 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_3pnj397\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_474y397 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_t3ve133 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 sub_column col4-2 tb_ejm3397 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_45ta397   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Model and Solve Nonlinear Optimization Problems with Artelys Knitro<br><\/strong><strong>Presented by: Louis-Pierre Campeau<\/strong><\/p>\n<p><strong>11am-12:45pm | Camellia 1<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_knwn298 \" 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-knwn298-0\" class=\"tb_title_accordion\" aria-controls=\"acc-knwn298-0-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-knwn298-0-content\" data-id=\"acc-knwn298-0\" 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_6xdf320\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_97u1320 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_sqef320   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Artelys Knitro is the leading solver focused on large-scale, nonlinear (potentially non-convex), optimization problems. Knitro offers both interior-point and active-set algorithms for continuous models, as well as tools for handling problems with integer variables and other discrete structures. This tutorial will introduce the key features of Knitro and demonstrate how to use Knitro to model and solve optimization problems in various environments. The talk will showcase several instances of nonlinear problems, typically addressed through linear relaxation, yet warranting a direct approach. It will delve into the methodologies and tools employed to tackle these challenges. This tutorial aims at an audience familiar with the basics of mathematical optimization and Python programming but will mostly be focusing on practical examples.<\/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>\n        <\/div>\n                        <div  data-anchor=\"ormae\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-ormae tb_tcj0632 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_wngp632 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_mz6a632\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_wq60632 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_zmwr220 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-723x1024-150x213.png\" width=\"150\" height=\"213\" 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 sub_column col4-2 tb_tc6j632 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_wjzg632   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Accelerating Time-to-Value with Explainable AI and Agentic Decision Workflows<br>Presented by: Amit Garg and William Lopez<\/strong><\/p>\n<p><strong>11am-12:45pm | <\/strong><strong>Camellia 2<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_0pnk748 \" 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-0pnk748-0\" class=\"tb_title_accordion\" aria-controls=\"acc-0pnk748-0-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-0pnk748-0-content\" data-id=\"acc-0pnk748-0\" 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_89d2772\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_ywim772 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_l1fr772   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Across industries, organizations are increasingly adopting advanced optimization to enhance planning, scheduling, and operational performance. As these models become more powerful, the next frontier is enabling decision-makers across the enterprise to interact with optimization systems seamlessly and translate insights into everyday operational decisions.<\/p>\n<p>This workshop introduces the architecture and implementation of an AI-powered Decision Intelligence platform\u00a0developed by ORMAE that combines mathematical optimization with Explainable AI and agentic decision workflows. The platform integrates optimization engines with conversational interfaces, explainability layers, and event-driven agents that respond dynamically to changing operational conditions.<\/p>\n<p>Participants will explore how planners and business users can interact with optimization systems using natural language, rapidly generate scenarios, and understand the reasoning behind recommended decisions. The session will include a live demonstration and hands-on walkthrough of an AI-driven optimization application, illustrating how optimization models can be operationalized as interactive decision tools within enterprise workflows.<\/p>\n<p>Attendees will leave with a practical framework for deploying explainable, adaptive optimization systems, accelerating time-to-value and expanding the role of decision intelligence across supply chains, industrial operations, and enterprise planning environments.<\/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>\n        <\/div>\n                        <div  data-anchor=\"sas\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-sas tb_hww2292 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_7lup292 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_2j1y292\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_psgd292 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_5ggz992 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 sub_column col4-2 tb_booo292 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_n85k292   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>SAS Analytics and Quantum Computing<\/strong><br><strong>Presented by: Rob Pratt and Yan Xu<\/strong><\/p>\n<p><strong>11am-12:45pm | Magnolia 3<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_jvjq207 \" 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-jvjq207-0\" class=\"tb_title_accordion\" aria-controls=\"acc-jvjq207-0-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-jvjq207-0-content\" data-id=\"acc-jvjq207-0\" 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_du43223\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_k4xr223 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_mwpg223   \" 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>This workshop will review the SAS analytics portfolio, highlight recently added optimization features, and explore several optimization examples, including an application where quantum computing yields performance improvements.<\/p>\n<p>Academic faculty, staff, and students can access the latest SAS software through our free academic software platforms SAS Viya for Learners and SAS Viya Workbench for Learners. Access instructions and learning resources will be provided.<\/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>\n        <\/div>\n                        <div  data-anchor=\"bayesia\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-bayesia tb_b9e9125 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_5zfw125 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_c8ir125\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_d37e125 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_2wnx677 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 sub_column col4-2 tb_jgd1125 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_gibq125   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Beyond Narratives: Making LLM Knowledge Computable with Bayesian Networks<br><\/strong><strong>Presented by: Stefan Conrady<\/strong><\/p>\n<p><strong>1-2:45pm | <\/strong><strong>Camellia 3<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_nuwm383 \" 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-nuwm383-0\" class=\"tb_title_accordion\" aria-controls=\"acc-nuwm383-0-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-nuwm383-0-content\" data-id=\"acc-nuwm383-0\" 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_sr4b402\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_vmnt403 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_xlbv403   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Recent advances in large language models (LLMs) have made vast amounts of domain knowledge accessible in natural language form. However, this knowledge remains implicit, unstructured, and difficult to operationalize for rigorous analysis or decision support. This workshop introduces a novel methodology for transforming language-based knowledge into explicit, simulation-ready causal models.<\/p>\n<p>Using BayesiaLab and its subject-matter assistant, Hellixia, participants will see how knowledge derived from LLMs can be combined with empirical data and human expertise to construct Bayesian networks. The approach translates high-level, language-based abstractions into formal variables, causal relationships, and probabilistic dependencies, yielding transparent models that support causal inference, scenario analysis, and decision optimization.<\/p>\n<p>Following this introduction, the workshop presents a supply chain case study that demonstrates the end-to-end workflow. Starting from a business question, participants will observe how LLM-informed knowledge is structured, refined, and integrated with data to produce a fully operational model.<\/p>\n<p>By making implicit knowledge explicit and computable, this methodology enables a new class of analytics workflows that bridge generative AI and causal modeling. The resulting models are interpretable, auditable, and directly applicable to complex decision-making problems across domains.<\/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>\n        <\/div>\n                        <div  data-anchor=\"decisionbrain\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-decisionbrain tb_qoij849 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_hipi849 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_07om849\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_3kyr849 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_u29y395 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 sub_column col4-2 tb_akpi849 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_nhvm849   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Scaling Adaptive Workforce Optimization: How ATALIAN and DecisionBrain Replaced Manual Scheduling with Real-Time Intelligence<br>Presented by: Tom Heyward<\/strong><\/p>\n<p><strong>1-2:45pm | Magnolia 3<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_jdcp387 \" 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-jdcp387-0\" class=\"tb_title_accordion\" aria-controls=\"acc-jdcp387-0-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-jdcp387-0-content\" data-id=\"acc-jdcp387-0\" 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_4h84414\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_jju4414 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_40as414   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Airport cleaning operations run against one of the most demanding scheduling environments in facility management \u2014 shifting flight schedules, unpredictable passenger flow, and cascading task dependencies that render static daily plans obsolete within hours. This workshop presents how ATALIAN, a global leader in facility management, partnered with DecisionBrain to replace manual coordination with a dynamic, closed-loop scheduling system powered by the DB Gene platform.<\/p>\n<p>Attendees will see how real-time external data \u2014 including live flight information and zone-level crowd distribution \u2014 feeds directly into optimization logic to continuously revise workforce schedules as conditions evolve. We walk through a three-tier decision support model serving managers, supervisors, and frontline agents, and examine how role-specific workflows drove adoption across the organization.<\/p>\n<p>Beyond the airport context, the session draws out the transferable principles: how situation-aware constraints, automated re-scheduling, and real-time execution tracking apply across any workforce-intensive environment facing volatile demand.<\/p>\n<p>Participants leave with a practical framework for evaluating the transition from static, experience-dependent planning to standardized, AI-driven workforce optimization \u2014 and a clear picture of what production-ready decision intelligence looks like at enterprise scale.<\/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>\n        <\/div>\n                        <div  data-anchor=\"gams\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-gams tb_vr5w267 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_409n267 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_j8kz267\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_znnh267 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_ymzs130 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-250x99.png\" width=\"250\" height=\"99\" 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 sub_column col4-2 tb_l15s267 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_41nd267   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Bridging Optimization and Machine Learning: An Exploration with GAMSPy<br>Presented by: Adam Christensen &amp; Steven Dirkse<\/strong><\/p>\n<p><strong>1-2:45pm | Camellia 1<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_p0zs442 \" 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-p0zs442-0\" class=\"tb_title_accordion\" aria-controls=\"acc-p0zs442-0-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-p0zs442-0-content\" data-id=\"acc-p0zs442-0\" 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_u4tm462\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_v9q6463 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_kcew463   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>This workshop introduces 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 exercises. 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 features, demonstrating how GAMSPy can streamline complex modeling workflows and enhance your analytical capabilities.<\/p>\n<p>A major focus of the session is the intersection of optimization and Machine Learning (ML). We will explore essential GAMSPy ML functionality \u2013 such as formulations for neural network layers, activation functions, and regression trees. These tools enable users to implement sophisticated constructs directly within their optimization models, offering flexibility across diverse use cases.<\/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>    <\/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        <\/div>\n                        <div  data-anchor=\"nextmv\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-nextmv tb_5iuq38 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_g3m138 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_9tgp38\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_mz8738 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_gbc0745 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 sub_column col4-2 tb_kdsx38 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_8cru38   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Want Your Decision Project to Succeed? Treat it Like Software<br><\/strong><strong>Presented by: Nicole Misek and Ryan O&#8217;Neil<\/strong><\/p>\n<p><strong>1-2:45pm | Camellia 2<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_w6ab6 \" 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-w6ab6-0\" class=\"tb_title_accordion\" aria-controls=\"acc-w6ab6-0-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-w6ab6-0-content\" data-id=\"acc-w6ab6-0\" 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_ox3t27\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_snvi27 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_bms927   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Decision models drive significant ROI in cost savings, carbon emissions, customer satisfaction, personnel safety, and more. But there are overlooked inefficiencies outside the solver that compound during development, eroding stakeholder trust and confidence. And when it comes time to plug your model into software architectures, the project flounders and fails.<\/p>\n<p>Much of OR training and education has traditionally focused on the elegant and powerful math driving decision models. More recently, leaders within the community are realizing that a software-conscious mindset is necessary for the discipline\u2019s success. The good news: The tools and practices best suited to address this gap are more accessible than ever before.<\/p>\n<p>This session will cover how to leverage the Nextmv platform to easily incorporate DecisionOps into OR and decision science projects. We\u2019ll walk through hands-on exercises that demonstrate using an open source framework for structured local model development that can progress into robust model management, versioning, monitoring, testing, collaboration, and orchestration.<\/p>\n<p>Throughout, you\u2019ll see how to engage stakeholders efficiently by comparing individual model runs, performing scenario tests, comparing multiple models, and seamlessly integrating into the larger decision stack.<\/p>\n<p>Join us for this interactive session that will demonstrate these workflows, allow for hands-on experimentation, and Q&amp;A. This session is relevant to anyone working with tools such as: OR-Tools, Pyomo, HiGHS, Vroom, Gurobi, AMPL, Hexaly, FICO Xpress, NVIDIA cuOpt, IBM CPLEX, CVXPY, and more.<\/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>\n        <\/div>\n                        <div  data-anchor=\"fico\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-fico tb_ezq3450 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_ztof450 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_6lzg450\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_8dck450 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_twl8401 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-1-250x89.png\" width=\"250\" height=\"89\" 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-1-768x275.png 768w, 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 sub_column col4-2 tb_z0ut450 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_jhm5450   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Unlocking Business Value with the Latest from FICO\u00ae Xpress Optimization<br>Presented by: Carlos Zetina<\/strong><\/p>\n<p><strong>3-4:45pm | Camellia 1<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_5iqn128 \" 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-5iqn128-0\" class=\"tb_title_accordion\" aria-controls=\"acc-5iqn128-0-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-5iqn128-0-content\" data-id=\"acc-5iqn128-0\" 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_iraq152\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_zhij152 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_mm9d152   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>In recent years, the scope and responsibilities of optimization Data\/Decision Science (DS) teams continue to expand beyond simple model formulation. DS teams are now closely involved in the deployment, monitoring, and maintenance of the optimization models they develop.<\/p>\n<p>In this session,\u00a0we\u2019ll\u00a0explore how FICO Xpress Optimization\u00a0facilitates\u00a0collaboration among DS teams, engineers, and business users as they build end-to-end enterprise solutions using the latest technology and advances in the field.\u00a0We\u2019ll also\u00a0demonstrate\u00a0how FICO Xpress\u00a0accelerates\u00a0\u201ctime-to-value\u201d with its\u00a0state-of-the-art\u00a0solver,\u00a0robust modeling and programming interfaces, and its low-code\/no-code application development and deployment platform.<\/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>\n        <\/div>\n                        <div  data-anchor=\"gurobi\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-gurobi tb_9oab851 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_g0v7851 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_ov49851\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_zbnk851 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_hik0542 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-275x68.jpg\" width=\"275\" height=\"68\" 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-275x68.jpg 275w, 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-260x65.jpg 260w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue.jpg 600w\" sizes=\"auto, (max-width: 275px) 100vw, 275px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_ps37851 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_f6jb95   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Beans, Bots &amp; Better Solutions: A Gurobi Workshop<\/strong><br><strong>Presented by:\u00a0Xavier Nodet, Lindsay Montanari, Everett Dutton, and Anna Collins<\/strong><\/p>\n<p><strong>3-4:45pm | <\/strong><strong>Magnolia 3<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_gw2m89 \" 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-gw2m89-0\" class=\"tb_title_accordion\" aria-controls=\"acc-gw2m89-0-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-gw2m89-0-content\" data-id=\"acc-gw2m89-0\" 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_i0mi108\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_sbp1108 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_npj1108   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Join us for an engaging and informative session that highlights the latest innovations and practical techniques across the Gurobi ecosystem. We\u2019ll kick off with a brief welcome and agenda overview, along with pointers to other must\u2011see Gurobi sessions. Next, get introduced to Gurobean, with an overview of its purpose and capabilities from the team behind it. We\u2019ll then dive into heuristics and strategies for finding high\u2011quality solutions early, focusing on approaches you can apply to improve performance and results in real-world models. The session continues with updates on Gurobot\u00a0and a walkthrough of other new features, showcasing how these tools can enhance productivity and model understanding. We\u2019ll wrap up with an open Q&amp;A, giving you the chance to ask questions and connect the dots across the topics covered. Whether you\u2019re looking for new tools, practical insights, or what\u2019s coming next, this session offers something for every Gurobi user.<\/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>\n        <\/div>\n                        <div  data-anchor=\"hexaly\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-hexaly tb_0jt2165 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_bffs165 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_h0ji165\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_kvlc165 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_zxle165 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\/2025\/12\/hexaly-orange.svg\" width=\"250\" height=\"81\" class=\"wp-post-image wp-image-10581\" title=\"hexaly-orange\" alt=\"hexaly-orange\">    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_pb9w165 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_x4wj165   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>Hexaly, Hybrid Optimization Solver<\/strong><br><strong>Presented by: Fred Gardi<\/strong><\/p>\n<p><strong>3-4:45pm | <\/strong><strong>Camellia 2<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_zkn7518 \" 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-zkn7518-0\" class=\"tb_title_accordion\" aria-controls=\"acc-zkn7518-0-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-zkn7518-0-content\" data-id=\"acc-zkn7518-0\" 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_63a4536\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_rpv8536 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_aujp536   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Hexaly is a new type of global optimization solver. Its modeling interface, accessible in Python, Java, C#, and C++, is hybrid, unifying concepts from mixed-integer programming, nonlinear programming, constraint programming, and black-box optimization. As a result, it is nonlinear, set-oriented, and supports user-coded functions, enabling seamless integration of simulation with optimization or machine learning with optimization. Under the hood, Hexaly combines exact and heuristic optimization methods: spatial branch-and-bound, simplex methods, interior-point methods, constraint propagation, automatic branch-cut-price, local search, surrogate modeling, among others.<\/p>\n<p>Hexaly stands out from traditional solvers by delivering super-fast solutions to discrete optimization problems such as routing, scheduling, sequencing, packing, picking, clustering, matching, assignment, balancing, and location problems. For example, Hexaly provides solutions close to the best-known results in the literature for vehicle routing problems with thousands of points and job shop scheduling problems with millions of tasks, in just 1 minute of runtime on a basic laptop.<\/p>\n<p>In addition to the Optimizer, Hexaly offers an innovative platform called Hexaly Studio, designed to prototype, develop, and deploy optimization applications quickly in a low-code fashion. This web-based platform is also well-suited for educational purposes; like all other Hexaly products, it is free for faculty and students.<\/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>\n        <\/div>\n                        <div  data-anchor=\"jmp\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-jmp tb_7zez179 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_bvfr179 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_ihe9179\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-2 tb_d4vz179 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_263421 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 sub_column col4-2 tb_kxs2179 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_gi58179   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p><strong>No-code Machine Learning with JMP Student Edition and JMP Pro<\/strong><br><strong>Presented by: Ross Metusalem<\/strong><\/p>\n<p><strong>3-4:45pm | Camellia 3<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_vfza990 \" 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-vfza990-0\" class=\"tb_title_accordion\" aria-controls=\"acc-vfza990-0-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-vfza990-0-content\" data-id=\"acc-vfza990-0\" 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_ie6413\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_fkyt13 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_o87613   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>You don\u2019t have to write code to build modern, powerful machine learning models. JMP Student Edition (free for academic use) and JMP Pro are desktop software applications that provide an interactive, point-and-click interface for predictive modeling, clustering, and dimensionality reduction, as well as specialized techniques including image classification and text mining.<\/p>\n<p>The first hour of this workshop will focus on predictive modeling. You\u2019ll learn how to use JMP software to:<\/p>\n<ul>\n<li>Build predictive models using neural nets, tree-based methods, and other algorithms.<\/li>\n<li>Perform automated cross-validation and hyperparameter tuning.<\/li>\n<li>Explore models with interactive graphics.<\/li>\n<li>Easily fit and compare multiple competing models.<\/li>\n<li>Deploy models in JMP and other software environments.<\/li>\n<\/ul>\n<p>The remainder of this workshop will highlight JMP tools for a selection of additional techniques:<\/p>\n<ul>\n<li>Clustering,<\/li>\n<li>Image classification, and<\/li>\n<li>Integrating JMP and Python to combine capabilities from both.<\/li>\n<\/ul>\n<p>Learn more about JMP software capabilities at <a href=\"https:\/\/www.jmp.com\/capabilities\">jmp.com\/capabilities<\/a>, and get full-featured, free JMP software for academic use at <a href=\"https:\/\/www.jmp.com\/student\">jmp.com\/student<\/a>.<\/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>\n        <\/div>\n        <\/div>\n<!--\/themify_builder_content-->","protected":false},"excerpt":{"rendered":"<p>Exhibitor Workshops<\/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-10539","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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As such, they may include product demonstrations and be commercial.<\/p>\n<h2>Sunday, April 12<\/h2>\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>From Complexity to Clarity: Driving Better Decisions with AnyLogic Simulations<\/strong><br><strong>Presented by: Saurabh Parakh and Martin Franklin<\/strong><\/p> <p><strong>9-10:45am | Magnolia 3<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>MOSIMTEC will demonstrate how AnyLogic Simulation Software enables organizations to make faster, smarter, and more confident decisions in increasingly complex environments. As businesses face growing uncertainty across supply chains, manufacturing, logistics, and service operations, traditional analysis methods often fall short. This session will showcase how multimethod simulation: combining discrete event (DES), agent-based (ABS), and system dynamics (SD) modeling, provides a powerful, flexible approach to understanding real-world systems.<\/p> <p>Through practical examples and live demonstrations, attendees will see how AnyLogic models capture variability, interdependencies, and human behavior to reveal insights that static tools cannot. We will highlight how companies are using simulation to reduce risk, optimize performance, and evaluate strategic scenarios before making costly investments.<\/p> <p>Participants will leave with a clear understanding of why AnyLogic stands out as a leading simulation platform; scalable, visually intuitive, and capable of integrating with real data and enterprise systems. Whether you are new to simulation or looking to enhance your current analytics toolkit, this session will provide actionable insights into leveraging simulation as a competitive advantage.<\/p><\/li><\/ul>\n<p>MOSIMTEC will demonstrate how AnyLogic Simulation Software enables organizations to make faster, smarter, and more confident decisions in increasingly complex environments. As businesses face growing uncertainty across supply chains, manufacturing, logistics, and service operations, traditional analysis methods often fall short. This session will showcase how multimethod simulation: combining discrete event (DES), agent-based (ABS), and system dynamics (SD) modeling, provides a powerful, flexible approach to understanding real-world systems.<\/p> <p>Through practical examples and live demonstrations, attendees will see how AnyLogic models capture variability, interdependencies, and human behavior to reveal insights that static tools cannot. We will highlight how companies are using simulation to reduce risk, optimize performance, and evaluate strategic scenarios before making costly investments.<\/p> <p>Participants will leave with a clear understanding of why AnyLogic stands out as a leading simulation platform; scalable, visually intuitive, and capable of integrating with real data and enterprise systems. Whether you are new to simulation or looking to enhance your current analytics toolkit, this session will provide actionable insights into leveraging simulation as a competitive advantage.<\/p>\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>Getting Optimization into Production: From Prototype Models to Operational Decision Systems<\/strong><br><strong>Presented by: Christian Valente and Juan Bohorquez<\/strong><\/p> <p><strong>11am-12:45pm | Camellia 3<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Optimization models often begin as small analytical prototypes but become much harder to deploy as they grow into operational decision systems. As models scale, teams encounter new challenges related to model structure, system integration, solver performance, and maintaining reliable workflows in production environments.<\/p> <p>This exhibitor workshop demonstrates how optimization applications evolve from simple models to scalable decision systems. Using a power generation portfolio optimization example, we begin with a basic linear programming model and progressively expand it toward a more realistic industry application.<\/p> <p>As the model grows in complexity, we highlight common barriers that arise when moving optimization into production environments, including model maintainability, integration with analytics systems, solver performance and feasibility management, and stakeholder trust in decision models.<\/p> <p>Participants will see how these challenges can be addressed through practical modeling patterns, solver strategies, and system integration approaches. The session will also illustrate accessible on-ramps for getting started with optimization and how those approaches can scale into robust production deployments.<\/p> <p>By the end of the workshop, attendees will have seen how a simple optimization model can evolve into a more realistic decision application while overcoming several common barriers to operational deployment.<\/p><\/li><\/ul>\n<p>Optimization models often begin as small analytical prototypes but become much harder to deploy as they grow into operational decision systems. As models scale, teams encounter new challenges related to model structure, system integration, solver performance, and maintaining reliable workflows in production environments.<\/p> <p>This exhibitor workshop demonstrates how optimization applications evolve from simple models to scalable decision systems. Using a power generation portfolio optimization example, we begin with a basic linear programming model and progressively expand it toward a more realistic industry application.<\/p> <p>As the model grows in complexity, we highlight common barriers that arise when moving optimization into production environments, including model maintainability, integration with analytics systems, solver performance and feasibility management, and stakeholder trust in decision models.<\/p> <p>Participants will see how these challenges can be addressed through practical modeling patterns, solver strategies, and system integration approaches. The session will also illustrate accessible on-ramps for getting started with optimization and how those approaches can scale into robust production deployments.<\/p> <p>By the end of the workshop, attendees will have seen how a simple optimization model can evolve into a more realistic decision application while overcoming several common barriers to operational deployment.<\/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>Model and Solve Nonlinear Optimization Problems with Artelys Knitro<br><\/strong><strong>Presented by: Louis-Pierre Campeau<\/strong><\/p> <p><strong>11am-12:45pm | Camellia 1<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Artelys Knitro is the leading solver focused on large-scale, nonlinear (potentially non-convex), optimization problems. Knitro offers both interior-point and active-set algorithms for continuous models, as well as tools for handling problems with integer variables and other discrete structures. This tutorial will introduce the key features of Knitro and demonstrate how to use Knitro to model and solve optimization problems in various environments. The talk will showcase several instances of nonlinear problems, typically addressed through linear relaxation, yet warranting a direct approach. It will delve into the methodologies and tools employed to tackle these challenges. This tutorial aims at an audience familiar with the basics of mathematical optimization and Python programming but will mostly be focusing on practical examples.<\/p><\/li><\/ul>\n<p>Artelys Knitro is the leading solver focused on large-scale, nonlinear (potentially non-convex), optimization problems. Knitro offers both interior-point and active-set algorithms for continuous models, as well as tools for handling problems with integer variables and other discrete structures. This tutorial will introduce the key features of Knitro and demonstrate how to use Knitro to model and solve optimization problems in various environments. The talk will showcase several instances of nonlinear problems, typically addressed through linear relaxation, yet warranting a direct approach. It will delve into the methodologies and tools employed to tackle these challenges. This tutorial aims at an audience familiar with the basics of mathematical optimization and Python programming but will mostly be focusing on practical examples.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/03\/ORMAE-web_ready_company_logo-V-723x1024-150x213.png\" width=\"150\" height=\"213\" 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>Accelerating Time-to-Value with Explainable AI and Agentic Decision Workflows<br>Presented by: Amit Garg and William Lopez<\/strong><\/p> <p><strong>11am-12:45pm | <\/strong><strong>Camellia 2<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Across industries, organizations are increasingly adopting advanced optimization to enhance planning, scheduling, and operational performance. As these models become more powerful, the next frontier is enabling decision-makers across the enterprise to interact with optimization systems seamlessly and translate insights into everyday operational decisions.<\/p> <p>This workshop introduces the architecture and implementation of an AI-powered Decision Intelligence platform\u00a0developed by ORMAE that combines mathematical optimization with Explainable AI and agentic decision workflows. The platform integrates optimization engines with conversational interfaces, explainability layers, and event-driven agents that respond dynamically to changing operational conditions.<\/p> <p>Participants will explore how planners and business users can interact with optimization systems using natural language, rapidly generate scenarios, and understand the reasoning behind recommended decisions. The session will include a live demonstration and hands-on walkthrough of an AI-driven optimization application, illustrating how optimization models can be operationalized as interactive decision tools within enterprise workflows.<\/p> <p>Attendees will leave with a practical framework for deploying explainable, adaptive optimization systems, accelerating time-to-value and expanding the role of decision intelligence across supply chains, industrial operations, and enterprise planning environments.<\/p><\/li><\/ul>\n<p>Across industries, organizations are increasingly adopting advanced optimization to enhance planning, scheduling, and operational performance. As these models become more powerful, the next frontier is enabling decision-makers across the enterprise to interact with optimization systems seamlessly and translate insights into everyday operational decisions.<\/p> <p>This workshop introduces the architecture and implementation of an AI-powered Decision Intelligence platform\u00a0developed by ORMAE that combines mathematical optimization with Explainable AI and agentic decision workflows. The platform integrates optimization engines with conversational interfaces, explainability layers, and event-driven agents that respond dynamically to changing operational conditions.<\/p> <p>Participants will explore how planners and business users can interact with optimization systems using natural language, rapidly generate scenarios, and understand the reasoning behind recommended decisions. The session will include a live demonstration and hands-on walkthrough of an AI-driven optimization application, illustrating how optimization models can be operationalized as interactive decision tools within enterprise workflows.<\/p> <p>Attendees will leave with a practical framework for deploying explainable, adaptive optimization systems, accelerating time-to-value and expanding the role of decision intelligence across supply chains, industrial operations, and enterprise planning environments.<\/p>\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>SAS Analytics and Quantum Computing<\/strong><br><strong>Presented by: Rob Pratt and Yan Xu<\/strong><\/p> <p><strong>11am-12:45pm | Magnolia 3<\/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>This workshop will review the SAS analytics portfolio, highlight recently added optimization features, and explore several optimization examples, including an application where quantum computing yields performance improvements.<\/p> <p>Academic faculty, staff, and students can access the latest SAS software through our free academic software platforms SAS Viya for Learners and SAS Viya Workbench for Learners. Access instructions and learning resources will be provided.<\/p><\/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>This workshop will review the SAS analytics portfolio, highlight recently added optimization features, and explore several optimization examples, including an application where quantum computing yields performance improvements.<\/p> <p>Academic faculty, staff, and students can access the latest SAS software through our free academic software platforms SAS Viya for Learners and SAS Viya Workbench for Learners. Access instructions and learning resources will be provided.<\/p>\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>Beyond Narratives: Making LLM Knowledge Computable with Bayesian Networks<br><\/strong><strong>Presented by: Stefan Conrady<\/strong><\/p> <p><strong>1-2:45pm | <\/strong><strong>Camellia 3<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Recent advances in large language models (LLMs) have made vast amounts of domain knowledge accessible in natural language form. However, this knowledge remains implicit, unstructured, and difficult to operationalize for rigorous analysis or decision support. This workshop introduces a novel methodology for transforming language-based knowledge into explicit, simulation-ready causal models.<\/p> <p>Using BayesiaLab and its subject-matter assistant, Hellixia, participants will see how knowledge derived from LLMs can be combined with empirical data and human expertise to construct Bayesian networks. The approach translates high-level, language-based abstractions into formal variables, causal relationships, and probabilistic dependencies, yielding transparent models that support causal inference, scenario analysis, and decision optimization.<\/p> <p>Following this introduction, the workshop presents a supply chain case study that demonstrates the end-to-end workflow. Starting from a business question, participants will observe how LLM-informed knowledge is structured, refined, and integrated with data to produce a fully operational model.<\/p> <p>By making implicit knowledge explicit and computable, this methodology enables a new class of analytics workflows that bridge generative AI and causal modeling. The resulting models are interpretable, auditable, and directly applicable to complex decision-making problems across domains.<\/p><\/li><\/ul>\n<p>Recent advances in large language models (LLMs) have made vast amounts of domain knowledge accessible in natural language form. However, this knowledge remains implicit, unstructured, and difficult to operationalize for rigorous analysis or decision support. This workshop introduces a novel methodology for transforming language-based knowledge into explicit, simulation-ready causal models.<\/p> <p>Using BayesiaLab and its subject-matter assistant, Hellixia, participants will see how knowledge derived from LLMs can be combined with empirical data and human expertise to construct Bayesian networks. The approach translates high-level, language-based abstractions into formal variables, causal relationships, and probabilistic dependencies, yielding transparent models that support causal inference, scenario analysis, and decision optimization.<\/p> <p>Following this introduction, the workshop presents a supply chain case study that demonstrates the end-to-end workflow. Starting from a business question, participants will observe how LLM-informed knowledge is structured, refined, and integrated with data to produce a fully operational model.<\/p> <p>By making implicit knowledge explicit and computable, this methodology enables a new class of analytics workflows that bridge generative AI and causal modeling. The resulting models are interpretable, auditable, and directly applicable to complex decision-making problems across domains.<\/p>\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>Scaling Adaptive Workforce Optimization: How ATALIAN and DecisionBrain Replaced Manual Scheduling with Real-Time Intelligence<br>Presented by: Tom Heyward<\/strong><\/p> <p><strong>1-2:45pm | Magnolia 3<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Airport cleaning operations run against one of the most demanding scheduling environments in facility management \u2014 shifting flight schedules, unpredictable passenger flow, and cascading task dependencies that render static daily plans obsolete within hours. This workshop presents how ATALIAN, a global leader in facility management, partnered with DecisionBrain to replace manual coordination with a dynamic, closed-loop scheduling system powered by the DB Gene platform.<\/p> <p>Attendees will see how real-time external data \u2014 including live flight information and zone-level crowd distribution \u2014 feeds directly into optimization logic to continuously revise workforce schedules as conditions evolve. We walk through a three-tier decision support model serving managers, supervisors, and frontline agents, and examine how role-specific workflows drove adoption across the organization.<\/p> <p>Beyond the airport context, the session draws out the transferable principles: how situation-aware constraints, automated re-scheduling, and real-time execution tracking apply across any workforce-intensive environment facing volatile demand.<\/p> <p>Participants leave with a practical framework for evaluating the transition from static, experience-dependent planning to standardized, AI-driven workforce optimization \u2014 and a clear picture of what production-ready decision intelligence looks like at enterprise scale.<\/p><\/li><\/ul>\n<p>Airport cleaning operations run against one of the most demanding scheduling environments in facility management \u2014 shifting flight schedules, unpredictable passenger flow, and cascading task dependencies that render static daily plans obsolete within hours. This workshop presents how ATALIAN, a global leader in facility management, partnered with DecisionBrain to replace manual coordination with a dynamic, closed-loop scheduling system powered by the DB Gene platform.<\/p> <p>Attendees will see how real-time external data \u2014 including live flight information and zone-level crowd distribution \u2014 feeds directly into optimization logic to continuously revise workforce schedules as conditions evolve. We walk through a three-tier decision support model serving managers, supervisors, and frontline agents, and examine how role-specific workflows drove adoption across the organization.<\/p> <p>Beyond the airport context, the session draws out the transferable principles: how situation-aware constraints, automated re-scheduling, and real-time execution tracking apply across any workforce-intensive environment facing volatile demand.<\/p> <p>Participants leave with a practical framework for evaluating the transition from static, experience-dependent planning to standardized, AI-driven workforce optimization \u2014 and a clear picture of what production-ready decision intelligence looks like at enterprise scale.<\/p>\n<a href=\"https:\/\/www.gams.com\/\"> <img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/GAMS-Logo-250x99.png\" width=\"250\" height=\"99\" 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>Bridging Optimization and Machine Learning: An Exploration with GAMSPy<br>Presented by: Adam Christensen &amp; Steven Dirkse<\/strong><\/p> <p><strong>1-2:45pm | Camellia 1<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>This workshop introduces 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 exercises. 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 features, demonstrating how GAMSPy can streamline complex modeling workflows and enhance your analytical capabilities.<\/p> <p>A major focus of the session is the intersection of optimization and Machine Learning (ML). We will explore essential GAMSPy ML functionality \u2013 such as formulations for neural network layers, activation functions, and regression trees. These tools enable users to implement sophisticated constructs directly within their optimization models, offering flexibility across diverse use cases.<\/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><\/li><\/ul>\n<p>This workshop introduces 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 exercises. 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 features, demonstrating how GAMSPy can streamline complex modeling workflows and enhance your analytical capabilities.<\/p> <p>A major focus of the session is the intersection of optimization and Machine Learning (ML). We will explore essential GAMSPy ML functionality \u2013 such as formulations for neural network layers, activation functions, and regression trees. These tools enable users to implement sophisticated constructs directly within their optimization models, offering flexibility across diverse use cases.<\/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>\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>Want Your Decision Project to Succeed? Treat it Like Software<br><\/strong><strong>Presented by: Nicole Misek and Ryan O'Neil<\/strong><\/p> <p><strong>1-2:45pm | Camellia 2<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Decision models drive significant ROI in cost savings, carbon emissions, customer satisfaction, personnel safety, and more. But there are overlooked inefficiencies outside the solver that compound during development, eroding stakeholder trust and confidence. And when it comes time to plug your model into software architectures, the project flounders and fails.<\/p> <p>Much of OR training and education has traditionally focused on the elegant and powerful math driving decision models. More recently, leaders within the community are realizing that a software-conscious mindset is necessary for the discipline\u2019s success. The good news: The tools and practices best suited to address this gap are more accessible than ever before.<\/p> <p>This session will cover how to leverage the Nextmv platform to easily incorporate DecisionOps into OR and decision science projects. We\u2019ll walk through hands-on exercises that demonstrate using an open source framework for structured local model development that can progress into robust model management, versioning, monitoring, testing, collaboration, and orchestration.<\/p> <p>Throughout, you\u2019ll see how to engage stakeholders efficiently by comparing individual model runs, performing scenario tests, comparing multiple models, and seamlessly integrating into the larger decision stack.<\/p> <p>Join us for this interactive session that will demonstrate these workflows, allow for hands-on experimentation, and Q&amp;A. This session is relevant to anyone working with tools such as: OR-Tools, Pyomo, HiGHS, Vroom, Gurobi, AMPL, Hexaly, FICO Xpress, NVIDIA cuOpt, IBM CPLEX, CVXPY, and more.<\/p><\/li><\/ul>\n<p>Decision models drive significant ROI in cost savings, carbon emissions, customer satisfaction, personnel safety, and more. But there are overlooked inefficiencies outside the solver that compound during development, eroding stakeholder trust and confidence. And when it comes time to plug your model into software architectures, the project flounders and fails.<\/p> <p>Much of OR training and education has traditionally focused on the elegant and powerful math driving decision models. More recently, leaders within the community are realizing that a software-conscious mindset is necessary for the discipline\u2019s success. The good news: The tools and practices best suited to address this gap are more accessible than ever before.<\/p> <p>This session will cover how to leverage the Nextmv platform to easily incorporate DecisionOps into OR and decision science projects. We\u2019ll walk through hands-on exercises that demonstrate using an open source framework for structured local model development that can progress into robust model management, versioning, monitoring, testing, collaboration, and orchestration.<\/p> <p>Throughout, you\u2019ll see how to engage stakeholders efficiently by comparing individual model runs, performing scenario tests, comparing multiple models, and seamlessly integrating into the larger decision stack.<\/p> <p>Join us for this interactive session that will demonstrate these workflows, allow for hands-on experimentation, and Q&amp;A. This session is relevant to anyone working with tools such as: OR-Tools, Pyomo, HiGHS, Vroom, Gurobi, AMPL, Hexaly, FICO Xpress, NVIDIA cuOpt, IBM CPLEX, CVXPY, and more.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/01\/FICO_RGB_Blue-1-250x89.png\" width=\"250\" height=\"89\" 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-1-768x275.png 768w, 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>Unlocking Business Value with the Latest from FICO\u00ae Xpress Optimization<br>Presented by: Carlos Zetina<\/strong><\/p> <p><strong>3-4:45pm | Camellia 1<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>In recent years, the scope and responsibilities of optimization Data\/Decision Science (DS) teams continue to expand beyond simple model formulation. DS teams are now closely involved in the deployment, monitoring, and maintenance of the optimization models they develop.<\/p> <p>In this session,\u00a0we\u2019ll\u00a0explore how FICO Xpress Optimization\u00a0facilitates\u00a0collaboration among DS teams, engineers, and business users as they build end-to-end enterprise solutions using the latest technology and advances in the field.\u00a0We\u2019ll also\u00a0demonstrate\u00a0how FICO Xpress\u00a0accelerates\u00a0\u201ctime-to-value\u201d with its\u00a0state-of-the-art\u00a0solver,\u00a0robust modeling and programming interfaces, and its low-code\/no-code application development and deployment platform.<\/p><\/li><\/ul>\n<p>In recent years, the scope and responsibilities of optimization Data\/Decision Science (DS) teams continue to expand beyond simple model formulation. DS teams are now closely involved in the deployment, monitoring, and maintenance of the optimization models they develop.<\/p> <p>In this session,\u00a0we\u2019ll\u00a0explore how FICO Xpress Optimization\u00a0facilitates\u00a0collaboration among DS teams, engineers, and business users as they build end-to-end enterprise solutions using the latest technology and advances in the field.\u00a0We\u2019ll also\u00a0demonstrate\u00a0how FICO Xpress\u00a0accelerates\u00a0\u201ctime-to-value\u201d with its\u00a0state-of-the-art\u00a0solver,\u00a0robust modeling and programming interfaces, and its low-code\/no-code application development and deployment platform.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue-275x68.jpg\" width=\"275\" height=\"68\" title=\"600x150px_Gurobi_Logo_Blue\" alt=\"600x150px_Gurobi_Logo_Blue\" srcset=\"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-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-260x65.jpg 260w, https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2026\/04\/600x150px_Gurobi_Logo_Blue.jpg 600w\" sizes=\"(max-width: 275px) 100vw, 275px\" \/>\n<p><strong>Beans, Bots &amp; Better Solutions: A Gurobi Workshop<\/strong><br><strong>Presented by:\u00a0Xavier Nodet, Lindsay Montanari, Everett Dutton, and Anna Collins<\/strong><\/p> <p><strong>3-4:45pm | <\/strong><strong>Magnolia 3<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Join us for an engaging and informative session that highlights the latest innovations and practical techniques across the Gurobi ecosystem. We\u2019ll kick off with a brief welcome and agenda overview, along with pointers to other must\u2011see Gurobi sessions. Next, get introduced to Gurobean, with an overview of its purpose and capabilities from the team behind it. We\u2019ll then dive into heuristics and strategies for finding high\u2011quality solutions early, focusing on approaches you can apply to improve performance and results in real-world models. The session continues with updates on Gurobot\u00a0and a walkthrough of other new features, showcasing how these tools can enhance productivity and model understanding. We\u2019ll wrap up with an open Q&amp;A, giving you the chance to ask questions and connect the dots across the topics covered. Whether you\u2019re looking for new tools, practical insights, or what\u2019s coming next, this session offers something for every Gurobi user.<\/p><\/li><\/ul>\n<p>Join us for an engaging and informative session that highlights the latest innovations and practical techniques across the Gurobi ecosystem. We\u2019ll kick off with a brief welcome and agenda overview, along with pointers to other must\u2011see Gurobi sessions. Next, get introduced to Gurobean, with an overview of its purpose and capabilities from the team behind it. We\u2019ll then dive into heuristics and strategies for finding high\u2011quality solutions early, focusing on approaches you can apply to improve performance and results in real-world models. The session continues with updates on Gurobot\u00a0and a walkthrough of other new features, showcasing how these tools can enhance productivity and model understanding. We\u2019ll wrap up with an open Q&amp;A, giving you the chance to ask questions and connect the dots across the topics covered. Whether you\u2019re looking for new tools, practical insights, or what\u2019s coming next, this session offers something for every Gurobi user.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/analytics\/files\/2025\/12\/hexaly-orange.svg\" width=\"250\" height=\"81\" title=\"hexaly-orange\" alt=\"hexaly-orange\">\n<p><strong>Hexaly, Hybrid Optimization Solver<\/strong><br><strong>Presented by: Fred Gardi<\/strong><\/p> <p><strong>3-4:45pm | <\/strong><strong>Camellia 2<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>Hexaly is a new type of global optimization solver. Its modeling interface, accessible in Python, Java, C#, and C++, is hybrid, unifying concepts from mixed-integer programming, nonlinear programming, constraint programming, and black-box optimization. As a result, it is nonlinear, set-oriented, and supports user-coded functions, enabling seamless integration of simulation with optimization or machine learning with optimization. Under the hood, Hexaly combines exact and heuristic optimization methods: spatial branch-and-bound, simplex methods, interior-point methods, constraint propagation, automatic branch-cut-price, local search, surrogate modeling, among others.<\/p> <p>Hexaly stands out from traditional solvers by delivering super-fast solutions to discrete optimization problems such as routing, scheduling, sequencing, packing, picking, clustering, matching, assignment, balancing, and location problems. For example, Hexaly provides solutions close to the best-known results in the literature for vehicle routing problems with thousands of points and job shop scheduling problems with millions of tasks, in just 1 minute of runtime on a basic laptop.<\/p> <p>In addition to the Optimizer, Hexaly offers an innovative platform called Hexaly Studio, designed to prototype, develop, and deploy optimization applications quickly in a low-code fashion. This web-based platform is also well-suited for educational purposes; like all other Hexaly products, it is free for faculty and students.<\/p><\/li><\/ul>\n<p>Hexaly is a new type of global optimization solver. Its modeling interface, accessible in Python, Java, C#, and C++, is hybrid, unifying concepts from mixed-integer programming, nonlinear programming, constraint programming, and black-box optimization. As a result, it is nonlinear, set-oriented, and supports user-coded functions, enabling seamless integration of simulation with optimization or machine learning with optimization. Under the hood, Hexaly combines exact and heuristic optimization methods: spatial branch-and-bound, simplex methods, interior-point methods, constraint propagation, automatic branch-cut-price, local search, surrogate modeling, among others.<\/p> <p>Hexaly stands out from traditional solvers by delivering super-fast solutions to discrete optimization problems such as routing, scheduling, sequencing, packing, picking, clustering, matching, assignment, balancing, and location problems. For example, Hexaly provides solutions close to the best-known results in the literature for vehicle routing problems with thousands of points and job shop scheduling problems with millions of tasks, in just 1 minute of runtime on a basic laptop.<\/p> <p>In addition to the Optimizer, Hexaly offers an innovative platform called Hexaly Studio, designed to prototype, develop, and deploy optimization applications quickly in a low-code fashion. This web-based platform is also well-suited for educational purposes; like all other Hexaly products, it is free for faculty and students.<\/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>No-code Machine Learning with JMP Student Edition and JMP Pro<\/strong><br><strong>Presented by: Ross Metusalem<\/strong><\/p> <p><strong>3-4:45pm | Camellia 3<\/strong><\/p>\n<ul><li><h4>Description<\/h4><p>You don\u2019t have to write code to build modern, powerful machine learning models. JMP Student Edition (free for academic use) and JMP Pro are desktop software applications that provide an interactive, point-and-click interface for predictive modeling, clustering, and dimensionality reduction, as well as specialized techniques including image classification and text mining.<\/p> <p>The first hour of this workshop will focus on predictive modeling. You\u2019ll learn how to use JMP software to:<\/p> <ul> <li>Build predictive models using neural nets, tree-based methods, and other algorithms.<\/li> <li>Perform automated cross-validation and hyperparameter tuning.<\/li> <li>Explore models with interactive graphics.<\/li> <li>Easily fit and compare multiple competing models.<\/li> <li>Deploy models in JMP and other software environments.<\/li> <\/ul> <p>The remainder of this workshop will highlight JMP tools for a selection of additional techniques:<\/p> <ul> <li>Clustering,<\/li> <li>Image classification, and<\/li> <li>Integrating JMP and Python to combine capabilities from both.<\/li> <\/ul> <p>Learn more about JMP software capabilities at <a href=\"https:\/\/www.jmp.com\/capabilities\">jmp.com\/capabilities<\/a>, and get full-featured, free JMP software for academic use at <a href=\"https:\/\/www.jmp.com\/student\">jmp.com\/student<\/a>.<\/p><\/li><\/ul>\n<p>You don\u2019t have to write code to build modern, powerful machine learning models. JMP Student Edition (free for academic use) and JMP Pro are desktop software applications that provide an interactive, point-and-click interface for predictive modeling, clustering, and dimensionality reduction, as well as specialized techniques including image classification and text mining.<\/p> <p>The first hour of this workshop will focus on predictive modeling. You\u2019ll learn how to use JMP software to:<\/p> <ul> <li>Build predictive models using neural nets, tree-based methods, and other algorithms.<\/li> <li>Perform automated cross-validation and hyperparameter tuning.<\/li> <li>Explore models with interactive graphics.<\/li> <li>Easily fit and compare multiple competing models.<\/li> <li>Deploy models in JMP and other software environments.<\/li> <\/ul> <p>The remainder of this workshop will highlight JMP tools for a selection of additional techniques:<\/p> <ul> <li>Clustering,<\/li> <li>Image classification, and<\/li> <li>Integrating JMP and Python to combine capabilities from both.<\/li> <\/ul> <p>Learn more about JMP software capabilities at <a href=\"https:\/\/www.jmp.com\/capabilities\">jmp.com\/capabilities<\/a>, and get full-featured, free JMP software for academic use at <a href=\"https:\/\/www.jmp.com\/student\">jmp.com\/student<\/a>.<\/p>","_links":{"self":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/pages\/10539","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=10539"}],"version-history":[{"count":222,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/pages\/10539\/revisions"}],"predecessor-version":[{"id":12873,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/pages\/10539\/revisions\/12873"}],"wp:attachment":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics\/wp-json\/wp\/v2\/media?parent=10539"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}