{"id":4365,"date":"2016-12-29T19:56:34","date_gmt":"2016-12-29T19:56:34","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/meetings2-2\/?page_id=4365"},"modified":"2025-07-17T16:55:45","modified_gmt":"2025-07-17T16:55:45","slug":"tracks","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/","title":{"rendered":"Tracks"},"content":{"rendered":"<!--themify_builder_content-->\n<div id=\"themify_builder_content-4365\" data-postid=\"4365\" class=\"themify_builder_content themify_builder_content-4365 themify_builder tf_clear\">\n                    <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_nd84357 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_f5cl359 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_3 tb_6gcg742\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col3-1 tb_9ax8743 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_35m924   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <ul>\n<li><a href=\"#advTut\"><b>Advanced Tutorials<\/b><\/a><\/li>\n<li><a href=\"#abSim\"><b>Agent-Based Simulation<\/b><\/a><\/li>\n<li><a href=\"#adMeth\"><b>Analysis Methodology<\/b><\/a><\/li>\n<li><a href=\"#aviation\"><b>Aviation Modeling &amp; Analysis<\/b><\/a><\/li>\n<li><a href=\"#caseStudies\"><b>Commercial Case Studies<\/b><\/a><\/li>\n<li><b><a href=\"#complex\">Complex &amp; Generative Systems<\/a><\/b><\/li>\n<li><a href=\"#datascience\"><strong>Data Science &amp; Simulation<\/strong><\/a><\/li>\n<li><a href=\"#envSusRisk\"><b>Environment, Sustainability &amp; Resilience<\/b><\/a><\/li>\n<li><strong><a href=\"#healthcare\">Healthcare &amp; Life Sciences<\/a><\/strong><\/li>\n<li><a href=\"#hybrid\"><strong>Hybrid Modeling &amp; Simulation<\/strong><\/a><\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col3-1 tb_hwqj744\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_9qzj840   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <ul>\n<li><strong><a href=\"#introTutorial\">Introductory Tutorials<\/a><\/strong><\/li>\n<li><strong><a href=\"#supplyChain\">Logistics, Supply Chain &amp; Transportation<\/a><\/strong><\/li>\n<li><strong><a href=\"#manuApp\">Manufacturing &amp; Industry 4.0<\/a><\/strong><\/li>\n<li><strong><a href=\"#militaryApp\">Military &amp; National Security<\/a><\/strong><\/li>\n<li><strong><a href=\"#masm\">Modeling &amp; Analysis of Semiconductor Manufacturing (MASM)<\/a><\/strong><\/li>\n<li><strong><a href=\"#modelmethodology\">Modeling Methodology<\/a><\/strong><\/li>\n<li><strong><a href=\"#phd\">PhD Colloquium<\/a><\/strong><\/li>\n<li><strong><a href=\"#poster\">Poster Session<\/a><\/strong><\/li>\n<li><strong><a href=\"#profDev\">Professional Development<\/a><\/strong><\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col3-1 tb_xu1q744 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_fxxw635   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <ul>\n<li><strong><a href=\"#projMgmt\">Project Management &amp; Construction<\/a><\/strong><\/li>\n<li><strong><a href=\"#reliability-modeling\">Reliability Modeling &amp; Simulation<\/a><\/strong><\/li>\n<li><strong><a href=\"#scienceApp\">Scientific AI &amp; Applications<\/a><\/strong><\/li>\n<li><strong><a href=\"#satw\">Simulation Around the World<\/a><\/strong><\/li>\n<li><strong><a href=\"#simai\">Simulation &amp; Artificial Intelligence<\/a><\/strong><\/li>\n<li><strong><a href=\"#simdigtwin\">Simulation as Digital Twin<\/a><\/strong><\/li>\n<li><strong><a href=\"#simed\">Simulation in Education<\/a><\/strong><\/li>\n<li><a href=\"#simspace\"><strong>Simulation in Space (NEW)<\/strong><\/a><\/li>\n<li><strong><a href=\"#simOptimize\">Simulation Optimization<\/a><\/strong><\/li>\n<li><strong><a href=\"#UncertaintyRobustSim\">Uncertainty Quantification &amp; Robust Simulation<\/a><\/strong><\/li>\n<li><strong><a href=\"#vendor\">Vendor<\/a><\/strong><\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"advTut\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-advTut tb_huzy263 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_cm3q265 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_h7zk928   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Advanced Tutorials<\/h3>\n<p><b>Track Coordinators: <\/b>Javier Faulin (Public University of Navarre), Giulia Pedrielli (Arizona State University)<\/p>\n<p>The Advanced Tutorial track is oriented toward experienced practitioners and researchers who want to hear about the most recent developments, presented in a directly applicable form. The track encourages tutorials that focus on topics of special interest, as well as the latest theory and methods and resulting modeling, simulation, and analysis tools. Also of interest are pertinent topics in related disciplines, such as social network, healthcare, epidemic disease, energy, emergency response, augmented and virtual reality, simulation of big-data, blockchain, and so on. These special-focus sessions give practitioners and researchers a survey of recent fundamental advances in the discipline of modeling and simulation.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"abSim\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-abSim tb_mtza945 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_8v4d945 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_cqms129   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Agent-Based Simulation<\/h3>\n<p><b>Track Coordinators: <\/b>Andrew Collins (Old Dominion University), Martijn Mes (University of Twente)<\/p>\n<p>The track focuses on theoretical, methodological, and applied research in agent-based simulation (ABS) and multi-agent systems. Submissions to the ABS track should address advancements in agent-based simulation modeling, including topics such as high-level specification, execution platforms, modeling languages, validation techniques, output analysis, and experimental methods. The track also welcomes contributions that explore the integration of ABS with emerging topics like artificial intelligence (AI), analytics, and big data, as well as applications to adaptive or self-organizing systems. Authors applying ABS to fields covered by other tracks at the Winter Simulation Conference are encouraged to submit their work to the relevant application track.<\/p>\n<p>Any paper that includes the development of agent-based simulation must clearly discuss the underlying model &#8211; preferably using structured protocols like the ODD (Overview, Design concepts, and Details) framework &#8211; and include an evaluation of the simulation&#8217;s validation.<\/p>\n<p>Contributions of particular interest include research on intelligent agents, agent behavior modeling, analytics, and applications of ABS to less traditional ABS domains, such as the humanities and arts. The track also encourages submissions demonstrating how agents can serve as supportive tools to enhance simulation-based problem-solving processes.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"adMeth\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-adMeth tb_1o5t306 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_pc8k308 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_61jb545   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Analysis Methodology<\/h3>\n<p><b>Track Coordinators: <\/b>Dohyun Ahn (Chinese University of Hong Kong), Ben Feng (University of Waterloo), Eunhye Song (Georgia Institute of Technology)<\/p>\n<p>The Analysis Methodology track invites research contributions to mathematical and computational aspects of computer simulation. This may be a new problem formulation, estimation procedure, algorithm design, proof technique, and more. Combining state-of-the-art methodologies in other adjacent areas such as statistics, computational physics, applied mathematics, data science, and machine learning to push the research frontier of simulation is particularly welcome. Topics of interest include, but are not limited to:<\/p>\n<ul>\n<li>Simulation experiment design<\/li>\n<li>Input modeling and output analysis<\/li>\n<li>Risk and uncertainty quantification<\/li>\n<li>Sensitivity analysis<\/li>\n<li>Variance reduction techniques<\/li>\n<li>Rare-event simulation<\/li>\n<li>Improving algorithmic efficiency<\/li>\n<li>Metamodeling<\/li>\n<li>Simulation model validation and calibration<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"aviation\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-aviation tb_ylxg70 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_6nl771 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_du1b205   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Aviation Modeling and Analysis<\/h3>\n<p><b>Track Coordinators: <\/b>Miguel Mujica Mota (Amsterdam University of Applied Sciences), Michael Schultz (Bundeswehr University Munich), John Shortle (George Mason University)<\/p>\n<p>The world\u2019s air transportation system is preparing for an influx of new users with diverse needs, while simultaneously growing in its traditional areas. The Aviation Track aims to cover most of the important areas of the aviation industry where simulation alone or together with other techniques can provide solutions. Therefore, we invite researchers from research institutions, universities, airlines, air navigation service providers, and industry to submit original papers presenting results of their work.<\/p>\n<p>Areas of interest include, but are not limited to:<\/p>\n<ul>\n<li>Human-in-the-Loop simulations for training and for evaluating new technologies<\/li>\n<li>Airports<\/li>\n<li>Capacity &amp; efficiency improvement<\/li>\n<li>Airport capacity forecast<\/li>\n<li>Business intelligence for airports<\/li>\n<li>Multi-Airport Systems<\/li>\n<li>Small and regional airport development<\/li>\n<li>Airline operations<\/li>\n<li>Maintenance, Repair, and Overhaul and Lean MRO<\/li>\n<li>Optimization of operational processes or specific problems in aviation<\/li>\n<li>Air Traffic Management<\/li>\n<li>ATC\/AIRPORT systems<\/li>\n<li>Digital \/ remote tower operations<\/li>\n<li>Predictability of air transportation operations<\/li>\n<li>Unmanned airborne systems<\/li>\n<li>Trajectory modeling<\/li>\n<li>Safety of interactions with manned aviation<\/li>\n<li>Air traffic control concepts<\/li>\n<li>Development of incident investigation<\/li>\n<li>Environmental effects of aviation<\/li>\n<li>Cargo problems in aviation<\/li>\n<li>Multimodality where aviation is involved<\/li>\n<li>Economics of the air transportation system<\/li>\n<li>Communications, navigation, and surveillance systems<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"caseStudies\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-caseStudies tb_aw01397 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_ug0m397 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_8hqo216   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Commercial Case Studies<\/h3>\n<p><strong>Track Coordinator<\/strong>: Saurabh Parakh (MOSIMTEC)<\/p>\n<p>Click <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/commercial-case-studies\/\">here<\/a> for more information.\u00a0<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"complex\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-complex tb_im8d833 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_m8j8836 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_9yl837   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Complex and Generative Systems<\/h3>\n<p><b>Track Coordinators:<\/b><span style=\"font-weight: 400;\">\u00a0 Saurabh Mittal (MITRE Corporation), Claudia Szabo (University of Adelaide)<br><\/span><span style=\"background-color: initial; font-size: 0.95em;\"><br><\/span><\/p>\n<p>This track is focused on the modeling, simulation, and engineering of complex, intelligent, adaptive, and generative systems and how they handle faults, system issues, and emergent behaviors, and provide new capabilities. The increasing popularity of the Generative AI technologies emphasizes that GenAI-enabled will be integrated in all the facets of society. These new class of systems will need to be integrated with existing systems within a system of systems (SoS) context. This SoS may also incorporate adaptive and autonomous elements (such as systems that have different levels of autonomy and situated behavior). This makes design, analysis, and testing for the system-at-hand a complex endeavor itself.<\/p>\n<p>The system\u2019s behavior is also determined by the input, which evolves from the dynamic environment. This exact factor is difficult to predict, due to an ever-increasing level of autonomy and complexity. Advanced Modeling and Simulation (M&amp;S) frameworks are required to facilitate SoS design, development, testing, and integration. In more particular, these frameworks must provide methods to deal with intelligent, emergent, autonomous, adaptive and generative behavior.<\/p>\n<p>The subject of emergent behavior and M&amp;S of emergent behaviors takes the center stage in such systems as it is unknown how a system responds in the face of emergent behavior arising out of interactions with other complex systems. The generative aspect integrated from GenAI technology impacting the behavior space of the SoS introduce further uncertainty that must be quantified. Intelligent behavior is also defined as an emergent property in some complex systems. Consequently, systems that respond and adapt to such behaviors may be called intelligent systems as well. With integrated generative technologies, such as Large Language Models (LLMs) and the associated vast amounts of corpus of data, the community is already pursuing Artificial Generative Intelligence (AGI). This track has two objectives.<\/p>\n<p>The first objective aims to focus on M&amp;S of the following aspects of complex SoS engineering with a focus on generative and intelligent systems, and brings researchers, developers and industry practitioners working in the areas of complex, generative and autonomous SoS engineering. This objective covers the following topics:<\/p>\n<ul>\n<li>Theory for complex, intelligence-based, adaptive and generative systems<\/li>\n<li>Computational intelligence and cognitive systems<\/li>\n<li>Human-in-the-loop systems and Human-on-the-loop systems<\/li>\n<li>M&amp;S Frameworks for intelligent and generative behavior<\/li>\n<li>Methodologies, tools, and architectures for adaptive generative systems<\/li>\n<li>Knowledge engineering, generation, and management<\/li>\n<li>Weak and Strong emergent behavior, Emergent Engineering<\/li>\n<li>Complex adaptive systems engineering employing LLMs<\/li>\n<li>Self-* (organization, generative, explanation, configuration) capability and collaborative behavior<\/li>\n<li>Applications to robotics, unmanned vehicles systems, swarm technology, semantic web technology, and multi-agent systems<\/li>\n<li>Live, Virtual and Constructive (LVC) environments<\/li>\n<li>Modeling, engineering, testing and verification of complex and generative behavior<\/li>\n<li>Modeling, simulating, and testing generative applications<\/li>\n<\/ul>\n<p>The second objective is to incorporate Complexity Science in simulation models. Complexity is a multi-level phenomenon that exists at structural, behavioral and knowledge levels in such SoS. Emergent behavior is an outcome of this complexity. Understanding emergent behavior as an outcome of this complexity will provide a foundation for generative intelligent systems, and the M&amp;S thereof. Topics related to this objective include, but are not limited to handling of:<\/p>\n<ul>\n<li>Complexity in Structure: network, hierarchical, small-world, flat, etc.<\/li>\n<li>Complexity in Behavior: Micro and macro behaviors, local and global behaviors, teleologic and epistemological behaviors<\/li>\n<li>Complexity in Knowledge: ontology design, ontology-driven modeling, ontology-evaluation, ontology transformation, etc.<\/li>\n<li>Complexity in Human-in-the-loop: artificial agents, cognitive agents, multi-agents, man-in-loop, human-computer-interaction<\/li>\n<li>Complexity in Human-on-the-loop: trust modeling, human-machine-interaction<\/li>\n<li>Complexity in intelligence-based systems: Situated behavior, knowledge-based behavior, mnemonic behavior, resource-constrained systems, energy-aware systems<\/li>\n<li>Complexity in adaptation and autonomy<\/li>\n<li>Complexity in architecture: Flat, full-mesh, hierarchical, adaptive, swarm, transformative<\/li>\n<li>Complexity in awareness: Self-* (organization, generative, explanation, configuration)<\/li>\n<li>Complexity in interactions: collaboration, negotiation, greedy, rule-based, environment-based, etc.<\/li>\n<li>Complexity in LVC environments<\/li>\n<li>Complexity in artificial systems, social systems, techno-economic-social systems<\/li>\n<li>Complexity in model engineering of complex SoS employing LLMs<\/li>\n<li>Complexity in model specification using modeling languages and architecture frameworks such as UML, PetriNets, SysML, DoDAF, MoDAF, UAF, etc.<\/li>\n<li>Complexity in simulation infrastructure engineering: distributed simulation, parallel simulation, cloud simulation, netcentric parallel distributed environments<\/li>\n<li>Complexity in Testing and Evaluation (T&amp;E) tools for SoS engineering<\/li>\n<li>Complexity in Heterogeneity: Hardware\/Software Co-design, Hardware in the Loop, Cyber-Physical Systems, the Internet of Things<\/li>\n<li>Metrics for Complexity design and evaluation<\/li>\n<li>Complexity in verification, validation, and accreditation in SoS<\/li>\n<li>Complexity of Application in domain model engineering: Financial, Power, Robotics, Swarm, Economic, Policy, etc.<\/li>\n<li>Complexity in SoS failure<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"datascience\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-datascience tb_njeo968 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_qbk1969 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_i6ab14   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Data Science and Simulation<\/h3>\n<p><b>Track Coordinators: <\/b>Abdolreza Abhari (Toronto Metropolitan University), Cheng-Bang Chen (University of Miami), Niclas Feldkamp (TU Ilmenau)<\/p>\n<p>The Data Science and Simulation track aims to promote novel contributions in the use and generation of big data within simulations as well as using data science to aid understanding of simulation results. This track welcomes all methodological, technical, and application area-focused contributions that advance the modeling and simulation body of knowledge. Some topics of interest include:<\/p>\n<ul>\n<li>Big Data in simulation<\/li>\n<li>Data analytics for simulation<\/li>\n<li>Machine learning methods to generate synthetic data<\/li>\n<li>Simulating massive data processing systems<\/li>\n<li>Digital Twin Simulations<\/li>\n<li>Machine learning and data mining in modeling and simulation<\/li>\n<li>Data-Driven techniques for modeling and simulation<\/li>\n<li>Deep learning in modeling and simulation<\/li>\n<li>Hybrid approaches of combining simulation and machine Learning<\/li>\n<li>Simulation initialization techniques using big data<\/li>\n<li>Big data management\/processing techniques for simulations<\/li>\n<li>Ontologies for big data modeling<\/li>\n<li>Data science pipelines for modeling for simulations<\/li>\n<li>Data science and social networks in simulation<\/li>\n<li>Simulation of cloud computing and distributed systems<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"envSusRisk\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-envSusRisk tb_zw2i511 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_ctfd512 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_ppsn803   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Environment, Sustainability, and Resilience<\/h3>\n<p><b>Track Coordinators: <\/b>Jiaqi Ge (University of Leeds), Shima Mohebbi (George Mason University)<\/p>\n<p>The Environment, Sustainability, and Resilience track focuses on the theory and application of simulation and modeling to derive innovative and resilient solutions to environmental and sustainability challenges. Application areas include infrastructure systems, cyber-physical systems, ecological systems, renewable resources, transportation, buildings, farming, manufacturing, and urban\/city science. We solicit papers presenting new ideas, concepts, models, methods, tools, standards, and applications to achieve sustainability and resiliency in natural and built environments. Possible topics include, but are not limited to:<\/p>\n<ul>\n<li>Decision support and analytics for sustainability<\/li>\n<li>Smart, connected, and resilient infrastructure<\/li>\n<li>Social dynamics and policy innovations of managing sustainable infrastructure systems<\/li>\n<li>Simulation and network modeling for resilience assessment<\/li>\n<li>Environmental modelling, visualization, and optimization<\/li>\n<li>Renewable resources and related processes<\/li>\n<li>Smart building systems and robust design<\/li>\n<li>Energy\/resource-efficient manufacturing<\/li>\n<li>Smart and resilient grids<\/li>\n<li>Information modeling and interoperability<\/li>\n<li>Energy-efficient and sustainable urban planning and design<\/li>\n<li>Intelligent transportation systems<\/li>\n<li>Human-environment interaction<\/li>\n<li>Ecological systems<\/li>\n<li>Compound hazards and their impact on communities and engineered systems<\/li>\n<li>Environmental risk assessment and mitigation<\/li>\n<li>Human adaptation to climate<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"healthcare\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-healthcare tb_o8sn802 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_ex24802 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_y2gb680   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Healthcare and Life Sciences<\/h3>\n<p><b>Track Coordinators: <\/b>Tugce Martagan (Eindhoven University of Technology), Varun Ramamohan (Indian Institute of Technology Delhi)<\/p>\n<p>The Healthcare and Life Sciences track addresses important areas in which simulation can provide critical decision support for operational and strategic planning and decision making that individual providers (doctors\/nurses, clinics, urgent care centers, hospitals) face, as well as for policy issues that must be addressed by administering systems (e.g., hospitals, insurance companies, and governments). Traditionally, this track has been broad in focus, incorporating Discrete Event Simulation, System Dynamics, Agent-Based Simulation, and\/or Monte Carlo simulations, with a variety of applications. A common thread is the use of simulation tools to provide insight into, or to inform, decisions for improved healthcare performance measures and outcomes. New modeling tools that address challenges with the conceptualization or implementation of healthcare systems, and general healthcare simulations are welcome. Topics include, but are not limited to, the following:<\/p>\n<ul>\n<li>Admissions and control<\/li>\n<li>Ancillary services<\/li>\n<li>Appointment scheduling<\/li>\n<li>Emergency room access<\/li>\n<li>Epidemic and pandemic modeling<\/li>\n<li>General healthcare simulation<\/li>\n<li>Global Health<\/li>\n<li>Healthcare optimization<\/li>\n<li>Healthcare systems<\/li>\n<li>Medical decision making<\/li>\n<li>Outpatient access<\/li>\n<li>Outpatient capacity analysis<\/li>\n<li>Payment\/Payer models<\/li>\n<li>Performance improvement models<\/li>\n<li>Pricing models<\/li>\n<li>Resource scheduling (e.g., nurse, doctor, anaesthesiologist, residents, equipment, etc.)<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"hybrid\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-hybrid tb_dk7o706 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_u8id707 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_54m0380   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Hybrid Modeling &amp; Simulation<\/h3>\n<p><strong>Track Coordinators: <\/strong>Masoud Fakhimi (University of Surrey), Nav Mustafee (University of Exeter)<\/p>\n<p>The Hybrid Modeling and Simulation track welcomes submissions from authors whose research necessitated the overcoming of limitations of using a single simulation approach by employing hybrid simulation (HS) formulation consisting of combinations of discrete-event simulation (DES), agent-based modeling (ABM), system dynamics (SD), and Monte Carlo simulation. We also welcome papers that have considered the integration of simulation techniques (as above) with research paradigms, frameworks and methodologies, techniques, tools and methods of enquiry from a wider plethora of techniques used in Operations Research (e.g., qualitative system dynamics and soft system methodologies with discrete-event simulation; analytical modeling with computer simulation; DEA and MCDA with DES), as also disciplines wider than only M&amp;S and OR, such as computer science and applied computing, system design, data science and AI, economics, psychology and humanities. Such cross-disciplinary hybrid models (HM) may include work that has combined ABM, DES and SD, or indeed a hybrid simulation, with design science (e.g., use of design and platform thinking in the conceptualization of a simulation study), systems engineering and automation (e.g., digital twins integrated with data acquisition systems), machine learning and AI (e.g., use of reinforcement learning feedback with simulation), generative AI (e.g., use of LLMs\/ChatGPT for better comprehension of simulation output), etc.<\/p>\n<p>In summary, the hybrid simulation and modeling track welcomes papers that have combined simulation techniques such as ABS, DES and SD (also referred to as hybrid simulation &#8211; HS) OR have used simulation with one or more techniques from disciplines outside M&amp;S (referred to as hybrid modeling &#8211; HM), thereby providing greater synergy for the solution and deeper insights into the problem, compared to what can be achieved through the application of single techniques.<\/p>\n<p>Topics include, but are not limited to, the following:<\/p>\n<ul>\n<li>Literature reviews in hybrid simulation (HS) and hybrid modeling (HM)<\/li>\n<li>Taxonomies and conceptualizations of HS and HM<\/li>\n<li>Methodology focusing on the development of frameworks and modeling formalisms to support hybrid M&amp;S<\/li>\n<li>Case studies on hybrid M&amp;S in domains such as healthcare, manufacturing, supply chain, circular economy and sustainability, defense and policy.<\/li>\n<li>Technical papers on the development of software artefacts for supporting hybrid M&amp;S.<\/li>\n<li>Papers on validation and verification of hybrid models.<\/li>\n<li>Application of hybrid simulation in behavioral studies (commonly referred to as Behavioral Operations Research).<\/li>\n<li>Digital twins whose implementation includes multiple computing artefacts, e.g., simulation with machine learning, or which requires integration with real-time systems using approaches commonly found in industrial engineering, computer science and software engineering.<\/li>\n<li>Hybrid models that include both data-driven learning (e.g., supervised and unsupervised learning, reinforcement learning, process mining) with computer simulation.<\/li>\n<li>Hybrid models that use AI and LLMs in one or more stages of a simulation study (e.g., in developing the conceptual model, code generation for open-source simulation software, and improving the comprehension of simulation outputs for the stakeholders)<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"introTutorial\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-introTutorial tb_2mz1282 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_xsf2284 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_trvu215   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Introductory Tutorials<\/h3>\n<p><b>Track Coordinators:<\/b> Chang-Han Rhee (Northwestern University),\u00a0Antuela Tako (Nottingham Trent University)<\/p>\n<p>The Introductory Tutorials track is oriented toward professionals in modeling and simulation interested in broadening, refreshing, and expanding their knowledge of the field. The track covers a wide range of topics including mathematical and statistical foundations, methods, application areas, software tools, and analysis tools. In addition, the track also encourages tutorials that provide an accessible introduction to emerging research topics in simulation.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"supplyChain\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-supplyChain tb_wii1141 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_li5n144 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_5arc188   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Logistics, Supply Chain Management, Transportation<\/h3>\n<p><b>Track Coordinators: <\/b>Montasir Abbas (Virginia Tech), Majsa Ammouriova (German Jordanian University), David Goldsman (Georgia Institute of Technology), Markus Rabe (Technische Universit\u00e4t Dortmund)<\/p>\n<p>The nature of today\u2019s highly dynamic, stochastic, and complex networks of supply, intralogistics, and distribution has led to decreasing transparency of enterprise processes, while at the same time increasing failure risks. These issues also apply for deliveries to the end customer, e.g., at collection points or at home delivery. In addition, pollution and climate change have created a demand for new concepts in urban logistics as well as careful planning in this uncertain field. Therefore, managers who are responsible for supply chain management and logistics require effective tools to provide credible analysis in this challenging environment. In order to facilitate the discussion of the best applications of simulation in this timely area, the LSCT track includes papers in logistics simulation, supply chain simulation, and simulation for planning, analyzing, and improving transportation in a wide scope encompassing topics from the detailed intralogistics level up to global supply chains. Topics of interest include, but are not limited to, the following:<\/p>\n<ul>\n<li>Green supply chain design<\/li>\n<li>Supply chain resilience<\/li>\n<li>Supply chain risk analysis<\/li>\n<li>Simulation-based optimization of supply chains<\/li>\n<li>Supply chain operations<\/li>\n<li>Demand and order fulfillment<\/li>\n<li>Inventory policies<\/li>\n<li>Warehouse management<\/li>\n<li>Intralogistics and advanced material flow systems<\/li>\n<li>Omni-channel logistics<\/li>\n<li>Online purchase delivery concepts<\/li>\n<li>Urban transport<\/li>\n<li>Last-mile logistics<\/li>\n<li>Crowdshipping<\/li>\n<li>Multi-modal logistics systems<\/li>\n<li>Port operations<\/li>\n<li>Rail operations<\/li>\n<li>Road networks and traffic management<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"manuApp\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-manuApp tb_rauk542 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_ugrn544 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_ite2813   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Manufacturing &amp; Industry 4.0<\/h3>\n<p><b>Track Coordinators: <\/b>Alp Akcay (Eindhoven University of Technology), Christoph Laroque (University of Applied Sciences Zwickau), Gordon Shao (NIST)<\/p>\n<p>Simulation is the model-based methodology for analyzing dynamical inter-dependencies in manufacturing systems. The Manufacturing Applications track is interested in research work using simulation in industrial application areas as found in the automotive, aircraft and shipbuilding industries, among others. Manufacturing applications relate to the model-based analysis of (i) all production and logistics processes within a company or along a supply chain, and (ii) all phases of a system life cycle, such as system acquisition, system design and planning, implementation, start of operation and ramp-up, as well as the operations itself.<\/p>\n<p>A contribution shall describe the aims of investigation, the investigated system, the simulation model, the experimental plan, the findings, and any implementation results. Additionally, specific challenges like system complexity, data collection and preparation, or verification and validation may be pointed out. Topics include, but are not limited to, the following:<\/p>\n<ul>\n<li>Manufacturing<\/li>\n<li>Applications of simulation-based optimization in production<\/li>\n<li>Cyber-physical systems, Industry 4.0 \/ Industry 5.0<\/li>\n<li>Production planning and scheduling<\/li>\n<li>Lean management<\/li>\n<li>Total Quality Management<\/li>\n<li>Maintenance and Lifecycle-Assessment<\/li>\n<li>Integration of energy and carbon footprint<\/li>\n<li>Digital Production Twins<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"militaryApp\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-militaryApp tb_r5eb836 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_s0h2838 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_ypp9584   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Military and National Security\u00a0<\/h3>\n<p><b>Track Coordinators: <\/b>Lance Champaign (Air Force Institute of Technology), Jamie Grymes (U.S. Military Academy)<\/p>\n<p>The Military and National Security Applications Track is interested in papers that describe the application of modeling and simulation theory, techniques, tools and technologies to challenges in the military and national security domain. Example application areas include: battle management command and control, air and missile defense, campaign analysis, weapon-target pairing, multi-domain operations, sustainment operations, operational testing and evaluation, wargaming and assessments, CBRNE defense, critical infrastructure analysis, homeland defense and domestic civil support operations, cybersecurity, information operations, electronic warfare, intelligence, surveillance and reconnaissance, medical and healthcare operations, manpower and personnel, readiness and training, cost, risk and decision analysis, special operations, etc.<\/p>\n<p>Topics of special interest include, but are not limited to, challenges and innovations for representation and implementation of command, control and communications, swarm intelligence, cybersecurity operations, cyber threat intelligence, social media analytics, hardware-in-the-loop simulations, human-machine teaming, future platforms and weapons prototyping, synthetic environments, multi-sensor fusion, complex behaviors of semi-automated forces, electronic warfare, expeditionary medical operations, automatic scenario planning and experimentation, and multi-resolution models. Papers investigating an innovative use of edge\/fog\/cloud technologies, gaming technology, mixed reality technology, artificial intelligence and machine learning technology, big data technologies, distributed computing technology, and networking technology for military and national security applications are also welcome!<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"masm\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-masm tb_krff94 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_djcu96 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_3m85207   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Modeling &amp; Analysis of Semiconductor Manufacturing (MASM)<\/h3>\n<p><b>Track Coordinators:\u00a0 <\/b>John W. Fowler (Arizona State University), Lars M\u00f6nch (University of Hagen)<br><br>Click <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/masm\/\">here<\/a> for more information.\u00a0<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"modelmethodology\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-modelmethodology tb_hsiy389 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_xpdk391 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_gjte3   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Modeling Methodology<\/h3>\n<p><b>Track Coordinators: <\/b>Rodrigo Castro (University of Buenos Aires), Andrea D\u2019Ambrogio (University of Rome Tor Vergata), Gabriel Wainer (Carleton University)<\/p>\n<p>The Modeling Methodology track is interested in methodological advances with respect to the theory and practice of modeling and simulation. These may include approaches to model development, model building, verification, validation, experimentation, and optimization. Contributions to the advancement of the technology and the software used to support modeling are also welcome as are contributions featuring guiding or unifying frameworks, the development and application of meaningful formal methods, and lessons learned. If you have an idea for a special session or a panel discussion of particular interest to the WSC participants, please send an email with a short description and references to the work of relevant experts to the track chairs. Topics of interest include, but are not limited to, the following:<\/p>\n<ul>\n<li>Modeling paradigms<\/li>\n<li>Formal modeling languages<\/li>\n<li>Modeling approaches for real-time systems<\/li>\n<li>Technological advances in modeling software<\/li>\n<li>Spatial and temporal modeling<\/li>\n<li>Multilevel modeling<\/li>\n<li>Multi-paradigm modeling<\/li>\n<li>Multi-formalism modeling<\/li>\n<li>Model reuse, repositories, and retrieval<\/li>\n<li>Parallel and Distributed simulation<\/li>\n<li>Modeling with ontologies<\/li>\n<li>Semantic tools supporting modeling methods<\/li>\n<li>Standardization challenges<\/li>\n<li>Modeling and Simulation for Cyber-Physical Systems<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"phd\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-phd tb_xhhg583 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_3gqc584 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_d0yg584   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>PhD Colloquium<\/h3>\n<p><strong>Chair:<\/strong> Eunhye Song (Georgia Institute of Technology)<br><strong>Members<\/strong>: Cristina Ruiz Martin (Carleton University), Alison Harper (University of Exeter), Sara Shashaani (North Carolina State University)<\/p>\n<p>In 2025, ACM-SIGSIM and INFORMS-Sim will once again sponsor the PhD Colloquium for PhD students who are within two years of graduation (planning to graduate by December 2027). Students close to graduation will be given an opportunity to showcase their work during a short presentation session and a poster during the Colloquium (apart from the regular tracks). Presenting your PhD work to your peers and the larger simulation community will give you the opportunity to receive valuable feedback and ideas, as well as introduce you to a network that can be very helpful for your career once you graduate.<\/p>\n<p>Visit the <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/phd-colloquium\/\">PhD Colloquium page<\/a> for additional details.\u00a0<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"poster\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-poster tb_phde924 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_ipis926 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_mybq900   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Poster Session<\/h3>\n<p><b>Track Coordinators: <\/b>Le Khanh Ngan Nguyen (University of Strathclyde), Juan Leonardo Sarli (The National Technological University \u2013 Santa Fe Regional Faculty)<\/p>\n<p>The Poster Session offers a timely venue to present and discuss new modeling and simulation research through a forum encouraging graphical presentation, demonstration, and active engagement among Winter Simulation Conference (WSC) participants. We are seeking outstanding extended abstracts (2 pages) submissions to be presented in a poster format at the conference. Competitive contributions will present interesting recent results, novel ideas or works-in-progress that are not quite ready for a regular full-length paper. Contributions from PhD students are particularly welcome. Submitted manuscripts should follow the standard template for WSC submission, and should not exceed the 2 pages limit. Extended abstract submissions are encouraged in all areas of modeling and simulation covered by WSC.<\/p>\n<p>Please refer to the <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/poster-sessions\/\">Poster Session page<\/a> for more information.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"profDev\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-profDev tb_wu7c287 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_1185288 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_4540437   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Professional Development<\/h3>\n<p><b>Track Coordinators: <\/b>Caroline Krejci (The University of Texas at Arlington), Nav Mustafee (University of Exeter)<\/p>\n<p>In this track, we explore the transformative role of the application of simulation and how it influences the professional sphere, emphasizing its capacity to enhance skills in communication, leadership, problem-solving, and more, while also showcasing its application across industries. This track invites a diverse dialogue, focusing on graduate students, challenges for women and underrepresented groups, early-career academics, and academia-industry partnerships. Through this lens, we aim to highlight how simulation can transcend traditional boundaries, empowering professionals to not only envision but realize the vast potential simulation, fostering a future where inspiration and simulation drive groundbreaking innovation.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"projMgmt\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-projMgmt tb_y6bc395 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_gq8v396 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_vweo877   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Project Management and Construction<\/h3>\n<p><b>Track Coordinators: <\/b>Eric Du (University of Florida), Joseph Louis (Oregon State University)<\/p>\n<p>The Project Management and Construction track includes innovative research as well as practical application papers that apply computer simulation to complex project and construction management problems. Computer simulation encompasses a broad range of data-driven, quantitative methods including, but not limited to:<\/p>\n<ul>\n<li>Discrete event simulation<\/li>\n<li>Continuous simulation<\/li>\n<li>System dynamics<\/li>\n<li>Big data analytics<\/li>\n<li>Virtual\/Augmented reality<\/li>\n<li>Automation and robotics<\/li>\n<li>Emerging AI techniques<\/li>\n<\/ul>\n<p>Applications include, but are not limited to<\/p>\n<ul>\n<li>Complex project planning and scheduling<\/li>\n<li>Planning for integrated project delivery<\/li>\n<li>Construction safety planning<\/li>\n<li>Off-site production and modularization systems<\/li>\n<li>Site operations and layout planning<\/li>\n<li>Human behavior and organization modeling<\/li>\n<li>Sustainable built environment<\/li>\n<li>Simulation as a project management education tool<\/li>\n<li>Lean production systems<\/li>\n<li>Sensed environments for simulation<\/li>\n<li>Project portfolio management<\/li>\n<li>System optimization and control<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"reliability-modeling\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-reliability-modeling tb_961i499 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_4332499 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_o9cg499   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Reliability Modeling and Simulation<\/h3>\n<p><b>Track Coordinators: <\/b>Sanja Lazarova-Molnar (Karlsruhe Institute of Technology), Olufemi Omitaomu (Oak Ridge National Lab), Li Xueping (University of Tennessee-Knoxville)<\/p>\n<p>Complex real-time systems need to have their dependability and reliability evaluated and addressed to ensure that systems perform satisfactorily despite the presence of faults. Systems\u2019 design decisions are also influenced by these evaluations. Simulation has often been utilized for this purpose. This track covers the use of modeling and simulation for the analysis of reliability and dependability of systems. Further relevant topics are listed as follows:<\/p>\n<ul>\n<li>data-driven reliability modeling<\/li>\n<li>simulation for optimizing repair and maintenance strategies<\/li>\n<li>reliability models for hardware and software<\/li>\n<li>reliability modeling of cyber-physical systems<\/li>\n<li>modeling and simulation of fault-tolerant systems<\/li>\n<li>fault models and fault abstraction<\/li>\n<li>reliability modeling formalisms<\/li>\n<li>predictive maintenance<\/li>\n<li>dependability analysis using simulation and experimental measurement<\/li>\n<li>prognostics &amp; health management<\/li>\n<li>case studies of using simulation for reliability analysis<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"scienceApp\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-scienceApp tb_idxh874 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_zemu876 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_k8mc512   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Scientific AI and Applications<\/h3>\n<p><b>Track Coordinators: <\/b>Rafael Mayo-Garc\u00eda (CIEMAT), Esteban Mocskos (Universidad de Buenos Aires)<\/p>\n<p>The \u2018Scientific AI and Applications\u2019 track is focused on theory, experimentation, and engineering practices that form the basis for the design and use of simulation methodologies in science, including artificial intelligence and evolutionary algorithms. The objective of the track is to be a point of transversal communication in which methodologies, techniques, tools, and practical issues in any specific scientific domain can be extended and adopted by others.<\/p>\n<p>According to the general WSC 2025 motto, contributions addressing simulations tackling future challenges on any scientific and\/or technological field are very welcome. Hence, topics of interest include, but are not limited to:<\/p>\n<ul>\n<li>Application of Artificial Intelligence for solving simulated phenomena<\/li>\n<li>Applied simulation methodologies<\/li>\n<li>Challenges in performance evaluation of scientific applications<\/li>\n<li>Data-driven workflows<\/li>\n<li>Digital twins of scientific ecosystems and facilities<\/li>\n<li>Evolutionary algorithms applied in science<\/li>\n<li>Large-scale debugging and analysis tools<\/li>\n<li>Modeling tools and frameworks<\/li>\n<li>Networking technologies in scientific applications<\/li>\n<li>Scaling methodologies<\/li>\n<li>Scientific data retrieval, storage, and processing<\/li>\n<li>Successful use cases<\/li>\n<li>Support for the development of scientific applications<\/li>\n<li>Usage of new technologies and architectures<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"satw\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-satw tb_1e53941 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_2ome472 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_r1ct283   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Simulation Around the World<\/h3>\n<p><b>Track Coordinator: <\/b>Mar\u00eda Julia Blas (INGAR CONICET-UTN), Stewart Robinson (Newcastle University)<\/p>\n<p>Click <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/simulation-around-the-world\/\">here<\/a> for more information.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"simai\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-simai tb_rcmk283 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_rnv0283 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_rf8e473   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Simulation and Artificial Intelligence<\/h3>\n<p><b>Track Coordinators: <\/b>Mohammad Dehghani\u00a0(Northeastern University), Edward Hua (MITRE Corporation), Yijie Peng (Peking University)<\/p>\n<p>The integration of simulation modeling and Artificial Intelligence (AI) has been highly successful over the past few years. Simulation modeling is an effective tool for analyzing the performance of complex stochastic systems, while Artificial Intelligence has demonstrated significant success in addressing decision-making challenges. The combination of both brings the best of both worlds, where simulation data, as a clean and processed dataset, enhances AI applications to unlock new opportunities. Major AI fields such as deep learning and reinforcement learning have played a crucial role in harnessing the potential of this integration. Additionally, recent advancements in generative AI (GenAI) are opening new avenues for innovation and creativity in simulation and decision-making processes.<\/p>\n<p>This track invites papers and presentations about state-of-the-art research at the intersection of simulation and AI. Potential topics include, but are not limited to:<\/p>\n<ul>\n<li>Simulation and AI methodologies<\/li>\n<li>AI for adaptive simulations<\/li>\n<li>Generative models for simulation<\/li>\n<li>GenAI-enabled simulations<\/li>\n<li>Large Language Models (LLMs) in simulation<\/li>\n<li>Simulation-based machine learning<\/li>\n<li>Simulation-based reinforcement learning<\/li>\n<li>AI-driven simulation modeling and optimization<\/li>\n<li>Applications of Simulation and AI<\/li>\n<li>Simulation and AI in parallel computing environment<\/li>\n<li>Simulation to train generative AI model<\/li>\n<li>Simulation for AI safety and ethics<\/li>\n<li>Multi-agent systems and simulation<\/li>\n<li>AI for simulation verification and validation (V&amp;V)<\/li>\n<li>Simulation for AI explainability<\/li>\n<li>AI-enhanced simulation for complex systems<\/li>\n<li>Human-AI interaction in simulated environments<\/li>\n<li>AI for predictive simulation<\/li>\n<li>Simulation in AI-driven robotics<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"simdigtwin\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-simdigtwin tb_fe7w860 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_wxhe861 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_qwlt862   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Simulation as Digital Twin<\/h3>\n<p><strong>Track Coordinators:<\/strong> Haobin Li (National University of Singapore), Giovanni Lugaresi (KU Leuven), Jie Xu (George Mason University)<\/p>\n<p>Simulation is a relevant enabling technology for fully exploiting data streams to make fast predictions, quantifying continuous improvement actions and making smart decisions in real-time. This track is interested in research contributions on simulation theory and applications to support complex processes such as production, logistics, service delivery, etc. A particular interest is on models and algorithms to create, update, and keep synchronized simulation models with physical systems, as well as methodologies of simulation modelling, prediction, and optimization methods suitable for making intelligent decisions in real-time data stream environments. Data-driven approaches are also well aligned with the track being the enablers for skill-free technologies and the integration with artificial intelligence. Contributions describing physical and\/or digital laboratories to test new models, methods, and tools for simulation as digital twin are also appreciated. Topics include, but are not limited to, the following:<\/p>\n<ul>\n<li>Data-driven simulation modelling &amp; validation<\/li>\n<li>Production planning and control<\/li>\n<li>Real time simulation-based control<\/li>\n<li>Simulation-based optimization<\/li>\n<li>Simulation-based closed-loop controls<\/li>\n<li>On-line validation<\/li>\n<li>Physical-to-digital synchronization and alignment<\/li>\n<li>Cyber-physical systems<\/li>\n<li>Digital Twins<\/li>\n<li>Manufacturing<\/li>\n<li>De-manufacturing and circular economy<\/li>\n<li>Transportation and logistics<\/li>\n<li>Automated warehouses<\/li>\n<li>Operations and supply chains<\/li>\n<li>Maintenance<\/li>\n<li>Healthcare systems<\/li>\n<li>Traffic control<\/li>\n<li>Service systems<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"simed\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-simed tb_50tl118 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_uy4c118 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_x1tp118   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Simulation in Education<\/h3>\n<p><b>Track Coordinators: <\/b>Omar Ashour (Penn State University), Ashkan Negahban (Penn State University)<\/p>\n<p>The Simulation in Education track aims to share pedagogical, andragogical, and didactic approaches that employ or promote the use of simulation in education. These approaches could utilize technology-enhanced environments (e.g., video games, simulations, mixed reality, pervasive computing), non-technology environments\/approaches (e.g., Lego simulations and role-play), or a combination of both. The track is focused on educators and trainers who are interested in incorporating simulations into classrooms and\/or training programs to educate the next generation of scientists, engineers, healthcare professionals, artists, humanists, and social scientists.<\/p>\n<p>The track also covers teaching and learning methods for developing modeling and simulation skills. All learning delivery modes and situations are of interest, i.e., in-person vs remote learning, formal vs informal learning, K-12, higher education, professional training, continuing education, adult learning, etc.\u00a0\u00a0\u00a0<\/p>\n<p>The track seeks work from all disciplines including but not limited to engineering, science, medicine, arts, humanities, and social sciences. The sessions in this track will cover a wide range of topics including but not limited to:<\/p>\n<ul>\n<li>The design and development of simulation methods<\/li>\n<li>Teaching, learning, and assessment approaches for simulation education<\/li>\n<li>Use of technology and immersive environments in education (virtual reality, augmented reality, mixed reality, video games, artificial intelligence, etc.)<\/li>\n<li>Best practices and case studies of using simulation in education<\/li>\n<li>New tools and techniques for using simulation in education<\/li>\n<li>Challenges in implementing simulation in education<\/li>\n<li>Hands-on workshops or demonstrations of simulation software packages<\/li>\n<li>Simulation-based and experiential learning<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"simspace\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-simspace tb_8oq8364 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_o1mv364 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_qfuh364   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Simulation in Space (NEW)<\/h3>\n<p><b>Track Coordinators: <\/b>Maziar Ghorbani (Brunel University of London), Anastasia Anagnostou (Brunel University of London)<\/p>\n<p>Simulation has been widely used in Space systems to analyse and validate new approaches and equipment, and to train people in their use. Simulation is a critical technique in Space research as the target environments cannot be easily reached. With new and ambitious space programs being developed, new challenges and uses of simulation are needed. The Simulation in Space Track aims to showcase the use of simulation across Space agencies and industries as well as new innovations in simulation to support these. We invite original contributions from academic institutions, space agencies and industries that present the results of simulation research, innovation and application in Space. Areas of interest include, but are not limited to:<\/p>\n<ul>\n<li>Satellite simulation<\/li>\n<li>Commercial space travel<\/li>\n<li>Space station simulation<\/li>\n<li>Space tourism<\/li>\n<li>Lunar habitation<\/li>\n<li>Internet of Space<\/li>\n<li>Lunar Internet simulation<\/li>\n<li>Interplanetary Internet simulation<\/li>\n<li>Space Simulation Standards<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"simOptimize\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-simOptimize tb_v1zw408 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_ey5w410 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_ngm7319   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Simulation Optimization<\/h3>\n<p><b>Track Coordinators: <\/b>David Eckman (Texas A&amp;M University), Siyang Gao (City University of Hong Kong), Yuwei Zhou (Indiana University)<\/p>\n<p>The Simulation Optimization track focuses on the design, analysis, and application of algorithms that can be coupled with computer simulations to identify decision variable values that optimize one or more simulation performance measures of interest. This track is interested in papers on the development of new algorithms accompanied by analysis of the algorithm\u2019s theoretical, empirical, and computational performance. The track also welcomes papers that compare existing simulation-optimization algorithms or apply them to real-world problems. Topics of interest include, but are not limited to:<\/p>\n<ul>\n<li>Global and black-box optimization<\/li>\n<li>Simheuristics<\/li>\n<li>Metaheuristics<\/li>\n<li>Discrete optimization via simulation<\/li>\n<li>Ranking and selection<\/li>\n<li>Stochastic programming<\/li>\n<li>Sample average approximation<\/li>\n<li>Stochastic approximation methods<\/li>\n<li>Metamodel-based methods<\/li>\n<li>Multi-objective optimization<\/li>\n<li>Optimization with stochastic constraints<\/li>\n<li>Approximate dynamic programming and reinforcement learning<\/li>\n<li>Optimal learning<\/li>\n<li>Active learning<\/li>\n<li>Multi-armed bandit methods<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"UncertaintyRobustSim\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-UncertaintyRobustSim tb_xisa662 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_y24g662 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_963n662   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Uncertainty Quantification &amp; Robust Simulation<\/h3>\n<p><b>Track Coordinators: <\/b>Ilya Ryzhov (University of Maryland), Wei Xie (Northeastern University)<\/p>\n<p>The Uncertainty Quantification and Robust Simulation track aims to cover mathematical, statistical, algorithmic, and application advances in uncertainty quantification and robust simulation, which facilitate characterization, quantification, and management of various sources of uncertainty inherent in the use of simulation models to guide optimal design and control for complex stochastic systems. The sources of uncertainty include the observation errors in real-world data sets used to improve the model fidelity of digital twins, calibrate the input and state transition models, and validate the simulation model, structural uncertainty, numerical uncertainty, etc. These uncertainties can impact, e.g., simulation-based predictive accuracy, simulation optimization, sensitivity analysis, optimal learning, and feasibility assessment in various ways and to different extents. Papers investigating various sources of uncertainty and their impacts that are broadly defined are welcome. Contributions can include the development of quantification criteria, novel statistical or mathematical methods to assess the impacts of different sources of uncertainty or errors, the efficiency analyses or improvements of existing methods, and applications of these methods in different domain contexts. Topics of interest include, but are not limited to:<\/p>\n<ul>\n<li>Input uncertainty quantification criteria and methods<\/li>\n<li>Robustness in input modeling and selection<\/li>\n<li>Model risk quantification and reduction<\/li>\n<li>Model calibration and validation<\/li>\n<li>Data assimilation<\/li>\n<li>Risk-sensitive simulation optimization<\/li>\n<li>Robustness against model misspecifications in simulation logic and state transition<\/li>\n<li>Sensitivity analysis<\/li>\n<li>Optimal learning and data collection<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"vendor\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-vendor tb_i849700 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_0dqq702 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_0zbe868   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Vendor<\/h3>\n<p><b>Track Coordinators: <\/b>Amy Brown Greer (MOSIMTEC LLC)<\/p>\n<p>In addition to the Sunday Workshops, exhibitors can participate in the Vendor Track at WSC. The Vendor Track provides an opportunity for companies that market modeling and simulation technology and services, or related services (e.g., statistical analyses) to present their innovations and successful applications.<\/p>\n<p>For each slot in the Vendor Track, vendors should submit a 2-page Extended Abstract.\u00a0 Extended Abstracts appear online and in the final program, but neither appear in the archival proceedings (due to IEEE rules). Extended Abstracts are reviewed by the track coordinators and may entail revisions. Extended Abstracts must use the Authors Kit to adhere to the publication format requirements.<\/p>\n<p>For more details on participating as an exhibitor, click <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/exhibitors\/\">here<\/a>.<\/p>\n<p style=\"font-variant-caps: normal; orphans: auto; text-align: start; widows: auto; word-spacing: 0px;\">\u00a0<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n        <\/div>\n<!--\/themify_builder_content-->","protected":false},"excerpt":{"rendered":"<p>Advanced Tutorials Agent-Based Simulation Analysis Methodology Aviation Modeling &amp; Analysis Commercial Case Studies Complex &amp; Generative Systems Data Science &amp; Simulation Environment, Sustainability &amp; Resilience Healthcare &amp; Life Sciences Hybrid Modeling &amp; Simulation<\/p>\n","protected":false},"author":1001077,"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-4365","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>Tracks - Winter Simulation Conference 2025<\/title>\n<meta name=\"description\" content=\"All WSC 2025 tracks and track coordinators. This year there are over 30 tracks for academics and industry professionals to peruse.\" \/>\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\/wsc2025\/tracks\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Tracks\" \/>\n<meta property=\"og:description\" content=\"All WSC 2025 tracks and track coordinators. This year there are over 30 tracks for academics and industry professionals to peruse.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/\" \/>\n<meta property=\"og:site_name\" content=\"Winter Simulation Conference 2025\" \/>\n<meta property=\"article:modified_time\" content=\"2025-07-17T16:55:45+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/files\/2024\/12\/RGB_WSC_2025_Logo-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"344\" \/>\n\t<meta property=\"og:image:height\" content=\"346\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/\",\"url\":\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/\",\"name\":\"Tracks - Winter Simulation Conference 2025\",\"isPartOf\":{\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/#website\"},\"datePublished\":\"2016-12-29T19:56:34+00:00\",\"dateModified\":\"2025-07-17T16:55:45+00:00\",\"description\":\"All WSC 2025 tracks and track coordinators. This year there are over 30 tracks for academics and industry professionals to peruse.\",\"breadcrumb\":{\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Tracks\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/#website\",\"url\":\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/\",\"name\":\"Winter Simulation Conference 2025\",\"description\":\"Winter Simulation Conference 2025\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Tracks - Winter Simulation Conference 2025","description":"All WSC 2025 tracks and track coordinators. This year there are over 30 tracks for academics and industry professionals to peruse.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/","og_locale":"en_US","og_type":"article","og_title":"Tracks","og_description":"All WSC 2025 tracks and track coordinators. This year there are over 30 tracks for academics and industry professionals to peruse.","og_url":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/","og_site_name":"Winter Simulation Conference 2025","article_modified_time":"2025-07-17T16:55:45+00:00","og_image":[{"width":344,"height":346,"url":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/files\/2024\/12\/RGB_WSC_2025_Logo-1.png","type":"image\/png"}],"twitter_card":"summary_large_image","schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/","url":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/","name":"Tracks - Winter Simulation Conference 2025","isPartOf":{"@id":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/#website"},"datePublished":"2016-12-29T19:56:34+00:00","dateModified":"2025-07-17T16:55:45+00:00","description":"All WSC 2025 tracks and track coordinators. This year there are over 30 tracks for academics and industry professionals to peruse.","breadcrumb":{"@id":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/tracks\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/"},{"@type":"ListItem","position":2,"name":"Tracks"}]},{"@type":"WebSite","@id":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/#website","url":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/","name":"Winter Simulation Conference 2025","description":"Winter Simulation Conference 2025","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"builder_content":"<ul> <li><a href=\"#advTut\"><b>Advanced Tutorials<\/b><\/a><\/li> <li><a href=\"#abSim\"><b>Agent-Based Simulation<\/b><\/a><\/li> <li><a href=\"#adMeth\"><b>Analysis Methodology<\/b><\/a><\/li> <li><a href=\"#aviation\"><b>Aviation Modeling &amp; Analysis<\/b><\/a><\/li> <li><a href=\"#caseStudies\"><b>Commercial Case Studies<\/b><\/a><\/li> <li><b><a href=\"#complex\">Complex &amp; Generative Systems<\/a><\/b><\/li> <li><a href=\"#datascience\"><strong>Data Science &amp; Simulation<\/strong><\/a><\/li> <li><a href=\"#envSusRisk\"><b>Environment, Sustainability &amp; Resilience<\/b><\/a><\/li> <li><strong><a href=\"#healthcare\">Healthcare &amp; Life Sciences<\/a><\/strong><\/li> <li><a href=\"#hybrid\"><strong>Hybrid Modeling &amp; Simulation<\/strong><\/a><\/li> <\/ul>\n<ul> <li><strong><a href=\"#introTutorial\">Introductory Tutorials<\/a><\/strong><\/li> <li><strong><a href=\"#supplyChain\">Logistics, Supply Chain &amp; Transportation<\/a><\/strong><\/li> <li><strong><a href=\"#manuApp\">Manufacturing &amp; Industry 4.0<\/a><\/strong><\/li> <li><strong><a href=\"#militaryApp\">Military &amp; National Security<\/a><\/strong><\/li> <li><strong><a href=\"#masm\">Modeling &amp; Analysis of Semiconductor Manufacturing (MASM)<\/a><\/strong><\/li> <li><strong><a href=\"#modelmethodology\">Modeling Methodology<\/a><\/strong><\/li> <li><strong><a href=\"#phd\">PhD Colloquium<\/a><\/strong><\/li> <li><strong><a href=\"#poster\">Poster Session<\/a><\/strong><\/li> <li><strong><a href=\"#profDev\">Professional Development<\/a><\/strong><\/li> <\/ul>\n<ul> <li><strong><a href=\"#projMgmt\">Project Management &amp; Construction<\/a><\/strong><\/li> <li><strong><a href=\"#reliability-modeling\">Reliability Modeling &amp; Simulation<\/a><\/strong><\/li> <li><strong><a href=\"#scienceApp\">Scientific AI &amp; Applications<\/a><\/strong><\/li> <li><strong><a href=\"#satw\">Simulation Around the World<\/a><\/strong><\/li> <li><strong><a href=\"#simai\">Simulation &amp; Artificial Intelligence<\/a><\/strong><\/li> <li><strong><a href=\"#simdigtwin\">Simulation as Digital Twin<\/a><\/strong><\/li> <li><strong><a href=\"#simed\">Simulation in Education<\/a><\/strong><\/li> <li><a href=\"#simspace\"><strong>Simulation in Space (NEW)<\/strong><\/a><\/li> <li><strong><a href=\"#simOptimize\">Simulation Optimization<\/a><\/strong><\/li> <li><strong><a href=\"#UncertaintyRobustSim\">Uncertainty Quantification &amp; Robust Simulation<\/a><\/strong><\/li> <li><strong><a href=\"#vendor\">Vendor<\/a><\/strong><\/li> <\/ul>\n<h3>Advanced Tutorials<\/h3> <p><b>Track Coordinators: <\/b>Javier Faulin (Public University of Navarre), Giulia Pedrielli (Arizona State University)<\/p> <p>The Advanced Tutorial track is oriented toward experienced practitioners and researchers who want to hear about the most recent developments, presented in a directly applicable form. The track encourages tutorials that focus on topics of special interest, as well as the latest theory and methods and resulting modeling, simulation, and analysis tools. Also of interest are pertinent topics in related disciplines, such as social network, healthcare, epidemic disease, energy, emergency response, augmented and virtual reality, simulation of big-data, blockchain, and so on. These special-focus sessions give practitioners and researchers a survey of recent fundamental advances in the discipline of modeling and simulation.<\/p>\n<h3>Agent-Based Simulation<\/h3> <p><b>Track Coordinators: <\/b>Andrew Collins (Old Dominion University), Martijn Mes (University of Twente)<\/p> <p>The track focuses on theoretical, methodological, and applied research in agent-based simulation (ABS) and multi-agent systems. Submissions to the ABS track should address advancements in agent-based simulation modeling, including topics such as high-level specification, execution platforms, modeling languages, validation techniques, output analysis, and experimental methods. The track also welcomes contributions that explore the integration of ABS with emerging topics like artificial intelligence (AI), analytics, and big data, as well as applications to adaptive or self-organizing systems. Authors applying ABS to fields covered by other tracks at the Winter Simulation Conference are encouraged to submit their work to the relevant application track.<\/p> <p>Any paper that includes the development of agent-based simulation must clearly discuss the underlying model - preferably using structured protocols like the ODD (Overview, Design concepts, and Details) framework - and include an evaluation of the simulation's validation.<\/p> <p>Contributions of particular interest include research on intelligent agents, agent behavior modeling, analytics, and applications of ABS to less traditional ABS domains, such as the humanities and arts. The track also encourages submissions demonstrating how agents can serve as supportive tools to enhance simulation-based problem-solving processes.<\/p>\n<h3>Analysis Methodology<\/h3> <p><b>Track Coordinators: <\/b>Dohyun Ahn (Chinese University of Hong Kong), Ben Feng (University of Waterloo), Eunhye Song (Georgia Institute of Technology)<\/p> <p>The Analysis Methodology track invites research contributions to mathematical and computational aspects of computer simulation. This may be a new problem formulation, estimation procedure, algorithm design, proof technique, and more. Combining state-of-the-art methodologies in other adjacent areas such as statistics, computational physics, applied mathematics, data science, and machine learning to push the research frontier of simulation is particularly welcome. Topics of interest include, but are not limited to:<\/p> <ul> <li>Simulation experiment design<\/li> <li>Input modeling and output analysis<\/li> <li>Risk and uncertainty quantification<\/li> <li>Sensitivity analysis<\/li> <li>Variance reduction techniques<\/li> <li>Rare-event simulation<\/li> <li>Improving algorithmic efficiency<\/li> <li>Metamodeling<\/li> <li>Simulation model validation and calibration<\/li> <\/ul>\n<h3>Aviation Modeling and Analysis<\/h3> <p><b>Track Coordinators: <\/b>Miguel Mujica Mota (Amsterdam University of Applied Sciences), Michael Schultz (Bundeswehr University Munich), John Shortle (George Mason University)<\/p> <p>The world\u2019s air transportation system is preparing for an influx of new users with diverse needs, while simultaneously growing in its traditional areas. The Aviation Track aims to cover most of the important areas of the aviation industry where simulation alone or together with other techniques can provide solutions. Therefore, we invite researchers from research institutions, universities, airlines, air navigation service providers, and industry to submit original papers presenting results of their work.<\/p> <p>Areas of interest include, but are not limited to:<\/p> <ul> <li>Human-in-the-Loop simulations for training and for evaluating new technologies<\/li> <li>Airports<\/li> <li>Capacity &amp; efficiency improvement<\/li> <li>Airport capacity forecast<\/li> <li>Business intelligence for airports<\/li> <li>Multi-Airport Systems<\/li> <li>Small and regional airport development<\/li> <li>Airline operations<\/li> <li>Maintenance, Repair, and Overhaul and Lean MRO<\/li> <li>Optimization of operational processes or specific problems in aviation<\/li> <li>Air Traffic Management<\/li> <li>ATC\/AIRPORT systems<\/li> <li>Digital \/ remote tower operations<\/li> <li>Predictability of air transportation operations<\/li> <li>Unmanned airborne systems<\/li> <li>Trajectory modeling<\/li> <li>Safety of interactions with manned aviation<\/li> <li>Air traffic control concepts<\/li> <li>Development of incident investigation<\/li> <li>Environmental effects of aviation<\/li> <li>Cargo problems in aviation<\/li> <li>Multimodality where aviation is involved<\/li> <li>Economics of the air transportation system<\/li> <li>Communications, navigation, and surveillance systems<\/li> <\/ul>\n<h3>Commercial Case Studies<\/h3> <p><strong>Track Coordinator<\/strong>: Saurabh Parakh (MOSIMTEC)<\/p> <p>Click <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/commercial-case-studies\/\">here<\/a> for more information.\u00a0<\/p>\n<h3>Complex and Generative Systems<\/h3> <p><b>Track Coordinators:<\/b>\u00a0 Saurabh Mittal (MITRE Corporation), Claudia Szabo (University of Adelaide)<br><br><\/p> <p>This track is focused on the modeling, simulation, and engineering of complex, intelligent, adaptive, and generative systems and how they handle faults, system issues, and emergent behaviors, and provide new capabilities. The increasing popularity of the Generative AI technologies emphasizes that GenAI-enabled will be integrated in all the facets of society. These new class of systems will need to be integrated with existing systems within a system of systems (SoS) context. This SoS may also incorporate adaptive and autonomous elements (such as systems that have different levels of autonomy and situated behavior). This makes design, analysis, and testing for the system-at-hand a complex endeavor itself.<\/p> <p>The system\u2019s behavior is also determined by the input, which evolves from the dynamic environment. This exact factor is difficult to predict, due to an ever-increasing level of autonomy and complexity. Advanced Modeling and Simulation (M&amp;S) frameworks are required to facilitate SoS design, development, testing, and integration. In more particular, these frameworks must provide methods to deal with intelligent, emergent, autonomous, adaptive and generative behavior.<\/p> <p>The subject of emergent behavior and M&amp;S of emergent behaviors takes the center stage in such systems as it is unknown how a system responds in the face of emergent behavior arising out of interactions with other complex systems. The generative aspect integrated from GenAI technology impacting the behavior space of the SoS introduce further uncertainty that must be quantified. Intelligent behavior is also defined as an emergent property in some complex systems. Consequently, systems that respond and adapt to such behaviors may be called intelligent systems as well. With integrated generative technologies, such as Large Language Models (LLMs) and the associated vast amounts of corpus of data, the community is already pursuing Artificial Generative Intelligence (AGI). This track has two objectives.<\/p> <p>The first objective aims to focus on M&amp;S of the following aspects of complex SoS engineering with a focus on generative and intelligent systems, and brings researchers, developers and industry practitioners working in the areas of complex, generative and autonomous SoS engineering. This objective covers the following topics:<\/p> <ul> <li>Theory for complex, intelligence-based, adaptive and generative systems<\/li> <li>Computational intelligence and cognitive systems<\/li> <li>Human-in-the-loop systems and Human-on-the-loop systems<\/li> <li>M&amp;S Frameworks for intelligent and generative behavior<\/li> <li>Methodologies, tools, and architectures for adaptive generative systems<\/li> <li>Knowledge engineering, generation, and management<\/li> <li>Weak and Strong emergent behavior, Emergent Engineering<\/li> <li>Complex adaptive systems engineering employing LLMs<\/li> <li>Self-* (organization, generative, explanation, configuration) capability and collaborative behavior<\/li> <li>Applications to robotics, unmanned vehicles systems, swarm technology, semantic web technology, and multi-agent systems<\/li> <li>Live, Virtual and Constructive (LVC) environments<\/li> <li>Modeling, engineering, testing and verification of complex and generative behavior<\/li> <li>Modeling, simulating, and testing generative applications<\/li> <\/ul> <p>The second objective is to incorporate Complexity Science in simulation models. Complexity is a multi-level phenomenon that exists at structural, behavioral and knowledge levels in such SoS. Emergent behavior is an outcome of this complexity. Understanding emergent behavior as an outcome of this complexity will provide a foundation for generative intelligent systems, and the M&amp;S thereof. Topics related to this objective include, but are not limited to handling of:<\/p> <ul> <li>Complexity in Structure: network, hierarchical, small-world, flat, etc.<\/li> <li>Complexity in Behavior: Micro and macro behaviors, local and global behaviors, teleologic and epistemological behaviors<\/li> <li>Complexity in Knowledge: ontology design, ontology-driven modeling, ontology-evaluation, ontology transformation, etc.<\/li> <li>Complexity in Human-in-the-loop: artificial agents, cognitive agents, multi-agents, man-in-loop, human-computer-interaction<\/li> <li>Complexity in Human-on-the-loop: trust modeling, human-machine-interaction<\/li> <li>Complexity in intelligence-based systems: Situated behavior, knowledge-based behavior, mnemonic behavior, resource-constrained systems, energy-aware systems<\/li> <li>Complexity in adaptation and autonomy<\/li> <li>Complexity in architecture: Flat, full-mesh, hierarchical, adaptive, swarm, transformative<\/li> <li>Complexity in awareness: Self-* (organization, generative, explanation, configuration)<\/li> <li>Complexity in interactions: collaboration, negotiation, greedy, rule-based, environment-based, etc.<\/li> <li>Complexity in LVC environments<\/li> <li>Complexity in artificial systems, social systems, techno-economic-social systems<\/li> <li>Complexity in model engineering of complex SoS employing LLMs<\/li> <li>Complexity in model specification using modeling languages and architecture frameworks such as UML, PetriNets, SysML, DoDAF, MoDAF, UAF, etc.<\/li> <li>Complexity in simulation infrastructure engineering: distributed simulation, parallel simulation, cloud simulation, netcentric parallel distributed environments<\/li> <li>Complexity in Testing and Evaluation (T&amp;E) tools for SoS engineering<\/li> <li>Complexity in Heterogeneity: Hardware\/Software Co-design, Hardware in the Loop, Cyber-Physical Systems, the Internet of Things<\/li> <li>Metrics for Complexity design and evaluation<\/li> <li>Complexity in verification, validation, and accreditation in SoS<\/li> <li>Complexity of Application in domain model engineering: Financial, Power, Robotics, Swarm, Economic, Policy, etc.<\/li> <li>Complexity in SoS failure<\/li> <\/ul>\n<h3>Data Science and Simulation<\/h3> <p><b>Track Coordinators: <\/b>Abdolreza Abhari (Toronto Metropolitan University), Cheng-Bang Chen (University of Miami), Niclas Feldkamp (TU Ilmenau)<\/p> <p>The Data Science and Simulation track aims to promote novel contributions in the use and generation of big data within simulations as well as using data science to aid understanding of simulation results. This track welcomes all methodological, technical, and application area-focused contributions that advance the modeling and simulation body of knowledge. Some topics of interest include:<\/p> <ul> <li>Big Data in simulation<\/li> <li>Data analytics for simulation<\/li> <li>Machine learning methods to generate synthetic data<\/li> <li>Simulating massive data processing systems<\/li> <li>Digital Twin Simulations<\/li> <li>Machine learning and data mining in modeling and simulation<\/li> <li>Data-Driven techniques for modeling and simulation<\/li> <li>Deep learning in modeling and simulation<\/li> <li>Hybrid approaches of combining simulation and machine Learning<\/li> <li>Simulation initialization techniques using big data<\/li> <li>Big data management\/processing techniques for simulations<\/li> <li>Ontologies for big data modeling<\/li> <li>Data science pipelines for modeling for simulations<\/li> <li>Data science and social networks in simulation<\/li> <li>Simulation of cloud computing and distributed systems<\/li> <\/ul>\n<h3>Environment, Sustainability, and Resilience<\/h3> <p><b>Track Coordinators: <\/b>Jiaqi Ge (University of Leeds), Shima Mohebbi (George Mason University)<\/p> <p>The Environment, Sustainability, and Resilience track focuses on the theory and application of simulation and modeling to derive innovative and resilient solutions to environmental and sustainability challenges. Application areas include infrastructure systems, cyber-physical systems, ecological systems, renewable resources, transportation, buildings, farming, manufacturing, and urban\/city science. We solicit papers presenting new ideas, concepts, models, methods, tools, standards, and applications to achieve sustainability and resiliency in natural and built environments. Possible topics include, but are not limited to:<\/p> <ul> <li>Decision support and analytics for sustainability<\/li> <li>Smart, connected, and resilient infrastructure<\/li> <li>Social dynamics and policy innovations of managing sustainable infrastructure systems<\/li> <li>Simulation and network modeling for resilience assessment<\/li> <li>Environmental modelling, visualization, and optimization<\/li> <li>Renewable resources and related processes<\/li> <li>Smart building systems and robust design<\/li> <li>Energy\/resource-efficient manufacturing<\/li> <li>Smart and resilient grids<\/li> <li>Information modeling and interoperability<\/li> <li>Energy-efficient and sustainable urban planning and design<\/li> <li>Intelligent transportation systems<\/li> <li>Human-environment interaction<\/li> <li>Ecological systems<\/li> <li>Compound hazards and their impact on communities and engineered systems<\/li> <li>Environmental risk assessment and mitigation<\/li> <li>Human adaptation to climate<\/li> <\/ul>\n<h3>Healthcare and Life Sciences<\/h3> <p><b>Track Coordinators: <\/b>Tugce Martagan (Eindhoven University of Technology), Varun Ramamohan (Indian Institute of Technology Delhi)<\/p> <p>The Healthcare and Life Sciences track addresses important areas in which simulation can provide critical decision support for operational and strategic planning and decision making that individual providers (doctors\/nurses, clinics, urgent care centers, hospitals) face, as well as for policy issues that must be addressed by administering systems (e.g., hospitals, insurance companies, and governments). Traditionally, this track has been broad in focus, incorporating Discrete Event Simulation, System Dynamics, Agent-Based Simulation, and\/or Monte Carlo simulations, with a variety of applications. A common thread is the use of simulation tools to provide insight into, or to inform, decisions for improved healthcare performance measures and outcomes. New modeling tools that address challenges with the conceptualization or implementation of healthcare systems, and general healthcare simulations are welcome. Topics include, but are not limited to, the following:<\/p> <ul> <li>Admissions and control<\/li> <li>Ancillary services<\/li> <li>Appointment scheduling<\/li> <li>Emergency room access<\/li> <li>Epidemic and pandemic modeling<\/li> <li>General healthcare simulation<\/li> <li>Global Health<\/li> <li>Healthcare optimization<\/li> <li>Healthcare systems<\/li> <li>Medical decision making<\/li> <li>Outpatient access<\/li> <li>Outpatient capacity analysis<\/li> <li>Payment\/Payer models<\/li> <li>Performance improvement models<\/li> <li>Pricing models<\/li> <li>Resource scheduling (e.g., nurse, doctor, anaesthesiologist, residents, equipment, etc.)<\/li> <\/ul>\n<h3>Hybrid Modeling &amp; Simulation<\/h3> <p><strong>Track Coordinators: <\/strong>Masoud Fakhimi (University of Surrey), Nav Mustafee (University of Exeter)<\/p> <p>The Hybrid Modeling and Simulation track welcomes submissions from authors whose research necessitated the overcoming of limitations of using a single simulation approach by employing hybrid simulation (HS) formulation consisting of combinations of discrete-event simulation (DES), agent-based modeling (ABM), system dynamics (SD), and Monte Carlo simulation. We also welcome papers that have considered the integration of simulation techniques (as above) with research paradigms, frameworks and methodologies, techniques, tools and methods of enquiry from a wider plethora of techniques used in Operations Research (e.g., qualitative system dynamics and soft system methodologies with discrete-event simulation; analytical modeling with computer simulation; DEA and MCDA with DES), as also disciplines wider than only M&amp;S and OR, such as computer science and applied computing, system design, data science and AI, economics, psychology and humanities. Such cross-disciplinary hybrid models (HM) may include work that has combined ABM, DES and SD, or indeed a hybrid simulation, with design science (e.g., use of design and platform thinking in the conceptualization of a simulation study), systems engineering and automation (e.g., digital twins integrated with data acquisition systems), machine learning and AI (e.g., use of reinforcement learning feedback with simulation), generative AI (e.g., use of LLMs\/ChatGPT for better comprehension of simulation output), etc.<\/p> <p>In summary, the hybrid simulation and modeling track welcomes papers that have combined simulation techniques such as ABS, DES and SD (also referred to as hybrid simulation - HS) OR have used simulation with one or more techniques from disciplines outside M&amp;S (referred to as hybrid modeling - HM), thereby providing greater synergy for the solution and deeper insights into the problem, compared to what can be achieved through the application of single techniques.<\/p> <p>Topics include, but are not limited to, the following:<\/p> <ul> <li>Literature reviews in hybrid simulation (HS) and hybrid modeling (HM)<\/li> <li>Taxonomies and conceptualizations of HS and HM<\/li> <li>Methodology focusing on the development of frameworks and modeling formalisms to support hybrid M&amp;S<\/li> <li>Case studies on hybrid M&amp;S in domains such as healthcare, manufacturing, supply chain, circular economy and sustainability, defense and policy.<\/li> <li>Technical papers on the development of software artefacts for supporting hybrid M&amp;S.<\/li> <li>Papers on validation and verification of hybrid models.<\/li> <li>Application of hybrid simulation in behavioral studies (commonly referred to as Behavioral Operations Research).<\/li> <li>Digital twins whose implementation includes multiple computing artefacts, e.g., simulation with machine learning, or which requires integration with real-time systems using approaches commonly found in industrial engineering, computer science and software engineering.<\/li> <li>Hybrid models that include both data-driven learning (e.g., supervised and unsupervised learning, reinforcement learning, process mining) with computer simulation.<\/li> <li>Hybrid models that use AI and LLMs in one or more stages of a simulation study (e.g., in developing the conceptual model, code generation for open-source simulation software, and improving the comprehension of simulation outputs for the stakeholders)<\/li> <\/ul>\n<h3>Introductory Tutorials<\/h3> <p><b>Track Coordinators:<\/b> Chang-Han Rhee (Northwestern University),\u00a0Antuela Tako (Nottingham Trent University)<\/p> <p>The Introductory Tutorials track is oriented toward professionals in modeling and simulation interested in broadening, refreshing, and expanding their knowledge of the field. The track covers a wide range of topics including mathematical and statistical foundations, methods, application areas, software tools, and analysis tools. In addition, the track also encourages tutorials that provide an accessible introduction to emerging research topics in simulation.<\/p>\n<h3>Logistics, Supply Chain Management, Transportation<\/h3> <p><b>Track Coordinators: <\/b>Montasir Abbas (Virginia Tech), Majsa Ammouriova (German Jordanian University), David Goldsman (Georgia Institute of Technology), Markus Rabe (Technische Universit\u00e4t Dortmund)<\/p> <p>The nature of today\u2019s highly dynamic, stochastic, and complex networks of supply, intralogistics, and distribution has led to decreasing transparency of enterprise processes, while at the same time increasing failure risks. These issues also apply for deliveries to the end customer, e.g., at collection points or at home delivery. In addition, pollution and climate change have created a demand for new concepts in urban logistics as well as careful planning in this uncertain field. Therefore, managers who are responsible for supply chain management and logistics require effective tools to provide credible analysis in this challenging environment. In order to facilitate the discussion of the best applications of simulation in this timely area, the LSCT track includes papers in logistics simulation, supply chain simulation, and simulation for planning, analyzing, and improving transportation in a wide scope encompassing topics from the detailed intralogistics level up to global supply chains. Topics of interest include, but are not limited to, the following:<\/p> <ul> <li>Green supply chain design<\/li> <li>Supply chain resilience<\/li> <li>Supply chain risk analysis<\/li> <li>Simulation-based optimization of supply chains<\/li> <li>Supply chain operations<\/li> <li>Demand and order fulfillment<\/li> <li>Inventory policies<\/li> <li>Warehouse management<\/li> <li>Intralogistics and advanced material flow systems<\/li> <li>Omni-channel logistics<\/li> <li>Online purchase delivery concepts<\/li> <li>Urban transport<\/li> <li>Last-mile logistics<\/li> <li>Crowdshipping<\/li> <li>Multi-modal logistics systems<\/li> <li>Port operations<\/li> <li>Rail operations<\/li> <li>Road networks and traffic management<\/li> <\/ul>\n<h3>Manufacturing &amp; Industry 4.0<\/h3> <p><b>Track Coordinators: <\/b>Alp Akcay (Eindhoven University of Technology), Christoph Laroque (University of Applied Sciences Zwickau), Gordon Shao (NIST)<\/p> <p>Simulation is the model-based methodology for analyzing dynamical inter-dependencies in manufacturing systems. The Manufacturing Applications track is interested in research work using simulation in industrial application areas as found in the automotive, aircraft and shipbuilding industries, among others. Manufacturing applications relate to the model-based analysis of (i) all production and logistics processes within a company or along a supply chain, and (ii) all phases of a system life cycle, such as system acquisition, system design and planning, implementation, start of operation and ramp-up, as well as the operations itself.<\/p> <p>A contribution shall describe the aims of investigation, the investigated system, the simulation model, the experimental plan, the findings, and any implementation results. Additionally, specific challenges like system complexity, data collection and preparation, or verification and validation may be pointed out. Topics include, but are not limited to, the following:<\/p> <ul> <li>Manufacturing<\/li> <li>Applications of simulation-based optimization in production<\/li> <li>Cyber-physical systems, Industry 4.0 \/ Industry 5.0<\/li> <li>Production planning and scheduling<\/li> <li>Lean management<\/li> <li>Total Quality Management<\/li> <li>Maintenance and Lifecycle-Assessment<\/li> <li>Integration of energy and carbon footprint<\/li> <li>Digital Production Twins<\/li> <\/ul>\n<h3>Military and National Security\u00a0<\/h3> <p><b>Track Coordinators: <\/b>Lance Champaign (Air Force Institute of Technology), Jamie Grymes (U.S. Military Academy)<\/p> <p>The Military and National Security Applications Track is interested in papers that describe the application of modeling and simulation theory, techniques, tools and technologies to challenges in the military and national security domain. Example application areas include: battle management command and control, air and missile defense, campaign analysis, weapon-target pairing, multi-domain operations, sustainment operations, operational testing and evaluation, wargaming and assessments, CBRNE defense, critical infrastructure analysis, homeland defense and domestic civil support operations, cybersecurity, information operations, electronic warfare, intelligence, surveillance and reconnaissance, medical and healthcare operations, manpower and personnel, readiness and training, cost, risk and decision analysis, special operations, etc.<\/p> <p>Topics of special interest include, but are not limited to, challenges and innovations for representation and implementation of command, control and communications, swarm intelligence, cybersecurity operations, cyber threat intelligence, social media analytics, hardware-in-the-loop simulations, human-machine teaming, future platforms and weapons prototyping, synthetic environments, multi-sensor fusion, complex behaviors of semi-automated forces, electronic warfare, expeditionary medical operations, automatic scenario planning and experimentation, and multi-resolution models. Papers investigating an innovative use of edge\/fog\/cloud technologies, gaming technology, mixed reality technology, artificial intelligence and machine learning technology, big data technologies, distributed computing technology, and networking technology for military and national security applications are also welcome!<\/p>\n<h3>Modeling &amp; Analysis of Semiconductor Manufacturing (MASM)<\/h3> <p><b>Track Coordinators:\u00a0 <\/b>John W. Fowler (Arizona State University), Lars M\u00f6nch (University of Hagen)<br><br>Click <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/masm\/\">here<\/a> for more information.\u00a0<\/p>\n<h3>Modeling Methodology<\/h3> <p><b>Track Coordinators: <\/b>Rodrigo Castro (University of Buenos Aires), Andrea D\u2019Ambrogio (University of Rome Tor Vergata), Gabriel Wainer (Carleton University)<\/p> <p>The Modeling Methodology track is interested in methodological advances with respect to the theory and practice of modeling and simulation. These may include approaches to model development, model building, verification, validation, experimentation, and optimization. Contributions to the advancement of the technology and the software used to support modeling are also welcome as are contributions featuring guiding or unifying frameworks, the development and application of meaningful formal methods, and lessons learned. If you have an idea for a special session or a panel discussion of particular interest to the WSC participants, please send an email with a short description and references to the work of relevant experts to the track chairs. Topics of interest include, but are not limited to, the following:<\/p> <ul> <li>Modeling paradigms<\/li> <li>Formal modeling languages<\/li> <li>Modeling approaches for real-time systems<\/li> <li>Technological advances in modeling software<\/li> <li>Spatial and temporal modeling<\/li> <li>Multilevel modeling<\/li> <li>Multi-paradigm modeling<\/li> <li>Multi-formalism modeling<\/li> <li>Model reuse, repositories, and retrieval<\/li> <li>Parallel and Distributed simulation<\/li> <li>Modeling with ontologies<\/li> <li>Semantic tools supporting modeling methods<\/li> <li>Standardization challenges<\/li> <li>Modeling and Simulation for Cyber-Physical Systems<\/li> <\/ul>\n<h3>PhD Colloquium<\/h3> <p><strong>Chair:<\/strong> Eunhye Song (Georgia Institute of Technology)<br><strong>Members<\/strong>: Cristina Ruiz Martin (Carleton University), Alison Harper (University of Exeter), Sara Shashaani (North Carolina State University)<\/p> <p>In 2025, ACM-SIGSIM and INFORMS-Sim will once again sponsor the PhD Colloquium for PhD students who are within two years of graduation (planning to graduate by December 2027). Students close to graduation will be given an opportunity to showcase their work during a short presentation session and a poster during the Colloquium (apart from the regular tracks). Presenting your PhD work to your peers and the larger simulation community will give you the opportunity to receive valuable feedback and ideas, as well as introduce you to a network that can be very helpful for your career once you graduate.<\/p> <p>Visit the <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/phd-colloquium\/\">PhD Colloquium page<\/a> for additional details.\u00a0<\/p>\n<h3>Poster Session<\/h3> <p><b>Track Coordinators: <\/b>Le Khanh Ngan Nguyen (University of Strathclyde), Juan Leonardo Sarli (The National Technological University \u2013 Santa Fe Regional Faculty)<\/p> <p>The Poster Session offers a timely venue to present and discuss new modeling and simulation research through a forum encouraging graphical presentation, demonstration, and active engagement among Winter Simulation Conference (WSC) participants. We are seeking outstanding extended abstracts (2 pages) submissions to be presented in a poster format at the conference. Competitive contributions will present interesting recent results, novel ideas or works-in-progress that are not quite ready for a regular full-length paper. Contributions from PhD students are particularly welcome. Submitted manuscripts should follow the standard template for WSC submission, and should not exceed the 2 pages limit. Extended abstract submissions are encouraged in all areas of modeling and simulation covered by WSC.<\/p> <p>Please refer to the <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/poster-sessions\/\">Poster Session page<\/a> for more information.<\/p>\n<h3>Professional Development<\/h3> <p><b>Track Coordinators: <\/b>Caroline Krejci (The University of Texas at Arlington), Nav Mustafee (University of Exeter)<\/p> <p>In this track, we explore the transformative role of the application of simulation and how it influences the professional sphere, emphasizing its capacity to enhance skills in communication, leadership, problem-solving, and more, while also showcasing its application across industries. This track invites a diverse dialogue, focusing on graduate students, challenges for women and underrepresented groups, early-career academics, and academia-industry partnerships. Through this lens, we aim to highlight how simulation can transcend traditional boundaries, empowering professionals to not only envision but realize the vast potential simulation, fostering a future where inspiration and simulation drive groundbreaking innovation.<\/p>\n<h3>Project Management and Construction<\/h3> <p><b>Track Coordinators: <\/b>Eric Du (University of Florida), Joseph Louis (Oregon State University)<\/p> <p>The Project Management and Construction track includes innovative research as well as practical application papers that apply computer simulation to complex project and construction management problems. Computer simulation encompasses a broad range of data-driven, quantitative methods including, but not limited to:<\/p> <ul> <li>Discrete event simulation<\/li> <li>Continuous simulation<\/li> <li>System dynamics<\/li> <li>Big data analytics<\/li> <li>Virtual\/Augmented reality<\/li> <li>Automation and robotics<\/li> <li>Emerging AI techniques<\/li> <\/ul> <p>Applications include, but are not limited to<\/p> <ul> <li>Complex project planning and scheduling<\/li> <li>Planning for integrated project delivery<\/li> <li>Construction safety planning<\/li> <li>Off-site production and modularization systems<\/li> <li>Site operations and layout planning<\/li> <li>Human behavior and organization modeling<\/li> <li>Sustainable built environment<\/li> <li>Simulation as a project management education tool<\/li> <li>Lean production systems<\/li> <li>Sensed environments for simulation<\/li> <li>Project portfolio management<\/li> <li>System optimization and control<\/li> <\/ul>\n<h3>Reliability Modeling and Simulation<\/h3> <p><b>Track Coordinators: <\/b>Sanja Lazarova-Molnar (Karlsruhe Institute of Technology), Olufemi Omitaomu (Oak Ridge National Lab), Li Xueping (University of Tennessee-Knoxville)<\/p> <p>Complex real-time systems need to have their dependability and reliability evaluated and addressed to ensure that systems perform satisfactorily despite the presence of faults. Systems\u2019 design decisions are also influenced by these evaluations. Simulation has often been utilized for this purpose. This track covers the use of modeling and simulation for the analysis of reliability and dependability of systems. Further relevant topics are listed as follows:<\/p> <ul> <li>data-driven reliability modeling<\/li> <li>simulation for optimizing repair and maintenance strategies<\/li> <li>reliability models for hardware and software<\/li> <li>reliability modeling of cyber-physical systems<\/li> <li>modeling and simulation of fault-tolerant systems<\/li> <li>fault models and fault abstraction<\/li> <li>reliability modeling formalisms<\/li> <li>predictive maintenance<\/li> <li>dependability analysis using simulation and experimental measurement<\/li> <li>prognostics &amp; health management<\/li> <li>case studies of using simulation for reliability analysis<\/li> <\/ul>\n<h3>Scientific AI and Applications<\/h3> <p><b>Track Coordinators: <\/b>Rafael Mayo-Garc\u00eda (CIEMAT), Esteban Mocskos (Universidad de Buenos Aires)<\/p> <p>The \u2018Scientific AI and Applications\u2019 track is focused on theory, experimentation, and engineering practices that form the basis for the design and use of simulation methodologies in science, including artificial intelligence and evolutionary algorithms. The objective of the track is to be a point of transversal communication in which methodologies, techniques, tools, and practical issues in any specific scientific domain can be extended and adopted by others.<\/p> <p>According to the general WSC 2025 motto, contributions addressing simulations tackling future challenges on any scientific and\/or technological field are very welcome. Hence, topics of interest include, but are not limited to:<\/p> <ul> <li>Application of Artificial Intelligence for solving simulated phenomena<\/li> <li>Applied simulation methodologies<\/li> <li>Challenges in performance evaluation of scientific applications<\/li> <li>Data-driven workflows<\/li> <li>Digital twins of scientific ecosystems and facilities<\/li> <li>Evolutionary algorithms applied in science<\/li> <li>Large-scale debugging and analysis tools<\/li> <li>Modeling tools and frameworks<\/li> <li>Networking technologies in scientific applications<\/li> <li>Scaling methodologies<\/li> <li>Scientific data retrieval, storage, and processing<\/li> <li>Successful use cases<\/li> <li>Support for the development of scientific applications<\/li> <li>Usage of new technologies and architectures<\/li> <\/ul>\n<h3>Simulation Around the World<\/h3> <p><b>Track Coordinator: <\/b>Mar\u00eda Julia Blas (INGAR CONICET-UTN), Stewart Robinson (Newcastle University)<\/p> <p>Click <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/simulation-around-the-world\/\">here<\/a> for more information.<\/p>\n<h3>Simulation and Artificial Intelligence<\/h3> <p><b>Track Coordinators: <\/b>Mohammad Dehghani\u00a0(Northeastern University), Edward Hua (MITRE Corporation), Yijie Peng (Peking University)<\/p> <p>The integration of simulation modeling and Artificial Intelligence (AI) has been highly successful over the past few years. Simulation modeling is an effective tool for analyzing the performance of complex stochastic systems, while Artificial Intelligence has demonstrated significant success in addressing decision-making challenges. The combination of both brings the best of both worlds, where simulation data, as a clean and processed dataset, enhances AI applications to unlock new opportunities. Major AI fields such as deep learning and reinforcement learning have played a crucial role in harnessing the potential of this integration. Additionally, recent advancements in generative AI (GenAI) are opening new avenues for innovation and creativity in simulation and decision-making processes.<\/p> <p>This track invites papers and presentations about state-of-the-art research at the intersection of simulation and AI. Potential topics include, but are not limited to:<\/p> <ul> <li>Simulation and AI methodologies<\/li> <li>AI for adaptive simulations<\/li> <li>Generative models for simulation<\/li> <li>GenAI-enabled simulations<\/li> <li>Large Language Models (LLMs) in simulation<\/li> <li>Simulation-based machine learning<\/li> <li>Simulation-based reinforcement learning<\/li> <li>AI-driven simulation modeling and optimization<\/li> <li>Applications of Simulation and AI<\/li> <li>Simulation and AI in parallel computing environment<\/li> <li>Simulation to train generative AI model<\/li> <li>Simulation for AI safety and ethics<\/li> <li>Multi-agent systems and simulation<\/li> <li>AI for simulation verification and validation (V&amp;V)<\/li> <li>Simulation for AI explainability<\/li> <li>AI-enhanced simulation for complex systems<\/li> <li>Human-AI interaction in simulated environments<\/li> <li>AI for predictive simulation<\/li> <li>Simulation in AI-driven robotics<\/li> <\/ul>\n<h3>Simulation as Digital Twin<\/h3> <p><strong>Track Coordinators:<\/strong> Haobin Li (National University of Singapore), Giovanni Lugaresi (KU Leuven), Jie Xu (George Mason University)<\/p> <p>Simulation is a relevant enabling technology for fully exploiting data streams to make fast predictions, quantifying continuous improvement actions and making smart decisions in real-time. This track is interested in research contributions on simulation theory and applications to support complex processes such as production, logistics, service delivery, etc. A particular interest is on models and algorithms to create, update, and keep synchronized simulation models with physical systems, as well as methodologies of simulation modelling, prediction, and optimization methods suitable for making intelligent decisions in real-time data stream environments. Data-driven approaches are also well aligned with the track being the enablers for skill-free technologies and the integration with artificial intelligence. Contributions describing physical and\/or digital laboratories to test new models, methods, and tools for simulation as digital twin are also appreciated. Topics include, but are not limited to, the following:<\/p> <ul> <li>Data-driven simulation modelling &amp; validation<\/li> <li>Production planning and control<\/li> <li>Real time simulation-based control<\/li> <li>Simulation-based optimization<\/li> <li>Simulation-based closed-loop controls<\/li> <li>On-line validation<\/li> <li>Physical-to-digital synchronization and alignment<\/li> <li>Cyber-physical systems<\/li> <li>Digital Twins<\/li> <li>Manufacturing<\/li> <li>De-manufacturing and circular economy<\/li> <li>Transportation and logistics<\/li> <li>Automated warehouses<\/li> <li>Operations and supply chains<\/li> <li>Maintenance<\/li> <li>Healthcare systems<\/li> <li>Traffic control<\/li> <li>Service systems<\/li> <\/ul>\n<h3>Simulation in Education<\/h3> <p><b>Track Coordinators: <\/b>Omar Ashour (Penn State University), Ashkan Negahban (Penn State University)<\/p> <p>The Simulation in Education track aims to share pedagogical, andragogical, and didactic approaches that employ or promote the use of simulation in education. These approaches could utilize technology-enhanced environments (e.g., video games, simulations, mixed reality, pervasive computing), non-technology environments\/approaches (e.g., Lego simulations and role-play), or a combination of both. The track is focused on educators and trainers who are interested in incorporating simulations into classrooms and\/or training programs to educate the next generation of scientists, engineers, healthcare professionals, artists, humanists, and social scientists.<\/p> <p>The track also covers teaching and learning methods for developing modeling and simulation skills. All learning delivery modes and situations are of interest, i.e., in-person vs remote learning, formal vs informal learning, K-12, higher education, professional training, continuing education, adult learning, etc.\u00a0\u00a0\u00a0<\/p> <p>The track seeks work from all disciplines including but not limited to engineering, science, medicine, arts, humanities, and social sciences. The sessions in this track will cover a wide range of topics including but not limited to:<\/p> <ul> <li>The design and development of simulation methods<\/li> <li>Teaching, learning, and assessment approaches for simulation education<\/li> <li>Use of technology and immersive environments in education (virtual reality, augmented reality, mixed reality, video games, artificial intelligence, etc.)<\/li> <li>Best practices and case studies of using simulation in education<\/li> <li>New tools and techniques for using simulation in education<\/li> <li>Challenges in implementing simulation in education<\/li> <li>Hands-on workshops or demonstrations of simulation software packages<\/li> <li>Simulation-based and experiential learning<\/li> <\/ul>\n<h3>Simulation in Space (NEW)<\/h3> <p><b>Track Coordinators: <\/b>Maziar Ghorbani (Brunel University of London), Anastasia Anagnostou (Brunel University of London)<\/p> <p>Simulation has been widely used in Space systems to analyse and validate new approaches and equipment, and to train people in their use. Simulation is a critical technique in Space research as the target environments cannot be easily reached. With new and ambitious space programs being developed, new challenges and uses of simulation are needed. The Simulation in Space Track aims to showcase the use of simulation across Space agencies and industries as well as new innovations in simulation to support these. We invite original contributions from academic institutions, space agencies and industries that present the results of simulation research, innovation and application in Space. Areas of interest include, but are not limited to:<\/p> <ul> <li>Satellite simulation<\/li> <li>Commercial space travel<\/li> <li>Space station simulation<\/li> <li>Space tourism<\/li> <li>Lunar habitation<\/li> <li>Internet of Space<\/li> <li>Lunar Internet simulation<\/li> <li>Interplanetary Internet simulation<\/li> <li>Space Simulation Standards<\/li> <\/ul>\n<h3>Simulation Optimization<\/h3> <p><b>Track Coordinators: <\/b>David Eckman (Texas A&amp;M University), Siyang Gao (City University of Hong Kong), Yuwei Zhou (Indiana University)<\/p> <p>The Simulation Optimization track focuses on the design, analysis, and application of algorithms that can be coupled with computer simulations to identify decision variable values that optimize one or more simulation performance measures of interest. This track is interested in papers on the development of new algorithms accompanied by analysis of the algorithm\u2019s theoretical, empirical, and computational performance. The track also welcomes papers that compare existing simulation-optimization algorithms or apply them to real-world problems. Topics of interest include, but are not limited to:<\/p> <ul> <li>Global and black-box optimization<\/li> <li>Simheuristics<\/li> <li>Metaheuristics<\/li> <li>Discrete optimization via simulation<\/li> <li>Ranking and selection<\/li> <li>Stochastic programming<\/li> <li>Sample average approximation<\/li> <li>Stochastic approximation methods<\/li> <li>Metamodel-based methods<\/li> <li>Multi-objective optimization<\/li> <li>Optimization with stochastic constraints<\/li> <li>Approximate dynamic programming and reinforcement learning<\/li> <li>Optimal learning<\/li> <li>Active learning<\/li> <li>Multi-armed bandit methods<\/li> <\/ul>\n<h3>Uncertainty Quantification &amp; Robust Simulation<\/h3> <p><b>Track Coordinators: <\/b>Ilya Ryzhov (University of Maryland), Wei Xie (Northeastern University)<\/p> <p>The Uncertainty Quantification and Robust Simulation track aims to cover mathematical, statistical, algorithmic, and application advances in uncertainty quantification and robust simulation, which facilitate characterization, quantification, and management of various sources of uncertainty inherent in the use of simulation models to guide optimal design and control for complex stochastic systems. The sources of uncertainty include the observation errors in real-world data sets used to improve the model fidelity of digital twins, calibrate the input and state transition models, and validate the simulation model, structural uncertainty, numerical uncertainty, etc. These uncertainties can impact, e.g., simulation-based predictive accuracy, simulation optimization, sensitivity analysis, optimal learning, and feasibility assessment in various ways and to different extents. Papers investigating various sources of uncertainty and their impacts that are broadly defined are welcome. Contributions can include the development of quantification criteria, novel statistical or mathematical methods to assess the impacts of different sources of uncertainty or errors, the efficiency analyses or improvements of existing methods, and applications of these methods in different domain contexts. Topics of interest include, but are not limited to:<\/p> <ul> <li>Input uncertainty quantification criteria and methods<\/li> <li>Robustness in input modeling and selection<\/li> <li>Model risk quantification and reduction<\/li> <li>Model calibration and validation<\/li> <li>Data assimilation<\/li> <li>Risk-sensitive simulation optimization<\/li> <li>Robustness against model misspecifications in simulation logic and state transition<\/li> <li>Sensitivity analysis<\/li> <li>Optimal learning and data collection<\/li> <\/ul>\n<h3>Vendor<\/h3> <p><b>Track Coordinators: <\/b>Amy Brown Greer (MOSIMTEC LLC)<\/p> <p>In addition to the Sunday Workshops, exhibitors can participate in the Vendor Track at WSC. The Vendor Track provides an opportunity for companies that market modeling and simulation technology and services, or related services (e.g., statistical analyses) to present their innovations and successful applications.<\/p> <p>For each slot in the Vendor Track, vendors should submit a 2-page Extended Abstract.\u00a0 Extended Abstracts appear online and in the final program, but neither appear in the archival proceedings (due to IEEE rules). Extended Abstracts are reviewed by the track coordinators and may entail revisions. Extended Abstracts must use the Authors Kit to adhere to the publication format requirements.<\/p> <p>For more details on participating as an exhibitor, click <a href=\"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/exhibitors\/\">here<\/a>.<\/p> <p style=\"font-variant-caps: normal; orphans: auto; text-align: start; widows: auto; word-spacing: 0px;\">\u00a0<\/p>","_links":{"self":[{"href":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/wp-json\/wp\/v2\/pages\/4365","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/wp-json\/wp\/v2\/users\/1001077"}],"replies":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/wp-json\/wp\/v2\/comments?post=4365"}],"version-history":[{"count":395,"href":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/wp-json\/wp\/v2\/pages\/4365\/revisions"}],"predecessor-version":[{"id":11131,"href":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/wp-json\/wp\/v2\/pages\/4365\/revisions\/11131"}],"wp:attachment":[{"href":"https:\/\/meetings.informs.org\/wordpress\/wsc2025\/wp-json\/wp\/v2\/media?parent=4365"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}