This special issue for Omega celebrates the journal's 55th anniversary with a focus on the application of data-driven optimization techniques to smart city challenges. The issue seeks research that addresses how advanced optimization methods and data analytics can improve urban systems and city management.

Appel publié par Omega. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Data-driven optimization
• Smart cities
• Urban management
• Optimization algorithms
• City systems

Timeline

• August 31, 2027: Full paper submission deadline

Adele Marshall, Queen's University Belfast

Laura Boyle, Queen's University Belfast

Sally Brailsford, University of Southampton

Erwin Hans, University of Twente

Brian Denton, University of Michigan

Martin Kunc, University of Southampton

Healthcare systems globally face mounting challenges from aging populations and workforce constraints while simultaneously benefiting from expanding digital health data and artificial intelligence capabilities. This special issue explores how Operational Research and AI methods can be combined to improve healthcare planning, delivery, and evaluation. Building on prior work examining the OR-AI interface, this collection focuses specifically on healthcare applications where reliable and verifiable AI is critical.

The special issue seeks original research demonstrating substantive contributions at the intersection of OR and AI in healthcare contexts. Submissions should include methodological advances, applied studies with measurable practical value, and work addressing real-world implementation challenges. Papers focused solely on optimization or simulation without significant AI integration may be more appropriate for other venues.

Appel publié par Journal of the Operational Research Society. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Machine learning and predictive modelling for patient flow, demand forecasting, and clinical decision support integrated with OR
• Large language models, generative AI, and retrieval-augmented generation for extracting modelling insight from unstructured clinical documents and health records
• Reinforcement learning and approximate dynamic programming for sequential decisions in screening, treatment, and care planning
• Generative approaches to synthetic health data for modelling and privacy-preserving analysis
• Optimisation of healthcare resources including workforce planning, scheduling, and capacity management using integrated AI and OR methodologies
• Hybrid simulation–AI approaches for the design and evaluation of care delivery
• Data-driven and digital-twin models of care pathways and healthcare delivery
• Statistical and stochastic modelling of patient journeys and survival
• AI-enhanced planning and delivery of care in emergency, unscheduled, and elective settings
• Personalised and stratified care through AI-enabled pathways, screening, prevention, and chronic disease management
• Human–AI collaboration in clinical and operational decision-making
• Trustworthy AI in health addressing equity, ethics, transparency, and verification
• Implementation and evaluation of OR/AI models in healthcare practice

Timeline

• Invalid DateTime: Expected publication
• January 15, 2027: Submission deadline
• April 1, 2027: First-round decisions

Abdel Lisser, Paris-Saclay University

Jia Liu, Xi'an Jiaotong University

Francesca Maggioni, University of Bergamo

S. K. Neogy, Indian Statistical Institute

Vikas Vikram Singh, Indian Institute of Technology Delhi

This special issue brings together recent advances in optimization and decision-making under uncertainty, focusing on theoretical contributions that strengthen the mathematical foundations and algorithmic techniques in operations research. The scope encompasses developments in stochastic, robust, and distributionally robust optimization, game theory, sequential decision-making, and learning-based methods that have expanded modern operations research's applicability.

The special issue welcomes original research presenting new optimization models, theoretical results, computational methodologies, and convergence analyses. While organized alongside ICORSI 2026 held at Indian Institute of Technology Delhi, the issue is open to all researchers in these areas regardless of conference participation.

Appel publié par Annals of Operations Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Mathematical programming
• Convex, nonconvex, and mixed-integer optimization
• Stochastic, robust, and distributionally robust optimization
• Chance-constrained optimization
• Variational inequalities and equilibrium problems
• Game theory and multi-agent optimization
• Markov decision processes and stochastic games
• Reinforcement learning for sequential decision making
• Online and data-driven optimization
• Optimization under risk measures
• Bayesian optimization
• Black-box optimization

Timeline

• March 31, 2028: Submission deadline

Andrea Flori, Politecnico di Milano

Anna Maria Gambaro, Università del Piemonte Orientale

Ioannis Kyriakou, University of London

Duc Khuong Nguyen, EMLV Business School

Climate-related operational risks pose significant threats to financial and economic systems, particularly as economies transition toward low-carbon models. The timing and pace of this transition create substantial uncertainties, especially for carbon-intensive firms that must balance profitability with decarbonization goals. Vulnerabilities extend through interconnected global supply chains where disruptions propagate indirectly, amplifying operational and systemic risks across multiple tiers.

Financial markets increasingly transmit climate-related risks through asset prices, volatility, and liquidity shocks, potentially triggering market instability through supply chain relationships. This special issue addresses the gap in understanding how climate and environmental risks propagate through financial and economic systems, seeking contributions that employ robust stochastic optimization, machine learning, and advanced forecasting methods to inform portfolio allocation and risk management decisions in the face of evolving climate regulations and transition uncertainties.

Appel publié par Annals of Operations Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Robust stochastic optimization methods for climate transition risks
• Portfolio optimization and risk-adjusted return modelling under climate policy uncertainty
• Credit and counterparty risk assessment under low-carbon transition scenarios
• Risk-sharing mechanisms and insurance models for climate-related disruptions
• Supply chain risk management in climate transition
• Operational decision-making in emission trading schemes and carbon pricing
• Predictive modelling of carbon stranding risk via supervised learning
• Machine learning and big data analytics for climate risk scenario classification
• Natural language processing applications in climate policy risk analysis
• Bayesian network approaches to modelling climate transition risk propagation
• Agent-based models of climate transition dynamics and systemic effects

Timeline

• December 31, 2026: Submission deadline

Olga Battaïa, Kedge Business School

Yasser Dessouky, San Jose State University

Reza Zanjirani Farahani, Paris School of Business

Masood Fathi, University of Skövde

Madjid Tavana, La Salle University

This special issue investigates how collaborative intelligence—combining human expertise, artificial intelligence, and distributed problem-solving—can advance operations research to address modern decision-making challenges. Traditional OR methods struggle with the dynamic and interconnected nature of contemporary problems, from logistics to emergency response. The collection seeks contributions that demonstrate how human-AI collaboration can enhance decision-making, boost system resilience, and optimize complex operational environments.

The call invites theoretical, computational, and applied research on human-AI collaboration in OR models, optimization and game-theoretic approaches for multi-agent systems, adaptive and decentralized frameworks, and data-driven learning-based optimization. Submissions should demonstrate both theoretical rigor and practical relevance, with innovative methodologies and real-world applications that advance collaborative intelligence in operations research.

Appel publié par Annals of Operations Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

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

Stelios Bekiros, University of Turin

Andrea D'Ariano, Roma Tre University

Peng Wu, Fuzhou University

Guowei Zhang, Dalian University of Technology

Modern operations research increasingly involves complex decision-making in data-rich environments where contextual information can significantly improve operational policies. This special issue focuses on integrating side information into optimization frameworks, combining advances from optimization, machine learning, and statistical learning. The challenge is to develop methods that go beyond historical averages to create adaptive, personalized solutions that effectively handle high-dimensional data, distribution shifts, and real-time computational demands.

The special issue welcomes contributions that develop novel theories, algorithms, and practical applications merging optimization with machine learning and data analytics. Priority is given to methods that address uncertainty while leveraging contextual information, with emphasis on improving actual decision quality and system performance rather than predictive accuracy alone.

Appel publié par Annals of Operations Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

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

Jiaguo Liu, Dalian Maritime University

Hakan Yildiz, Wayne State University

Guoqing Zhang, University of Windsor

This special issue addresses the urgent need to understand and manage global supply chain reconfiguration in response to tariff uncertainty and shifting trade policies. Rising tariffs and trade tensions are forcing organizations to redesign their sourcing strategies, manufacturing locations, and logistics networks, creating significant practical challenges that require new analytical approaches.

The journal seeks high-quality contributions that apply operations research and artificial intelligence methods to develop decision-making models for supply chain management under tariff uncertainty. Both theoretical advances and practical case studies are welcome, particularly interdisciplinary research that combines OR, AI, supply chain management, and international economics with real-world applications.

Appel publié par Annals of Operations Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

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: Submission deadline

Kannan Govindan, University of Southern Denmark

This special section addresses the integration of sustainable development goals into supply chain management through advanced operations research methods. Organizations face mounting pressure to balance economic growth with resource conservation and environmental protection while addressing poverty and societal issues. The seventeen sustainable development goals adopted in 2015 target achievement by 2030, with significant implications for how supply chains are designed and operated across the globe.

The special section invites research on how optimization models, decision-making algorithms, and novel operations research techniques can support the implementation of sustainable supply chain management. Contributions should explore policy design with multiple stakeholders, innovative sustainability practices, and governance mechanisms for monitoring progress toward sustainable goals in different economic and geographical contexts. The work should demonstrate how efficient resource utilization and environmental responsibility can be achieved while improving organizational and societal outcomes.

Appel publié par Annals of Operations Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Designing and policymaking in sustainable development goals through stakeholder support
• Implementing sustainable development goals in supply chains through innovative strategies and practices
• Monitoring supply chain governance and implications with a focus on sustainable development goals
• Multi-attribute decision making approaches for sustainable supply chain management
• Multi-objective decision making approaches for sustainable supply chain management
• Green supply chain management strategies
• Lean supply chain approaches
• Resource conservation and efficiency in supply chains
• Circular economy in supply chain contexts
• Sustainable supplier selection and evaluation
• Carbon emissions reduction in supply chains
• Reverse logistics and closed-loop supply chains
• Sustainable procurement practices
• Environmental impact assessment in supply chains
• Social and economic sustainability in supply chain operations

Timeline

• Invalid DateTime: Submission deadline

Eva K. Lee, Georgia Institute of Technology

Ariela Sofer, George Mason University

This special section showcases optimization and computational approaches applied to medical and biological challenges. It aims to bring together mathematical programming researchers and healthcare professionals to address complex decision problems in medicine, from medical devices to clinical data analysis.

The special section welcomes theoretical and methodological research on optimization models and algorithms, including continuous, integer, combinatorial, and stochastic methods, applied to healthcare and life sciences. Topics of interest span disease modeling, diagnosis, treatment planning, medical imaging, epidemiology, and molecular biology.

Appel publié par Annals of Operations Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Disease modeling
• Medical diagnosis
• Treatment planning
• Biological and medical imaging
• Epidemiology
• Molecular biology
• Linear and nonlinear optimization
• Integer programming
• Combinatorial optimization
• Stochastic optimization

Timeline

• Invalid DateTime: Submission deadline

Appel publié par Transportation Research Part E: Logistics and Transportation Review. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Timeline

• October 30, 2026: Full paper submission deadline

Hyun-Jung Kim, KAIST

Shu-Kai Fan, National Taipei University of Technology

Fugee Tsung, Hong Kong University of Science and Technology

Thomas Volling, Technical University Berlin

Jang Ho Kim, Korea University

Dong-Young Lim, Ulsan National Institute of Science and Technology

This special issue addresses decision-making in complex industrial systems facing dynamic uncertainty through artificial intelligence and optimization techniques. The issue seeks research combining AI methods such as reinforcement learning, generative AI, and digital twins with operations research and optimization approaches to manage manufacturing, supply chains, healthcare, and other sectors dealing with demand fluctuations and disruptions.

The special issue welcomes both theoretical and practical contributions demonstrating how AI-driven methodologies can improve decision-making under uncertainty. Particular emphasis is placed on interdisciplinary research with clear industrial applicability and the adoption of open science practices including data and code sharing to enhance reproducibility.

Appel publié par International Journal of Production Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• AI-driven decision-making under uncertainty
• Production planning and scheduling in stochastic and dynamic environments
• Reinforcement learning for uncertain industrial systems
• Stochastic optimization and robust operational strategies
• AI-enabled statistical quality control and process improvement
• Hybrid AI and optimization approaches for uncertain environments
• Data-driven optimization and prescriptive analytics
• AI-enhanced supply chain and logistics management under disruptions
• Real-time and adaptive decision-making systems
• Simulation-based optimization and digital twins under uncertainty
• Agentic AI and autonomous industrial systems
• Explainable and trustworthy AI for operational decision-making
• AI for resilient and sustainable operations
• Industrial applications and case studies of AI-driven decision systems

Timeline

• October 1, 2026: Submissions open
• January 31, 2027: Submission deadline