Tracks

Advanced Tutorials

Track Coordinators: Javier Faulin (Public University of Navarre), Giulia Pedrielli (Arizona State University)

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.

Agent-Based Simulation

Track Coordinators: Andrew Collins (Old Dominion University), Martijn Mes (University of Twente), Zhou He (University of Chinese Academy of Sciences) 

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.

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.

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.

Analysis Methodology

Track Coordinators: Dohyun Ahn (Chinese University of Hong Kong), Sara Shashaani (North Carolina State University), Jun Luo (Shanghai Jiao Tong University)

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:

  • Simulation experiment design
  • Input modeling and output analysis
  • Risk and uncertainty quantification
  • Sensitivity analysis
  • Variance reduction techniques
  • Rare-event simulation
  • Improving algorithmic efficiency
  • Metamodeling
  • Simulation model validation and calibration

Aviation Modeling and Analysis

Track Coordinators: Maurizio Tomasella (University of Edinburgh), Silvia Padrón Astorga (TBS Education)

In the last fifth of its one-century history, commercial aviation has developed into a globalized networked system of unprecedented complexity, one which more and more tightly couples organizations as diverse as airlines, airport operators, air navigation service providers, airframe and engine manufacturers together with their supply chains, a myriad of other kinds of service providers (airport ground handling, aircraft and other leasing service providers, etc.), chambers of commerce, governmental agencies (local, national, regional, global), and several standardization bodies and associations representing various subsets of stakeholders (ICAO, IATA, ACI, ATAG, etc.).

The world’s air transportation system is safer and more secure today than it has ever been, as well as quickly improving its environmental sustainability. However, the complexity of developing, operating and managing such an intricate network is a daunting task for anyone involved. It poses fundamental questions as to how to ensure operational performance and resilience of the overall system, or at least relevant, large sections of it. To unravel the tangle, a more thorough understanding ought to be developed of who is accountable for and who is in control of the various pockets of operational performance (or lack thereof) that should be involved in enhancing overall services to customers. In a truly globalized aviation industry where no ‘overall cop’ exists, the worldwide blame-game that takes place, on a daily basis, among the involved organizations can perhaps minimize compensations to be paid to let-down customers but surely cannot help with problem solving. This is where simulation steps in!

If you are from, collaborate with, or your research is relevant to any of the organizations mentioned above, and employ simulation in any of its forms, whether on its own or combined with other techniques, to tackle challenges or develop solutions to any aspect of aviation operations, this track is for you!

 

Complex, Resilient and Generative Systems

Track Coordinators:  Saurabh Mittal (MITRE Corporation), Claudia Szabo (University of Adelaide)

The increasing integration of the Internet of Things (IoT) technologies emphasize that heterogeneous systems are the norm today. Emerging technologies like Generative Artificial Intelligent (Gen-AI) and its various instantiations such GPT-3, GPT-4, etc., are being integrated into legacy systems across various domains. A system deployed in such an environment eventually becomes a part of a larger system of systems (SoS). Gen-AI-enabled SoS will include a generative aspect in the sense that the system is continuously learning and evolving. This SoS further incorporates adaptive and autonomous elements (systems that have different levels of autonomy and situated behavior) turning the SoS into a complex adaptive system (CAS). This makes design, analysis, and testing for the system-at-hand a complex endeavor itself. Testing in isolation is not the same as a real-system operation, since the system’s behavior is also determined by input, which evolves from the environment. This exact factor is difficult to predict, due to an ever-increasing level of autonomy and environment complexity. Advanced Modeling and Simulation (M&S) frameworks are required to facilitate CAS design, development, testing, and integration. These frameworks must provide methods to deal with intelligent, emergent, adaptive, generative and resilient behavior that encompasses autonomy. The subject of emergent, generative and resilient behavior, and M&S of such behaviors takes the center stage in such systems as it is unknown how a system responds in the face of such behaviors arising out of interactions with other complex systems.

This track is focused on the modeling, simulation, and validation and verification of complex, adaptive, generative and resilient systems and how they handle faults, system issues, and emergent behaviors. This track has two objectives.

The first objective aims to focus on M&S of the following aspects of complex SoS engineering with a focus on resilient and generative systems, and brings researchers, developers and industry practitioners working in the areas of complex, adaptive, autonomous, generative and resilient SoS engineering. This objective covers the following topics, but not limited to:

  • Theory for intelligence-based, adaptive, complex, generative and resilient systems
  • Computational intelligence and cognitive systems engineering approaches impacting resilience and inclusion of Gen-AI-enabled systems
  • Human-in/on/with/out-of-the-loop systems
  • M&S Frameworks for adaptive, generative and resilient behavior
  • Methodologies, tools, and architectures for adaptive control systems / Cyber-physical systems
  • Knowledge engineering, generation, and management
  • Weak and Strong emergent behavior, Emergent Engineering
  • Generative system structure and impact on SoS behaviors
  • Complex adaptive systems engineering
  • Self-* (organization, explanation, configuration) capability and generative behavior
  • Applications to robotics, unmanned systems, swarm technology, semantic web technology, and multi-agent systems
  • Live, Virtual and Constructive (LVC) environments
  • Modeling, engineering, testing and verification of complex, generative and resilient behaviors
  • Development and testing of complex, generative and distributed systems
  • Modeling, simulating, and testing IoT environments and applications

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. Generative, emergent and resilient behavior is an outcome of this complexity. Understanding this complexity will provide a foundation for resilient and generative systems, and the M&S thereof. Topics related to this objective include, but are not limited to handling of:

  • Complexity in Structure: network, hierarchical, small-world, flat, etc.
  • Complexity in Behavior: Micro and macro behaviors, local and global behaviors, teleologic and epistemological behaviors
  • Complexity in Knowledge: ontology design, ontology-driven modeling, ontology-evaluation, ontology transformation, etc.
  • Complexity in Human-in-the-loop: artificial agents, cognitive agents, multi-agents, man-in-loop, human-computer-interaction
  • Complexity in Human-on-the-loop: trust modeling, human-machine-interaction
  • Complexity in resilience-based systems: Situated behavior, knowledge-based behavior, resource-constrained systems, energy-aware systems
  • Complexity in adaptation and autonomy
  • Complexity in generative structure and behavior modeling
  • Complexity in architecture: Flat, full-mesh, hierarchical, adaptive, swarm, transformative
  • Complexity in awareness: Self-* (organization, explanation, configuration)
  • Complexity in interactions: collaboration, negotiation, greedy, rule-based, environment-based, etc.
  • Complexity in LVC environments
  • Complexity in artificial systems, social systems, techno-economic-social systems
  • Complexity in model engineering of complex and resilient SoS
  • Complexity in model specification using modeling languages and architecture frameworks such as UML, PetriNets, SysML, DoDAF, MoDAF, UAF, etc.
  • Complexity in simulation infrastructure engineering: distributed simulation, parallel simulation, cloud simulation, netcentric parallel distributed environments
  • Complexity in Testing and Evaluation (T&E) tools for SoS engineering
  • Complexity in Heterogeneity: Hardware/Software Co-design, Hardware in the Loop, Cyber-Physical Systems, the Internet of Things, Gen-AI-enabled systems
  • Metrics for Complexity design and evaluation
  • Impact of cybersecurity processes on CAS engineering
  • Complexity in Verification, validation, and accreditation in SoS and CAS
  • Complexity of Application in domain model engineering: Financial, Power, Robotics, Swarm, Economic, Policy, etc.
  • Complexity in SoS and CAS failure

Data Science and Simulation

Track Coordinators: Abdolreza Abhari (Toronto Metropolitan University), Cheng-Bang Chen (University of Miami), Niclas Feldkamp (TU Ilmenau)

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:

  • Big Data in simulation
  • Data analytics for simulation
  • Machine learning methods to generate synthetic data
  • Simulating massive data processing systems
  • Digital Twin Simulations
  • Machine learning and data mining in modeling and simulation
  • Data-Driven techniques for modeling and simulation
  • Deep learning in modeling and simulation
  • Hybrid approaches of combining simulation and machine Learning
  • Simulation initialization techniques using big data
  • Big data management/processing techniques for simulations
  • Ontologies for big data modeling
  • Data science pipelines for modeling for simulations
  • Data science and social networks in simulation
  • Simulation of cloud computing and distributed systems

Healthcare and Life Sciences

Track Coordinators: Lucy Morgan (NHS), Kathy Kotiadis (University of Kent), Paul Harper (Cardiff University)

The Healthcare and Life Sciences track embraces a range of simulation methodologies that focus on advancing knowledge, innovation, and practical applications in medicine and healthcare systems.   We welcome a wide range of contributions such as those using simulation to support improved decision making or policy formulation, address conceptualization or implementation challenges, or consider model reuse and reproducibility. Topics include, but are not limited to, the following:

  • Admissions and control
  • Ancillary services
  • Appointment scheduling
  • Emergency room access
  • Epidemic and pandemic modeling
  • General healthcare simulation
  • Global Health
  • Healthcare optimization
  • Healthcare systems
  • Medical decision making
  • Outpatient access
  • Outpatient capacity analysis
  • Payment/Payer models
  • Performance improvement models
  • Pricing models
  • Resource scheduling (e.g., nurse, doctor, anaesthesiologist, residents, equipment, etc.)

Hybrid Modeling & Simulation

Track Coordinators: Masoud Fakhimi (University of Surrey), Anastasia Anagnostou (Brunel University London) and Wael Rashwan (Maynooth University)

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&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.

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&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.

Topics include, but are not limited to, the following:

  • Literature reviews in hybrid simulation (HS) and hybrid modeling (HM)
  • Taxonomies and conceptualizations of HS and HM
  • Methodology focusing on the development of frameworks and modeling formalisms to support hybrid M&S
  • Case studies on hybrid M&S in domains such as healthcare, manufacturing, supply chain, circular economy and sustainability, defense and policy.
  • Technical papers on the development of software artefacts for supporting hybrid M&S.
  • Papers on validation and verification of hybrid models.
  • Application of hybrid simulation in behavioral studies (commonly referred to as Behavioral Operations Research).
  • Digital twins whose implementation includes multiple computing artefacts, e.g., simulation with machine learning, or which require integration with real-time systems using approaches commonly found in industrial engineering, computer science and software engineering.
  • Hybrid models that include both data-driven learning (e.g., supervised and unsupervised learning, reinforcement learning, process mining) with computer simulation.
  • 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).
  • Hybrid M&S applications in Industry 5.0, robotics, and human–machine collaboration.
  • Explainable AI techniques used to support interpretation, transparency, or decision-making in hybrid M&S studies.

Industry Case Studies

Track Coordinator: Saurabh Parakh (MOSIMTEC), Amy Greer (MOSIMTEC)

Many busy professionals have done important work that others would like to see, but don’t have the time and resources required to submit a full paper and make early commitments to participate. If this describes you, then this track is designed specifically for you.

The Industry Case Studies Track serves as a multidisciplinary forum for commercial simulation practitioners to share what they have learned modeling real world problems. The applications are open to all areas including, but not limited to:

  • Manufacturing
  • Logistics and distribution
  • Healthcare
  • Mining
  • Social and human behavior
  • Aerospace
  • Food services
  • Military
  • Data analytics
  • Standard implementations
  • Supply chain
  • Digital transformation

The track will consist of 25-minute presentations plus 5 minutes for questions and answers. Each presentation should focus on a specific problem where simulation was utilized to conduct an analysis and provide recommendations for potential solutions.

No full length paper is necessary — only a 150 word abstract and a two-page extended abstract is required to be submitted for consideration. The extended abstract should describe the problem, the simulation methods used, the results, and the impact/benefits of the project. Those case studies selected for presentation at WSC will have their abstract appear on the WSC Archive website to share what they have learned modeling real world problems. The submission deadlines are also much more relaxed than for full papers – submissions are in August with final editing in September (see the official Call for Papers for details).

Introductory Tutorials

Track Coordinators: Canan Gunes Corlu (Boston University), Angel A. Juan (Universitat Politècnica de València)

The Introductory Tutorials track is oriented toward professionals in modeling and simulation who wish to broaden, refresh, and expand their knowledge of the field. The track covers a wide range of topics including mathematical, statistical, and computational foundations, methods, application fields, software tools, and analysis tools. Tutorials may also introduce the hybridization of simulation with areas such as artificial intelligence, optimization, data analytics, and machine learning, showing how these approaches can extend modeling and simulation potential and applications. In addition, the track encourages accessible presentations of new research directions and innovative techniques in simulation.

Logistics, Supply Chain Management, Transportation

Track Coordinators: Hendrik van der Valk (Technische Universität Dortmund), Majsa Ammouriova (German Jordanian University), David Goldsman (Georgia Institute of Technology), Joachim Hunter (Fraunhofer ISST)

The nature of today’s 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:

  • Literature reviews in hybrid simulation (HS) and hybrid modeling (HM)
  • Taxonomies and conceptualizations of HS and HM
  • Methodology focusing on the development of frameworks and modeling formalisms to support hybrid M&S
  • Case studies on hybrid M&S in domains such as healthcare, manufacturing, supply chain, circular economy and sustainability, defense and policy.
  • Technical papers on the development of software artefacts for supporting hybrid M&S.
  • Papers on validation and verification of hybrid models.
  • Application of hybrid simulation in behavioral studies (commonly referred to as Behavioral Operations Research).
  • Digital twins whose implementation includes multiple computing artefacts, e.g., simulation with machine learning, or which require integration with real-time systems using approaches commonly found in industrial engineering, computer science and software engineering.
  • Hybrid models that include both data-driven learning (e.g., supervised and unsupervised learning, reinforcement learning, process mining) with computer simulation.
  • 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).
  • Hybrid M&S applications in Industry 5.0, robotics, and human–machine collaboration.
  • Explainable AI techniques used to support interpretation, transparency, or decision-making in hybrid M&S studies.

Manufacturing & Industry 4.0

Track Coordinators: Alp Akcay (Eindhoven University of Technology), Christoph Laroque (University of Applied Sciences Zwickau), Gordon Shao (NIST)

Simulation is a commonly used methodology to analyze dynamic interdependencies within manufacturing systems. The Manufacturing & Industry 4.0 Applications track invites original research contributions that apply simulation to address real-world challenges in industrial domains including sectors such as automotive, aerospace, and shipbuilding, among others. These applications include the analysis of (i) production and logistics processes within a company or across the supply chain, (ii) all phases of a system’s life cycle, including acquisition, design and planning, implementation, start of operation and ramp-up, as well as the operations itself.

Contributions should clearly outline the research objectives, the system under study, the simulation model, the experimental design, key findings, and any implementation results. Authors are encouraged to highlight specific challenges such as system complexity, data collection and preparation, verification and validation.

Relevant topics include, but are not limited to:

  • Manufacturing systems and processes
  • Applications of simulation-based optimization in production
  • Cyber-physical systems and Industry 4.0 / Industry 5.0
  • Industrial IoT (IIoT) and connected manufacturing
  • Intelligent automation and robotics for manufacturing
  • Production planning and scheduling
  • Human–machine collaboration in production systems
  • Lean management practices
  • Total Quality Management (TQM)
  • Maintenance strategies and lifecycle engineering
  • Integration of energy efficiency and carbon footprint analysis
  • Digital twins for manufacturing and production systems

Military and National Security 

Track Coordinators:Oliver Rose (Universität der Bundeswehr München), TBC

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.

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!

Modeling & Analysis of Semiconductor Manufacturing (MASM)

Track Coordinators:  John W. Fowler (Arizona State University), Lars Mönch (University of Hagen), TBC

Description to come.

Modeling Methodology

Track Coordinators: Rodrigo Castro (University of Buenos Aires), Andrea D’Ambrogio (University of Rome Tor Vergata), Gabriel Wainer (Carleton University)

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:

  • Modeling paradigms
  • Formal modeling languages
  • Modeling approaches for real-time systems
  • Technological advances in modeling software
  • Spatial and temporal modeling
  • Multilevel modeling
  • Multi-paradigm modeling
  • Multi-formalism modeling
  • Model reuse, repositories, and retrieval
  • Parallel and Distributed simulation
  • Modeling with ontologies
  • Semantic tools supporting modeling methods
  • Standardization challenges
  • Modeling and Simulation for Cyber-Physical Systems

PhD Colloquium

Chair: Eunhye Song (Georgia Institute of Technology)
Members: Cristina Ruiz Martin (Carleton University), Alison Harper (University of Exeter), Laura Boyle (Queen’s University Belfast)

In 2026, 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 2028). 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.

Poster Session

Track Coordinators: Le Khanh Ngan Nguyen (University of Strathclyde), Hanane El Raoui (University of Strathclyde)

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.

Professional Development

Track Coordinators: Philippe J. Giabbanelli (Old Dominion University), Laura Boyle (Queen’s University Belfast), Luke Rhodes-Leaders (Lancaster University)

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.

 

Project Management and Construction

Track Coordinators: Magesh Nagarajan (Indian Institute of Technology Jodhpur), Mohammed Al-Mhdawi (Teesside University)

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. With growing adoption of digital technologies and associated data sources, there is potential for Computer simulation in various stages of project management and construction to support planners and decision makers. Computer simulation encompasses a broad range of data-driven, quantitative methods including, but not limited to:

  • Discrete event simulation
  • Continuous simulation
  • System dynamics
  • Bayesian networks
  • Fuzzy set theory
  • Big data analytics
  • Virtual/Augmented reality
  • Automation and robotics

Applications include, but are not limited to

  • Complex project planning and scheduling
  • Planning for integrated project delivery
  • Construction safety planning
  • Construction risk management
  • Off-site production and modularization systems
  • Site operations and layout planning
  • Human behaviour and organization modelling
  • Smart and Sustainable built environment
  • Simulation as a project management education tool
  • Lean production systems
  • Sensed environments for simulation
  • Project portfolio management
  • Programme management
  • System optimization and control

Reliability Modeling and Simulation

Track Coordinators: Sanja Lazarova-Molnar (Karlsruhe Institute of Technology), Olufemi Omitaomu (Oak Ridge National Lab), Li Xueping (University of Tennessee-Knoxville)

Ensuring dependable performance in complex, real-time systems requires rigorous evaluation of reliability, fault behavior, and system resilience. Modeling and simulation provide essential tools for exploring fault scenarios, assessing design trade-offs, and optimizing maintenance and operational strategies. This track focuses on methodological advances and applications that leverage simulation, analytics, and computational frameworks to understand and improve the reliability of hardware, software, and cyber-physical systems. Topics include, but not limited to:

  • Data-driven and AI-assisted reliability modeling
  • Simulation-based optimization of repair and maintenance strategies
  • Reliability models for hardware, software, and integrated systems
  • Reliability and resilience modeling for cyber-physical and IoT systems
  • Modeling and simulation of fault-tolerant architectures
  • Fault models, abstraction techniques, and uncertainty quantification
  • Reliability modeling formalisms and stochastic methods
  • Predictive maintenance and condition-based monitoring
  • Dependability analysis using simulation or experimental measurement
  • Prognostics and health management
  • Digital twins for reliability assessment
  • Case studies of simulation-driven reliability engineering

Simulation and Artificial Intelligence

Track Coordinators: Mohammad Dehghani (Northeastern University), Edward Hua (MITRE Corporation), Sahil Belsare (Walmart)

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. This track welcomes all AI-related work in simulation, including traditional AI approaches such as machine learning, neural networks, deep learning, reinforcement learning, predictive modeling, and optimization algorithms, as well as recent advances in generative AI (GenAI) and Large Language Models (LLMs). These diverse AI technologies are transforming how we create, deploy, and interact with simulation models, enabling everything from automated model generation to intelligent decision support. Furthermore, the emergence of agentic AI systems capable of autonomous decision-making and self-directed learning represents a paradigm shift in simulation applications, where AI agents can independently plan, execute, and adapt simulation experiments to achieve specified goals.

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:

  • GenAI-enabled simulations
  • Large Language Models (LLMs) for simulation code generation and documentation
  • Model Context Protocol (MCP) in simulation modeling
  • GenAI for automated scenario generation and testing
  • Natural language interfaces for simulation control
  • GenAI for simulation metamodeling
  • Prompt engineering for simulation applications
  • Multi-agent systems and simulation
  • Agentic AI for autonomous simulation experiments
  • Neural networks and deep learning in simulation
  • Predictive models using simulation data
  • Simulation-based machine learning
  • Simulation-based reinforcement learning
  • Reinforcement learning environments and simulation testbeds
  • AI-driven simulation modeling and optimization
  • AI for adaptive simulations
  • AI for predictive simulation
  • AI-enabled simulation software packages and frameworks
  • Simulation and AI in parallel computing environment
  • Simulation for AI safety and ethics
  • AI for simulation verification and validation (V&V)
  • Simulation for AI explainability
  • AI-enhanced simulation for complex systems
  • Human-AI interaction in simulated environments
  • Digital twins with embedded AI
  • Simulation in AI-driven robotics
  • Applications of Simulation and AI

Simulation and Quantum Computing

Track Coordinators: Deniz Cetinkaya (Bournemouth University), Stephen John Turner (Vidyasirimedhi Institute of Science and Technology)

Quantum computing exploits the principles of quantum mechanics, such as superposition, entanglement, and interference, to process information in fundamentally new ways. By combining the rich representational power of quantum states with the possibility of exponential parallelism, quantum computing has the potential to solve problems that are too complex for classical computers. We have recently seen huge advancements in quantum technology, and quantum computers are now poised to play a significant role in many aspects of science, technology, commerce and industry.

This track aims to bring together researchers and practitioners working at the intersection of quantum computing, modeling, and simulation. We welcome contributions that advance the understanding, design, implementation, and application of quantum computing techniques for simulation across diverse domains, as well as applications of modelling and simulation in the field of quantum computing. Contributions of particular interest include the use of quantum-enhanced simulation in application areas such as business, finance, logistics, manufacturing, defense, education, health, and energy. Authors are invited to submit original research papers, including case studies, applications and reviews.

Topics of interest include, but are not limited to:

  • Quantum simulation techniques
  • Modeling and simulation of quantum information
  • Quantum algorithms for simulation
  • Simulation-based quantum optimization
  • Simulation and quantum machine learning
  • Simulation and quantum AI
  • Quantum agents and simulation
  • Quantum simulation of complex systems
  • Hybrid quantum–classical simulation approaches
  • Simulation and visualization for quantum education
  • Tools for quantum simulation and emulation
  • Quantum Hamiltonian simulation
  • Quantum Monte Carlo methods
  • Quantum Markov Decision Processes
  • Quantum metaheuristics for simulation
  • Simulation for quantum performance evaluation
  • Simulation of quantum resource management and scheduling
  • Simulation of quantum networks
  • Quantum digital twins

Simulation Around the World

Track Coordinator: María Julia Blas (INGAR CONICET-UTN), Rafael Mayo-García (CIEMAT)

Simulation Around the World is a hybrid track whose purpose is to broaden participation among three key categories of people:

  • Expand access to simulation researchers and practitioners residing in geographical regions that are historically underrepresented in WSC (see list of regions/countries in [1]);
  • Enable participation from regions where travel is prohibited or challenging, e.g. due to conflict or natural disaster; and
  • Enable participation by people unable to travel due to personal reasons. (Personal reasons include, but are not limited to, inability to obtain a visa, caring responsibilities, bereavement, or personal or family healthcare concerns.)

Since space is limited, all people anticipating difficulty traveling to WSC according to A, B or C are encouraged to submit to this track.

[1] Eligible countries include*:

Countries on the African continent.

Asia: Afghanistan, Bangladesh, Bhutan, Brunei, Cambodia, East Timor, Indonesia, Kazakhstan, Kyrgyzstan, Laos, Mongolia, Myanmar, Nepal, Pakistan, Philippines, Sri Lanka, Tajikistan, Thailand, Turkmenistan, Uzbekistan, Vietnam.

America: Argentina, Bolivia, Brazil, Caribbean, Chile, Colombia, Costa Rica, Ecuador, El Salvador, French Guiana, Guatemala, Guyana, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Suriname, Uruguay, Venezuela.

Middle East/Western Asia: Armenia, Azerbaijan, Georgia, Iran, Iraq, Jordan, Lebanon, Oman, Palestine, Qatar, Syria, Yemen.

* If you reside in a country other than those listed which you believe should be on the eligible list, please provide a brief case with your submission.

If a presenter accepted to the SATW track unexpectedly becomes able to attend the regular conference in-person, they may continue to participate in the track but will need to pay the in-person registration fee.

Simulation for Climate Resilience

Track Coordinators: Albert Chen (University of Exeter), Nav Mustafee (University of Exeter) and Jiaqi Ge (University of Leeds)

Climate resilience refers to the capacity of a system to anticipate, prepare for, respond to, and recover from the adverse impacts of climate change. Such systems include social and institutional structures, as well as operational networks such as transportation infrastructure, healthcare systems, and supply chains, all of which are increasingly vulnerable to climate-related hazards such as extreme weather events, flooding, heatwaves, and droughts. Simulation provides a powerful means of modeling complex systems under future climate scenarios, enabling researchers and practitioners to test adaptive strategies, assess system vulnerabilities, and plan for effective response and recovery. We solicit papers presenting new ideas, concepts, models, methods, tools, standards, and applications to achieve sustainability and resiliency. Some of the topics related to simulation for climate resilience include:

  • Modelling and analysis of climate impacts on infrastructure, ecosystems, and supply chains
  • Simulation-based decision support for adaptation and mitigation strategies
  • Risk assessment and scenario planning under climate uncertainty
  • Integration of data analytics, AI, and simulation for resilient system design
  • Digital twins that enhance climate resilience
  • Agent-based modelling of adaptation and mitigation strategies
  • Environmental modelling, visualization, and optimisation
  • Environmental risk assessment and mitigation
  • Human adaptation to climate
  • Climate hazards and their impact on communities and engineered systems

The track also welcomes papers on environment and sustainability. Application areas include infrastructure systems, ecological systems, renewable resources, transportation, buildings, farming, manufacturing, and urban/city science. Possible topics include, but are not limited to:

  • Triple Bottom line of sustainability and the Circular Economy
  • Sustainability supply chains
  • Decision support and analytics for sustainability
  • Smart, connected, and resilient infrastructure
  • Social dynamics and policy innovations of managing sustainable infrastructure systems
  • Simulation and network modelling for resilience assessment
  • Energy/resource-efficient manufacturing
  • Smart and resilient grids
  • Sustainable urban planning and design
  • Human-environment interaction

Simulation in Digital Twins

Track Coordinators: Giovanni Lugaresi (KU Leuven, Belgium), Guodong Shao (NIST, US), Haobin Li (NUS, Singapore)

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 the physical systems these models represent, 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 and application areas include, but are not limited to, the following:

  • Data-driven simulation modelling & validation
  • Real time simulation-based control
  • Simulation-based closed-loop controls
  • On-line validation of simulation and digital twins
  • Relevant standards and implementations
  • Physical-to-digital synchronization and alignment
  • Cyber-physical systems
  • Digital Twin frameworks and software architectures
  • Manufacturing
  • Production planning and control
  • De-manufacturing and circular economy
  • Transportation and logistics
  • Automated warehouses
  • Operations and supply chains
  • Maintenance
  • Healthcare systems
  • Traffic control
  • Service systems Data-driven simulation modeling & validation

Simulation in Education

Track Coordinators: Korina Katsaliaki (International Hellenic University), Theresa M K Roeder (San Francisco State University)

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, and AI-driven learning systems enabling adaptive interaction), 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.

 

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.   

 

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:

 

  • The design and development of simulation methods
  • Teaching, learning, and assessment approaches for simulation education
  • Use of technology and immersive environments in education (virtual reality, augmented reality, mixed reality, video games, artificial intelligence, etc.)
  • Best practices and case studies of using simulation in education
  • New tools and techniques for using simulation in education
  • Challenges in implementing simulation in education
  • Hands-on workshops or demonstrations of simulation software packages
  • Simulation-based and experiential learning

 

 

Simulation Optimization

Track Coordinators: David Eckman (Texas A&M University), Siyang Gao (City University of Hong Kong), Luke Rhodes-Leader (Lancaster University)

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’s theoretical, empirical, and computational performance. The track also welcomes papers that compare existing simulation-optimization algorithms or apply them to real-world problems in innovative ways. Topics of interest include, but are not limited to:

  • Global and black-box optimization
  • Simheuristics
  • Metaheuristics
  • Discrete optimization via simulation
  • Ranking and selection
  • Stochastic programming
  • Sample average approximation
  • Stochastic approximation methods
  • Metamodel-based methods
  • Multi-objective optimization
  • Optimization with stochastic constraints
  • Approximate dynamic programming and reinforcement learning
  • Optimal learning
  • Active learning
  • Multi-armed bandit methods

Simulation Optimization

Track Coordinators: David Eckman (Texas A&M University), Siyang Gao (City University of Hong Kong), Luke Rhodes-Leader (Lancaster University)

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’s theoretical, empirical, and computational performance. The track also welcomes papers that compare existing simulation-optimization algorithms or apply them to real-world problems in innovative ways. Topics of interest include, but are not limited to:

  • Global and black-box optimization
  • Simheuristics
  • Metaheuristics
  • Discrete optimization via simulation
  • Ranking and selection
  • Stochastic programming
  • Sample average approximation
  • Stochastic approximation methods
  • Metamodel-based methods
  • Multi-objective optimization
  • Optimization with stochastic constraints
  • Approximate dynamic programming and reinforcement learning
  • Optimal learning
  • Active learning
  • Multi-armed bandit methods

System Dynamics

Track Coordinators:  Steffen Bayer (University of Southampton), Martin Kunc (University of Southampton)

The System Dynamics (SD) track invites contributions that explore the modeling and simulation of complex systems characterized by feedback loops, time delays, and non-linear relationships. SD continues to be a powerful tool for understanding dynamic behavior in domains such as public policy, healthcare, business strategy, environmental systems, and education.

This track welcomes papers that advance both the theory and practice of System Dynamics, with a special emphasis on recent innovations that are reshaping the field. Topics of interest include:

  • Conceptual modeling and causal loop diagramming
  • Stock and flow modeling techniques
  • Model calibration, validation, and sensitivity analysis
  • Policy design and scenario testing
  • Integration with other paradigms (e.g., agent-based modeling, discrete-event simulation)
  • Participatory modeling and stakeholder engagement
  • Teaching and learning with SD
  • Recent Advancements in System Dynamics

We especially encourage submissions that reflect the latest developments, including:

  • AI-assisted modeling: Use of machine learning and natural language processing to automate causal loop extraction, model generation, and scenario analysis.
  • Hybrid modeling frameworks: Integration of SD with deep learning and neural state-space models to enhance predictive capabilities while maintaining interpretability.
  • Digital twins and real-time simulation: Application of SD in digital twin environments for real-time decision support and system monitoring.
  • Explainable AI in SD: Development of interpretable neural system dynamics models that combine causal insights with scalable data-driven approaches.
  • Cloud-based and collaborative modeling platforms: Tools that support distributed model development and stakeholder interaction.

We welcome case studies, methodological papers, and interdisciplinary research that demonstrate how System Dynamics is evolving to meet the challenges of increasingly complex and data-rich environments.

Uncertainty Quantification & Robust Simulation

Track Coordinators: Wei Xie (Northeastern University), Ye Chen (Bowling Green State University)

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 datasets 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 analytics, 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:

  • Input uncertainty quantification criteria and methods
  • Robustness in input modeling and selection
  • Model risk quantification and reduction
  • Model calibration and validation
  • Data assimilation
  • Risk-sensitive simulation optimization
  • Robustness against model misspecifications in simulation logic and state transition
  • Sensitivity analysis
  • Optimal learning and data collection

Vendor

Track Coordinators: Renee Thiesing (Promita Consulting), Simon Taylor (Brunel University London)

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.

For each slot in the Vendor Track, vendors should submit a 2-page Extended Abstract.  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.