Stelios Bekiros, University of Turin

Andrea D'Ariano, Roma Tre University

Peng Wu, Fuzhou University

Guowei Zhang, Dalian University of Technology

Operations research (OR) problems increasingly arise in data-rich and highly uncertain environments where operational decisions must adapt dynamically to evolving contextual information. Advances in sensing technologies, digital platforms, enterprise systems, and large-scale data collection have significantly expanded the availability of side information related to demand patterns, operational states, user behaviors, market conditions, and environmental factors. In many modern applications, such contextual information provides valuable predictive signals that can substantially improve decision quality when effectively integrated into optimization models.

In response, contextual optimization has emerged as a rapidly growing research direction at the intersection of optimization, machine learning, statistics, and prescriptive analytics. Unlike classical optimization paradigms that rely solely on historical averages or predefined uncertainty sets, contextual optimization explicitly incorporates side information into the decision-making process, enabling more adaptive, personalized, and data-driven operational policies. Recent developments in this area include contextual stochastic optimization, distributionally robust optimization with covariates, decision-focused learning, learning-enhanced optimization, online optimization, and adaptive decision-making frameworks.

Despite substantial progress, several important challenges remain open. Real-world contextual optimization problems often involve high-dimensional and heterogeneous data, noisy or incomplete observations, distribution shifts, privacy concerns, interpretability requirements, and stringent real-time computational constraints. Moreover, predictive accuracy alone is insufficient in operational settings; the ultimate objective is to improve downstream decision quality and system performance. Consequently, there is a growing need for new theories, methodologies, and computational frameworks that systematically integrate learning, analytics, and optimization under uncertainty.

This special issue aims to bring together recent advances in contextual and data-driven optimization methods, with particular emphasis on optimization and decision making under uncertainty using side information. We welcome high-quality contributions that develop novel theories, models, algorithms, and applications integrating optimization with machine learning, statistical learning, and data analytics.

Potential topics

• Contextual Stochastic Optimization
• Distributionally Robust Optimization with Side Information
• Prescriptive Analytics and Contextual Decision Making
• Learning-Enhanced Optimization
• Decision-Focused Learning
• Online, Dynamic, and Adaptive Optimization
• Learning-Augmented Algorithms
• Interpretable and Trustworthy Optimization Models
• Statistical Guarantees and Generalization in Optimization
• Data-Driven Optimization Under Uncertainty
• AI and Machine Learning for OR
• Human-in-the-Loop Optimization
• Optimization with Foundation Models or Generative AI
• Scalable Algorithms for Large-Scale Optimization Problems
• Digital Twin and Real-Time Decision Systems
• Contextual Transportation and Logistics Optimization
• Contextual Supply Chain and Revenue Management
• Data-Driven Healthcare and Service Operations

Timeline

• July 31, 2027: Submission deadline

Annamaria Barbagallo, University of Naples Federico II

Encarnación Algaba, University of Seville

Giovanna Bimonte, University of Salerno

Francesco Ciardiello, University of Salerno

This special issue aims to promote research on game theory as a fundamental framework for analyzing strategic interaction, cooperation, conflict, coordination, and decision-making in complex systems. The issue aims to showcase recent developments in game theory and its diverse applications, with particular emphasis on interaction dynamics and collective decision-making, encompassing theoretical contributions, practical applications, and interdisciplinary approaches.

In particular, we encourage participants of the European Meeting on Game Theory 2026 (SING21) to submit complete and expanded versions of the papers presented at the conference. Nevertheless, submissions of other relevant papers are also welcome. Papers based on conference presentations should be significantly revised and extended, and they should appropriately reference the original conference version where relevant.

All submissions will undergo a rigorous peer-review process coordinated by the Guest Editors. Accepted papers will be published online individually, before print publication.

Potential topics

• Cooperative games and their applications
• Non-cooperative games and strategic interaction
• Mechanism design
• Network
• Dynamic games
• Evolutionary games
• Stochastic games
• Voting, power indices, and collective decision-making
• Auctions and market design
• Bargaining
• Learning and experimentation in games
• Computational game theory
• Applications of game theory to management, energy, health policy, industrial organization, environmental and resource economics, transportation, cybersecurity, artificial intelligence, and other fields

Timeline

• April 20, 2027: Submission deadline

Yi-Kuei Lin, National Yang Ming Chiao Tung University

Industrial engineering systems, including manufacturing systems, supply chains, logistics networks, energy and power systems, transportation systems, and service operations, play a critical role in modern society. The reliable operation of such systems is essential for ensuring efficiency, safety, and robustness in the presence of uncertainty, disruptions, and complex interactions among system components. Failures or performance degradation in these systems may lead to significant economic losses, operational inefficiencies, and societal impacts.

Traditionally, reliability modeling in industrial engineering has been grounded in probabilistic analysis, stochastic modeling, and analytical evaluation techniques. While these approaches remain fundamental, recent advances in sensing technologies, industrial Internet of Things, and information systems have enabled the collection of large-scale operational and condition-monitoring data. This has led to a growing interest in data-driven and intelligent reliability modeling, where statistical learning, machine learning, and other statistical analytical approaches complement classical reliability theory.

At the same time, reliability analysis is increasingly expected to support decision-making processes, such as maintenance planning, resource allocation, system design, and operational optimization. Integrating reliability modeling with decision support frameworks allows practitioners and decision-makers to move beyond reliability assessment toward actionable, reliability-informed decisions under uncertainty.

We invite submissions on topics related to reliability modeling and decision support for industrial engineering systems using Operations Research, industrial engineering, and data-driven methodologies. Contributions addressing both theoretical developments and practical applications are welcome.

We welcome the submission of original research papers that provide new insights and practical solutions to challenges in reliability modeling and decision support for industrial engineering systems. Submissions should demonstrate methodological rigor and clearly highlight their relevance to real-world industrial applications. Papers that illustrate how data-driven reliability analysis can enhance decision-making and system performance are particularly encouraged.

Potential topics

• Data-driven reliability modeling and parameter estimation using operational data
• Reliability analysis using statistical learning and machine learning methods
• Reliability models combining analytical formulations with data-driven learning approaches
• Decision support systems incorporating reliability analysis and risk information
• Optimization and decision-making problems informed by reliability performance
• Predictive maintenance and condition monitoring using data and learning models
• Multistate system and network reliability analysis
• Reliability evaluation of manufacturing, logistics, and supply chain systems
• Reliability modeling in energy, power, and infrastructure systems
• Stochastic modeling and uncertainty analysis in data-driven reliability studies
• Simulation, enumeration, and approximation methods for reliability evaluation
• System design and resource allocation considering reliability and operational data
• Reliability analysis supported by digital twins, data analytics, and intelligent systems
• Reliability, robustness, and resilience of industrial engineering systems

Timeline

• July 31, 2027: Submission deadline

Guoqing Zhang, University of Windsor

Jiaguo Liu, Dalian Maritime University

Hakan Yildiz, Wayne State University

The reconfiguration of global supply chains has become increasingly urgent amid escalating tariff uncertainty and shifting international trade policies. Tariff-induced disruptions are reshaping sourcing strategies, manufacturing footprints, logistics networks, and market access worldwide.

While tariff-related supply chain research is not new, today's environment is marked by unprecedented levels of uncertainty and complexity. High tariff rates, retaliatory measures, and escalating trade tensions have created significant challenges for supply chain reconfiguration and management in practice. The unprecedented challenges of tariff uncertainty highlight the need for new decision-making models, offering significant opportunities to advance the literature and address pressing real-world challenges.

This special issue invites high-quality contributions that develop and apply Operations Research (OR) and Artificial Intelligence (AI) methods to address the challenges and opportunities in global supply chain reconfiguration under tariff uncertainty. We welcome theoretical developments, methodological innovations, applied modelling studies, and quantitatively supported managerial insights. Interdisciplinary research that integrates OR, AI, supply chain management, and international economics, particularly with real-world case applications, is especially encouraged.

Manuscripts should be original, unpublished, and prepared according to submission guidelines. Submissions are expected to have strong methodological contributions in OR and/or AI, with clear relevance to global supply chain reconfiguration under trade policy uncertainty.

Potential topics

• Supply network redesign and optimization under tariff uncertainty
• Robust and stochastic optimization models for tariff-driven supply chain planning
• Global supply chain reconfiguration under trade policy uncertainty
• AI-powered dynamic supply chain adaptation and tariff response strategies
• Dynamic production, sourcing, and logistics strategies facing tariff risks
• Supply chain resilience and risk management for tariff disruptions
• Logistics and warehousing for global e-commerce and omnichannel supply chains
• Maritime network and logistics optimization with tariff impacts
• Hybrid OR–machine learning for adaptive decision-making in global supply chains
• Multi-echelon inventory management under fluctuating tariff policies
• AI and data-driven methods for trade policy analysis and supply chain impacts
• Optimization models and algorithms for large-scale global supply chain problems

Timeline

• October 31, 2026: Manuscript submission deadline

Madjid Tavana, La Salle University

Olga Battaïa, KEDGE Business School

Yasser Dessouky, San Jose State University

Masood Fathi, University of Skövde

Reza Zanjirani Farahani, Paris School of Business

The increasing complexity of modern decision-making demands advanced Operations Research (OR) models that integrate Collaborative Intelligence—the synergy between human expertise, Artificial Intelligence (AI)-driven decision support, and distributed problem-solving frameworks. This paradigm enhances adaptability, efficiency, and resilience in complex operations by leveraging multi-agent coordination, human-AI collaboration, decentralized optimization, and machine learning-enhanced decision-making.

Traditional OR methodologies, despite their strengths in static environments, struggle with the dynamic, interconnected, and uncertain nature of modern decision-making. As Russell Ackoff noted in his 1956 article, "The Aging of a Young Profession," OR was already showing signs of stagnation, becoming overly preoccupied with mathematical techniques rather than addressing real-world problems holistically. By 1979, in "The Future of Operational Research is Past," he further criticized OR for prioritizing optimization within narrow constraints instead of embracing a systemic, interdisciplinary approach. Modern applications like logistics, production, service systems, and emergency response require more adaptive and interactive OR models. Collaborative Intelligence offers a transformative approach, enabling real-time interaction among humans, AI agents, and mathematical models to optimize problem-solving and system performance.

The rapid advancements in AI have revolutionized decision-making across OR domains. However, fully autonomous AI systems face challenges in handling uncertainty, ethical considerations, and interpretability, particularly in high-stakes environments. Collaborative Intelligence bridges this gap by combining the computational power of AI with human intuition, adaptability, and ethical reasoning, fostering trust and robustness in decision-making.

This special issue explores cutting-edge methodologies, theoretical advancements, and practical applications of Collaborative Intelligence in OR. We invite high-quality contributions that address how human expertise and AI can collaboratively enhance decision-making, improve system resilience, and optimize complex operational environments. We welcome original research contributions that propose innovative mathematical models, algorithms, and applications of OR for collaborative decision-making, resilience planning, decentralized optimization, and dynamic problem-solving in complex operational environments. Submissions should demonstrate theoretical rigor and practical relevance, focusing on advancing the state of the art in Collaborative Intelligence.

This special issue aims to provide a platform for researchers and practitioners to share insights, methodologies, and case studies that highlight the transformative potential of Collaborative Intelligence in OR.

Potential topics

• Designing OR models that facilitate seamless interaction and information exchange between human decision-makers and AI agents
• The framework for integrating human judgment, preferences, and ethical considerations into AI-driven decision-making
• Techniques for visualizing and interpreting AI outputs to enhance human understanding and trust
• Novel optimization algorithms and game-theoretic frameworks for coordinating and optimizing decisions in multi-agent environments
• Models addressing diverse objectives, capabilities, and interactions among multiple agents
• Approaches to managing conflicts, uncertainties, and strategic behaviors in multi-agent decision-making
• OR frameworks capable of dynamically adapting to real-time changes and uncertainties
• Decentralized optimization algorithms and control strategies for distributed systems
• Online learning and adaptive control techniques to improve system responsiveness and resilience
• Leveraging machine learning and data analytics to extract insights and patterns for OR applications
• Learning-based optimization algorithms that improve performance through data feedback
• Predictive analytics and simulation techniques for enhanced decision-making and risk management

Timeline

• December 31, 2026: Submission deadline

Dries F. Benoit, Ghent University

Kristof Coussement, Institut d'Economie Scientifique Et de Gestion

Cem İyigün, Middle East Technical University

Asil Oztekin, University of Massachusetts Lowell

The objective of this special section is to publish papers that contribute to both the theory and practice of data science and predictive analytics. While papers describing new techniques have been published in other journals, we would expect to see how the technique could be applied in practice with implications to create value in organizations. Creating value through analytics may lead to, or require, organizational change for it to effective. The relationship with Operations Research is direct but distinct: analytics is an organizational activity that draws on and uses the techniques of data science and operational research as appropriate.

Research into data science and analytics should seek to both incorporate the unique aspects of the OR discipline, as well as the innovations, concerns and characteristics of the analytics movement.

Potential topics

• Ethics and governance issues in analytics: How should data be obtained? What are the ethical implications of using applications of analytics to influence behavior?
• Big data and analytics: What are the limitations and applications of optimization and other OR techniques to large datasets? What are the challenges for applications of OR methods within distributed systems? What is the possibility that OR models could in fact be the producers of big data, e.g., large-scale simulation models? What new methods/models in response to big data, e.g., sentiment mining, can be adopted by OR?
• Organizational issues in analytics adoption: What are the issues facing organizations trying to adopt analytics? What is the role of real-time applications of OR in organizations?
• Data quality and analytics: What methods can be used for hypothesis testing and model validation in large datasets? How can unstructured data be used effectively in OR models? What is the role of multi-methodology in business analytics? What opportunities do open data present for the OR discipline?
• Analytics and decision support: How can data visualization techniques be used across the breadth of OR? What role do problem structuring and "soft" OR techniques play in analytics and big data projects?

Timeline

• Invalid DateTime: Submission deadline

Kannan Govindan, University of Southern Denmark

Organizations must rethink and optimize their existing strategies to meet their sustainable business goals, due to the constant depletion of vital resources and the greater demands of societal issues. The increasing population not only has an impact on natural resources, but it also results in greater pollution and contributes to greater levels of poverty. With these considerations, on September 25, 2015, some countries adopted a new set of sustainability development goals (SDGs) to end poverty and to improve prosperity worldwide. Seventeen sustainable development goals have been targeted to achieve by 2030 under the new sustainable development agenda. Further, these sustainable development goals encompass a range of perspectives and various levels of applications, including supply chain management.

In recent years, researchers and practitioners have addressed supply chain management issues because of their significant impacts on organizational developments regardless of the field of applications. Over the years, researchers have proven that an inefficient supply chain can force an organization to fail. Hence, integrating SDGs in operations and supply chain management areas is an important topic to explore. Many studies sought to explore sustainability in supply chain management with a wide range of concepts and strategies, including green, lean, and other approaches with a special focus on newly targeted SDGs. Research is needed to further analyze and shape the implementation of SDGs in supply chain management environments with the help of advanced operations research (OR) methods, including multi-attribute decision making (MADM) and multi-objective decision making (MODM).

The main objective of this special section is to invite academic practitioners to contribute a better understanding of the scientific basis for decoupling economic growth from resource depletion and environmental degradation while improving human lives with the assistance of advanced OR methods. It is believed that advanced optimization models and algorithms can improve data-driven decision models by either formulating pattern discoveries and knowledge extraction problems or by defining efficient algorithms for implementing SDGs in supply chain management at a macro level. Additionally, new algorithms continually make efficiency gains and improve the applicability of older algorithms. Virtually every numerical analysis includes several optimization algorithms that can be applied to design sustainability in supply chain. With these concerns, this special section pertains to sustainable supply chain management (SSCM) by making direct connections between resources, the environment, the economy, and the goal of achieving SDGs in different parts of the world through OR methods.

These themes should incorporate an integrated approach: looking at possibilities of connecting the conservation of resources with sustainable human economic activities (from a supply chain perspective), and monitoring progress towards sustainable indicator strategies with the support of multi stakeholders in predominant inter- and intra- organizational management fields. This special section will comprise a significant body of knowledge about how natural resources are being utilized in various economies and will address the impact of this use on the eco-efficient and socio-economic systems, and suggest pathways of transitioning to a more sustainable future. This perspective includes building on and expounding the significance of resource conservation and efficiency along with a societal focus to improve sustainable development in supply chain management by achieving the proposed SDGs.

This special section seeks to be specific regarding the different kinds of sustainable supply chains because they can affect the policy implications of different sustainable indicators. In addition to these ideas, this special section welcomes authors to contact us to discuss other possible subtopics. To encourage effective methods and thereby motivate the authors to adopt a variety of sustainable supply chain perspectives in approaching this subject, the special section Editor has purposely kept the above list of suggested topics short. However, all submissions must suit within the domain statement of the journal.

Potential topics

• Designing and policymaking in SDGs through stakeholder support: to provide examples of sustainable policy and design making with multi-stakeholder perspectives and to define how sustainability is considered in framing policies and enumerating success factors and challenges on supply chain management
• Implementing SDGs in supply chains through innovative strategies and practices: to identify the sustainable goals, indicators, and practices related to the implementation of sustainable supply chain management and to explore how this knowledge may be used to effectively manage and conserve resources with less environmental and societal impact
• Monitoring supply chain governance and implications with a focus on SDGs: to elaborate practices and supporting mechanisms in measuring, monitoring, and reporting progress toward achieving targets, and to further emphasize various challenges related to the implementation of SDGs in supply chain management in different applications/context/economies

Timeline

• Invalid DateTime: Submission deadline

Eva K. Lee, Georgia Institute of Technology

Ariela Sofer, George Mason University

Optimization has long been a cornerstone for advancement of various industrial, government, and military applications. In recent years, it has become increasingly important in advancing research and development related to medical and biological applications. Complexity of medical devices and delivery systems demands solutions to complex decision problems; and biological and clinical applications provide rich, sometimes overwhelming, sources of data upon which challenging optimization problems must be formulated and solved. Such challenges stimulate partnerships between members of the mathematical programming community and biologists, clinicians, public health officials, and others. These partnerships provide excellent opportunities to push the frontier of theoretical and computational optimization while solving important problems that benefit society and mankind.

To highlight and support the role of optimization and computation in such applications, and to disseminate advances to the broad research community in a timely manner, the Annals of Operations Research: Operations Research in Medicine special section was established in 2000. Each volume is devoted to presenting state-of-the-art research results in this dynamic and exciting area. We seek original, high quality contributions that investigate theoretical or methodological work on models and algorithms involving continuous linear and nonlinear optimization, integer programming, combinatorial optimization and stochastic approaches applied to medical and biological applications.

Manuscripts must be original, previously unpublished, and not currently under review in other journals. Each manuscript will be subjected to peer review according to the standard of Annals of Operations Research.

Potential topics

• Disease modeling
• Medical diagnosis
• Treatment planning
• Biological and medical imaging
• Epidemiology
• Molecular biology
• Continuous linear and nonlinear optimization applied to medical and biological applications
• Integer programming applied to medical and biological applications
• Combinatorial optimization applied to medical and biological applications
• Stochastic approaches applied to medical and biological applications

Timeline

• Invalid DateTime: Submission deadline

Timeline

• August 1, 2026: Submission deadline