{"id":5135,"date":"2024-02-15T15:06:32","date_gmt":"2024-02-15T15:06:32","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/speakers\/armonie-boucharens-copy\/"},"modified":"2024-02-15T15:51:22","modified_gmt":"2024-02-15T15:51:22","slug":"iaaa-finalists","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/speakers\/iaaa-finalists\/","title":{"rendered":"IAAA Finalists"},"content":{"rendered":"<!--themify_builder_content-->\n<div id=\"themify_builder_content-5135\" data-postid=\"5135\" class=\"themify_builder_content themify_builder_content-5135 themify_builder tf_clear\">\n                    <div  data-css_id=\"y990649\" data-lazy=\"1\" class=\"module_row themify_builder_row fullwidth_row_container tb_y990649 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_p5xb445 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_dw32055 track  \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h6>Track: INFORMS Prizes &#8211; IAAA Finalists<\/h6>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module text -->\n<div  class=\"module module-text tb_8yiu590   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>The IAAA Finalists will present at the conference on Tuesday, April 16 in the INFORMS Prizes Track from 9:10am \u2013 12:20pm EST. Judges will then review the rank and announce the winner at the INFORMS Analytics Society luncheon.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_qsmi272 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_l69b273 first\">\n                    <!-- module accordion -->\n<div  class=\"module module-accordion tb_94pc306 \" 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-94pc306-0\" class=\"tb_title_accordion\" aria-controls=\"acc-94pc306-0-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\">Amplifying Marketing Impact: Optimizing Network TV Advertising Inventory with Advanced Analytics<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-94pc306-0-content\" data-id=\"acc-94pc306-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p>Sebastian Souyris, Lally School of Management, Rensselaer Polytechnic Institute<br>Sridhar Seshadri, Gies College of Business, University of Illinois Urbana-Champaign<br>Dana Popescu, Darden Business School, University of Virginia<br>Vinod Kumawat, RSG Media<br>Anand Punjabi, RSG Media<br>Shiv Sehgal, RSG Media<br>Divyani Sharma, RSG Media<br>Varun Verma, RSG Media<\/p>\n<p>Promotional advertisements are a fundamental marketing instrument for television networks, enabling them to foster audience engagement for upcoming television programs. The efficient planning of such promotional material is crucial for a show&#8217;s success, generating revenue opportunities for networks via commercial advertising. Traditionally, networks have employed manual processes for devising multiple promotional campaigns; however, these methods are labor-intensive, time-consuming, and inefficient.<br>RSG Audience Marketing Prophet application optimizes and automates the promotional planning process for networks, ensuring each campaign attains its desired outcomes while adhering to the constraints imposed by a limited marketing inventory and network conditions. To address this challenge, we have developed an innovative approach that synergizes mathematical programming and machine learning techniques to produce comprehensive long-term promotional campaign strategies and daily promotional schedules. These plans and schedules are of high quality, as evidenced by conventional business metrics and a minimal integer programming gap. Using our methodology, 24 TV cable and one broadcasting network (Paramount Global and A&amp;E Networks) have observed a 5% to 17% increment in marketing inventory for commercial advertising.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion --><!-- module accordion -->\n<div  class=\"module module-accordion tb_q7hq474 \" 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-q7hq474-0\" class=\"tb_title_accordion\" aria-controls=\"acc-q7hq474-0-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\">Battling Nursing Shortage Crisis: Building Innovative Staff Deployment Solutions with Human Expertise and Machine Learning<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-q7hq474-0-content\" data-id=\"acc-q7hq474-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p>Pengyi Shi, Associate Professor of Operations Management, Mitchell E. Daniels, Jr. School of Business, Purdue University<br>Jonathan E. Helm, Professor, Operations &amp; Decision Technologies, Kelley School of Business, Indiana University; Industry Partner and Contact: Indiana University Health<br>Mary Drewes, Vice President, Associate Chief Nurse Executive, Operations<br>Jacob Cecil, Data Analyst, Workforce Central<br>William Tindall, Director System Resource Center and Staffing Optimization<br>Troy Tinsley, Regional Director of Supply Chain at Riley Children\u2019s Health. (Former) Director of Workforce Strategy and Operational Excellence<\/p>\n<p>We partnered with the largest health system in Indiana, IU Health (IUH), to co-develop and implement an advanced analytics-driven decision support solution (DSS) to support their novel practice of mobilizing resource nurses across its 16 hospitals. The aim is to reduce staffing shortage, improve patient care, reduce harm events, and increase retention for nurses through improved working conditions. Our DSS combines state-of-the-art generative AI models with multi-stage reinforcement learning, creating a framework that handles both forecasting and decision optimization in environments with complex spatial-temporal correlations. The DSS provides multi-tiered recommendations, including resource nurse on-call lists, real-time redeployment between hospitals, and future hiring decisions. We conducted a pilot from May to June 2023, which produced a remarkable 17% reduction in understaffing, with estimated annual savings of $2.5-3.5 million in IUH alone, with over $1.5 billion projected savings if adopted on a national scale.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_3jej151 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_5ewq151 first\">\n                    <!-- module accordion -->\n<div  class=\"module module-accordion tb_7e1t878 \" 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-7e1t878-0\" class=\"tb_title_accordion\" aria-controls=\"acc-7e1t878-0-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\">Interdependencies and Cascading Effects of Disasters on Critical Infrastructures<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-7e1t878-0-content\" data-id=\"acc-7e1t878-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p>Eva K Lee, PhD, Director, Whitaker-NSF Center for Operations Research in Medicine and HealthCare;<br>Chief Scientific Officer, the Data and Analytics Innovation Institute; Senior Data Scientist, AccuHealth<br>Technologies; Subject Matter Expert, DHS Medical &amp; Public Health Information Sharing Environment.<br>William Wang, PhD, Georgia Institute of Technology; Research Scientist, Amazon.<br>Taylor Leonard, PhD, Lieutenant Colonel, United States Air Force; Chief, Strike Branch, Department of<br>the Air Force, Office of Studies and Analysis.<br>Jerry Booker, Former Director, Enterprise Risk &amp;. Performance, TSA\u2013Strategy, Policy Coordination, and<br>Innovation.<\/p>\n<p>Critical Infrastructure (CI) is vital for a modern economy, and ensuring its security is crucial. The potential incapacitation or destruction of CI could have severe consequences for national security, the economy, public health, and safety. This project specifically examines the primary effects of cyber-attacks on critical infrastructure, investigating hidden vulnerabilities, cyber-physical disruptions, and their cascading impacts. Our goal is to develop analytical methods that provide valuable insights for policymakers, aiding in informed decision-making. Our accomplishments include (a) establishing the first influence system network model to reveal vulnerabilities with maximum cascading effects in communication critical infrastructure, utilizing scalable computational advances to model the entire U.S. communication network; (b) creating a multi-layer influence network incorporating interdependencies across various CI sectors; and (c) proving theoretical results about the monotonicity and submodularity of the influence function, with implications for computational complexity. The analytic tools and computational framework enhance the capabilities of cybersecurity and infrastructure security leaders, providing improved assessment for decision-making. This contributes to better system analysis, content relevancy, and long-term objectives of assisting decision-makers in assessing disruptions, establishing policies, and designing effective mitigation strategies. Identifying critical nodes enhances both defense and offense strategies, optimizing resource allocation for maximum protection.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_vd2l673 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_3y56673 first\">\n                    <!-- module accordion -->\n<div  class=\"module module-accordion tb_fojv99 \" 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-fojv99-0\" class=\"tb_title_accordion\" aria-controls=\"acc-fojv99-0-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\">Optimizing Milk Pools at the Rogers Hixon Ontario Human Milk Bank<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-fojv99-0-content\" data-id=\"acc-fojv99-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p>Timothy C. Y. Chan, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada<br>Rafid Mahmood, Telfer School of Management, University of Ottawa, Ottawa, ON, Canada<br>Deborah L O\u2019Connor, Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada<br>Debbie Stone, Rogers Hixon Ontario Human Milk Bank, Toronto, ON, Canada<br>Sharon Unger, Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada<br>Rachel K. Wong, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada<br>Ian Yihang Zhu NUS Business School, National University of Singapore, Singapore<\/p>\n<p>Human donor milk provides critical nutrition for millions of infants that are born preterm each year. Donor milk is collected, processed, and distributed by milk banks that pool donations together to improve the overall macronutrient content. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content in donations, which means pooling is done heuristically. We collaborated with the Rogers Hixon Ontario Human Milk Bank (RHOHMB) to create a data-driven milk pooling optimization framework via predictive and prescriptive modeling. We implemented this framework over a year-long trial and observed that pools created by our approach met clinical macronutrient targets approximately 31% more often than the previous nurse-led approach, while taking 60% less recipe creation time. This is the first work in the broader blending literature that combines machine learning and optimization. Our results demonstrate that such pipelines are feasible to implement in a healthcare setting and can yield significant improvements over current practices.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion --><!-- module accordion -->\n<div  class=\"module module-accordion tb_730448 \" 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-730448-0\" class=\"tb_title_accordion\" aria-controls=\"acc-730448-0-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\">Policy Learning Utilizing Counterfactuals with an Application to the Dynamic Pricing of Airline Ancillaries<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-730448-0-content\" data-id=\"acc-730448-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p>Shivaram Subramanian, IBM Thomas J. Watson Research Ctr. <br>Wei Sun, IBM Thomas J. Watson Research Ctr. <br>Markus Ettl, IBM Thomas J. Watson Research Ctr. <br>Youssef Drissi, IBM Thomas J. Watson Research Ctr. <br>Zhengliang Xue, IBM Thomas J. Watson Research Ctr.<\/p>\n<p>Recent advances in AI have encouraged organizations to implement data-driven prescriptive analytics to automate and improve their decision-making capabilities. Such prescriptive policies must satisfy a variety of operational and fairness constraints that are ubiquitous in practice. A simple and interpretable policy is preferable as they can be easily verified and integrated into existing systems. Prior literature focused on constructing variants of prescriptive decision trees that are unable to satisfy such constraints. Our application employs a novel causal-teacher prescriptive-student framework to optimize a constrained prescriptive policy generation problem. We employ a counterfactual estimator that allows the application to be agnostic to the choice of the prediction methods, and deployable across diverse domains. We solve a novel path-based mixed-integer program (MIP) using column generation to derive an optimal policy that represents a multiway-split decision tree. We demonstrate the efficacy of our application by partnering with a global airline carrier to successfully live-test pricing policies for ancillary products.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion --><!-- module accordion -->\n<div  class=\"module module-accordion tb_w8mh511 \" 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-w8mh511-0\" class=\"tb_title_accordion\" aria-controls=\"acc-w8mh511-0-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\">Using Innovative Analytics on Two Levels to Increase Vaccination Rates<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-w8mh511-0-content\" data-id=\"acc-w8mh511-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p>Theodore T. Allen, Ph.D. FactSpread Columbus, Ohio; The Ohio State University, Integrated Systems Engineering, Columbus, Ohio.<br>Antor Rashid, formerly FactSpread Columbus, Ohio; The Ohio State University, Integrated Systems Engineering, Columbus, Ohio.<br>Long Wang, Ph.D. FactSpread Columbus, Ohio.<\/p>\n<p>Our vaccination-related advertisements reached approximately 1 million people (3.9 million impressions) across over eight states. Our analytics-based approach was innovative partly because we prepared analytical information and presented it to millions of people. We also used analytical approaches to design our messages, target the campaigns, and measure the results. Optimization was used to cluster similar counties. Then, optimal experimental design and regression analyses were applied to tune the advertisement selections and policy parameters. A second, more major campaign likely benefited from the improved parameters. Estimates of people caused to become vaccinated include 0.7M over one year (p-value 0.001). Another estimate including information contagion effects is 2.2M (p-value 0.002). Therefore, estimates of the lives saved include 0.7M \u00f7 124 = 6,000 and 2.2M \u00f7 124 = 18,000. These outcomes occurred with a total direct cost across Meta, Google, Broadcast2World, AYTM, and Pathfinder Consulting of approximately $58,000. Therefore, the cost per United States person saved from death is likely under $10.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n        <\/div>\n<!--\/themify_builder_content-->","protected":false},"excerpt":{"rendered":"<p>Track: INFORMS Prizes &#8211; IAAA Finalists<\/p>\n","protected":false},"author":1001094,"featured_media":0,"parent":712,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"class_list":["post-5135","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) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>IAAA Finalists - 2024 INFORMS Analytics Conference<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/speakers\/iaaa-finalists\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"IAAA Finalists\" \/>\n<meta property=\"og:description\" content=\"Track: INFORMS Prizes - IAAA Finalists\" \/>\n<meta property=\"og:url\" content=\"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/speakers\/iaaa-finalists\/\" \/>\n<meta property=\"og:site_name\" content=\"2024 INFORMS Analytics Conference\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/INFORMSpage\/\" \/>\n<meta property=\"article:modified_time\" content=\"2024-02-15T15:51:22+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/files\/2023\/11\/analytics-2024-logo.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"414\" \/>\n\t<meta property=\"og:image:height\" content=\"414\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@2023_Analytics\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/speakers\/iaaa-finalists\/\",\"url\":\"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/speakers\/iaaa-finalists\/\",\"name\":\"IAAA Finalists - 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IAAA Finalists<\/h6>\n<p>The IAAA Finalists will present at the conference on Tuesday, April 16 in the INFORMS Prizes Track from 9:10am \u2013 12:20pm EST. Judges will then review the rank and announce the winner at the INFORMS Analytics Society luncheon.<\/p>\n<ul><li><h4>Amplifying Marketing Impact: Optimizing Network TV Advertising Inventory with Advanced Analytics<\/h4><p>Sebastian Souyris, Lally School of Management, Rensselaer Polytechnic Institute<br>Sridhar Seshadri, Gies College of Business, University of Illinois Urbana-Champaign<br>Dana Popescu, Darden Business School, University of Virginia<br>Vinod Kumawat, RSG Media<br>Anand Punjabi, RSG Media<br>Shiv Sehgal, RSG Media<br>Divyani Sharma, RSG Media<br>Varun Verma, RSG Media<\/p> <p>Promotional advertisements are a fundamental marketing instrument for television networks, enabling them to foster audience engagement for upcoming television programs. The efficient planning of such promotional material is crucial for a show's success, generating revenue opportunities for networks via commercial advertising. Traditionally, networks have employed manual processes for devising multiple promotional campaigns; however, these methods are labor-intensive, time-consuming, and inefficient.<br>RSG Audience Marketing Prophet application optimizes and automates the promotional planning process for networks, ensuring each campaign attains its desired outcomes while adhering to the constraints imposed by a limited marketing inventory and network conditions. To address this challenge, we have developed an innovative approach that synergizes mathematical programming and machine learning techniques to produce comprehensive long-term promotional campaign strategies and daily promotional schedules. These plans and schedules are of high quality, as evidenced by conventional business metrics and a minimal integer programming gap. Using our methodology, 24 TV cable and one broadcasting network (Paramount Global and A&amp;E Networks) have observed a 5% to 17% increment in marketing inventory for commercial advertising.<\/p><\/li><\/ul>\n<ul><li><h4>Battling Nursing Shortage Crisis: Building Innovative Staff Deployment Solutions with Human Expertise and Machine Learning<\/h4><p>Pengyi Shi, Associate Professor of Operations Management, Mitchell E. Daniels, Jr. School of Business, Purdue University<br>Jonathan E. Helm, Professor, Operations &amp; Decision Technologies, Kelley School of Business, Indiana University; Industry Partner and Contact: Indiana University Health<br>Mary Drewes, Vice President, Associate Chief Nurse Executive, Operations<br>Jacob Cecil, Data Analyst, Workforce Central<br>William Tindall, Director System Resource Center and Staffing Optimization<br>Troy Tinsley, Regional Director of Supply Chain at Riley Children\u2019s Health. (Former) Director of Workforce Strategy and Operational Excellence<\/p> <p>We partnered with the largest health system in Indiana, IU Health (IUH), to co-develop and implement an advanced analytics-driven decision support solution (DSS) to support their novel practice of mobilizing resource nurses across its 16 hospitals. The aim is to reduce staffing shortage, improve patient care, reduce harm events, and increase retention for nurses through improved working conditions. Our DSS combines state-of-the-art generative AI models with multi-stage reinforcement learning, creating a framework that handles both forecasting and decision optimization in environments with complex spatial-temporal correlations. The DSS provides multi-tiered recommendations, including resource nurse on-call lists, real-time redeployment between hospitals, and future hiring decisions. We conducted a pilot from May to June 2023, which produced a remarkable 17% reduction in understaffing, with estimated annual savings of $2.5-3.5 million in IUH alone, with over $1.5 billion projected savings if adopted on a national scale.<\/p><\/li><\/ul>\n<ul><li><h4>Interdependencies and Cascading Effects of Disasters on Critical Infrastructures<\/h4><p>Eva K Lee, PhD, Director, Whitaker-NSF Center for Operations Research in Medicine and HealthCare;<br>Chief Scientific Officer, the Data and Analytics Innovation Institute; Senior Data Scientist, AccuHealth<br>Technologies; Subject Matter Expert, DHS Medical &amp; Public Health Information Sharing Environment.<br>William Wang, PhD, Georgia Institute of Technology; Research Scientist, Amazon.<br>Taylor Leonard, PhD, Lieutenant Colonel, United States Air Force; Chief, Strike Branch, Department of<br>the Air Force, Office of Studies and Analysis.<br>Jerry Booker, Former Director, Enterprise Risk &amp;. Performance, TSA\u2013Strategy, Policy Coordination, and<br>Innovation.<\/p> <p>Critical Infrastructure (CI) is vital for a modern economy, and ensuring its security is crucial. The potential incapacitation or destruction of CI could have severe consequences for national security, the economy, public health, and safety. This project specifically examines the primary effects of cyber-attacks on critical infrastructure, investigating hidden vulnerabilities, cyber-physical disruptions, and their cascading impacts. Our goal is to develop analytical methods that provide valuable insights for policymakers, aiding in informed decision-making. Our accomplishments include (a) establishing the first influence system network model to reveal vulnerabilities with maximum cascading effects in communication critical infrastructure, utilizing scalable computational advances to model the entire U.S. communication network; (b) creating a multi-layer influence network incorporating interdependencies across various CI sectors; and (c) proving theoretical results about the monotonicity and submodularity of the influence function, with implications for computational complexity. The analytic tools and computational framework enhance the capabilities of cybersecurity and infrastructure security leaders, providing improved assessment for decision-making. This contributes to better system analysis, content relevancy, and long-term objectives of assisting decision-makers in assessing disruptions, establishing policies, and designing effective mitigation strategies. Identifying critical nodes enhances both defense and offense strategies, optimizing resource allocation for maximum protection.<\/p><\/li><\/ul>\n<ul><li><h4>Optimizing Milk Pools at the Rogers Hixon Ontario Human Milk Bank<\/h4><p>Timothy C. Y. Chan, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada<br>Rafid Mahmood, Telfer School of Management, University of Ottawa, Ottawa, ON, Canada<br>Deborah L O\u2019Connor, Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada<br>Debbie Stone, Rogers Hixon Ontario Human Milk Bank, Toronto, ON, Canada<br>Sharon Unger, Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada<br>Rachel K. Wong, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada<br>Ian Yihang Zhu NUS Business School, National University of Singapore, Singapore<\/p> <p>Human donor milk provides critical nutrition for millions of infants that are born preterm each year. Donor milk is collected, processed, and distributed by milk banks that pool donations together to improve the overall macronutrient content. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content in donations, which means pooling is done heuristically. We collaborated with the Rogers Hixon Ontario Human Milk Bank (RHOHMB) to create a data-driven milk pooling optimization framework via predictive and prescriptive modeling. We implemented this framework over a year-long trial and observed that pools created by our approach met clinical macronutrient targets approximately 31% more often than the previous nurse-led approach, while taking 60% less recipe creation time. This is the first work in the broader blending literature that combines machine learning and optimization. Our results demonstrate that such pipelines are feasible to implement in a healthcare setting and can yield significant improvements over current practices.<\/p><\/li><\/ul>\n<ul><li><h4>Policy Learning Utilizing Counterfactuals with an Application to the Dynamic Pricing of Airline Ancillaries<\/h4><p>Shivaram Subramanian, IBM Thomas J. Watson Research Ctr. <br>Wei Sun, IBM Thomas J. Watson Research Ctr. <br>Markus Ettl, IBM Thomas J. Watson Research Ctr. <br>Youssef Drissi, IBM Thomas J. Watson Research Ctr. <br>Zhengliang Xue, IBM Thomas J. Watson Research Ctr.<\/p> <p>Recent advances in AI have encouraged organizations to implement data-driven prescriptive analytics to automate and improve their decision-making capabilities. Such prescriptive policies must satisfy a variety of operational and fairness constraints that are ubiquitous in practice. A simple and interpretable policy is preferable as they can be easily verified and integrated into existing systems. Prior literature focused on constructing variants of prescriptive decision trees that are unable to satisfy such constraints. Our application employs a novel causal-teacher prescriptive-student framework to optimize a constrained prescriptive policy generation problem. We employ a counterfactual estimator that allows the application to be agnostic to the choice of the prediction methods, and deployable across diverse domains. We solve a novel path-based mixed-integer program (MIP) using column generation to derive an optimal policy that represents a multiway-split decision tree. We demonstrate the efficacy of our application by partnering with a global airline carrier to successfully live-test pricing policies for ancillary products.<\/p><\/li><\/ul>\n<ul><li><h4>Using Innovative Analytics on Two Levels to Increase Vaccination Rates<\/h4><p>Theodore T. Allen, Ph.D. FactSpread Columbus, Ohio; The Ohio State University, Integrated Systems Engineering, Columbus, Ohio.<br>Antor Rashid, formerly FactSpread Columbus, Ohio; The Ohio State University, Integrated Systems Engineering, Columbus, Ohio.<br>Long Wang, Ph.D. FactSpread Columbus, Ohio.<\/p> <p>Our vaccination-related advertisements reached approximately 1 million people (3.9 million impressions) across over eight states. Our analytics-based approach was innovative partly because we prepared analytical information and presented it to millions of people. We also used analytical approaches to design our messages, target the campaigns, and measure the results. Optimization was used to cluster similar counties. Then, optimal experimental design and regression analyses were applied to tune the advertisement selections and policy parameters. A second, more major campaign likely benefited from the improved parameters. Estimates of people caused to become vaccinated include 0.7M over one year (p-value 0.001). Another estimate including information contagion effects is 2.2M (p-value 0.002). Therefore, estimates of the lives saved include 0.7M \u00f7 124 = 6,000 and 2.2M \u00f7 124 = 18,000. These outcomes occurred with a total direct cost across Meta, Google, Broadcast2World, AYTM, and Pathfinder Consulting of approximately $58,000. Therefore, the cost per United States person saved from death is likely under $10.<\/p><\/li><\/ul>","_links":{"self":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/5135","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/users\/1001094"}],"replies":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/comments?post=5135"}],"version-history":[{"count":17,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/5135\/revisions"}],"predecessor-version":[{"id":5171,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/5135\/revisions\/5171"}],"up":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/712"}],"wp:attachment":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/media?parent=5135"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}