This special issue seeks research that applies operations research and data-driven methodologies to address complex socio-economic challenges within Ibero-American countries. The focus is on demonstrating how quantitative and analytical approaches can inform policy decisions and solve real-world problems affecting social and economic systems in this region.

Appel publié par Socio-Economic Planning Sciences. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Operations research applications in socio-economic systems
• Data-driven decision making methods
• Complex system analysis in Ibero-American context
• Evidence-based policy and management approaches
• Optimization and analytical methods for social and economic challenges

Timeline

• March 31, 2027: Submission deadline

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

Dries F. Benoit, Ghent University

Kristof Coussement, IÉSEG School of Management

Cem İyigün, Middle East Technical University

Asil Oztekin, University of Massachusetts Lowell

This special section seeks to publish research that bridges data science and operations research, emphasizing both theoretical advances and practical organizational applications. The focus extends beyond technical innovation to address how analytics creates measurable value within organizations and drives necessary organizational change.

The collection invites contributions exploring the intersection of OR and analytics across multiple dimensions: ethical and governance considerations in data usage, challenges of applying OR techniques to big data and distributed systems, organizational barriers to analytics adoption, data quality and validation methods for large datasets, and the role of visualization and soft OR techniques in supporting data-driven decision making.

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

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

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

Matteo Rocca, University of Insubria

Davide La Torre, Skema Business School

Constantin Zopounidis, Technical University of Crete

This special issue focuses on the intersection of Multiple Objective Optimization, Goal Programming, and Artificial Intelligence, addressing complex decision-making challenges in economics and social sciences. The collection seeks papers that combine these three domains to develop frameworks where optimization methods prioritize conflicting objectives, goal programming establishes specific targets, and AI enhances decision models through data analytics and machine learning.

The special issue welcomes both papers substantially extending contributions presented at the 16th International Conference on Multiple Objective Programming and Goal Programming (MOPGP'25) and new original work addressing theories and applications of MOP, GP, and AI in economic and social contexts.

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

Potential topics

• Advancements in Multiple Objective Programming Techniques
• Goal Programming Techniques and Formulations
• Goal Programming Approaches in Public Policy
• AI-Enhanced Decision Support Systems for Resource Allocation
• Data-Driven Methods in Economic and Social Decision-Making
• Integrating Machine Learning with MOP
• Multicriteria Deep Learning
• Applications of MOP and GP in Sustainable Development
• Real-Time Decision-Making Frameworks Using AI
• Comparative Studies of MOP and GP in Various Contexts
• Multi-Criteria Decision Analysis in Sustainable Economics
• Multiple Criteria Decision Making in Environmental Economics
• Optimization Models for Social Welfare
• Behavioural Insights in Multi-Objective Decision-Making
• Metaheuristics and Computational Methods in MOP
• MOP and MCDM in AI applications
• Innovative Applications to Economic and Social Sciences

Timeline

• June 30, 2027: 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

Alexandra Brintrup, University of Cambridge

Thomas Choi, Arizona State University

George Huang, Hong Kong Polytechnic University

Dmitry Ivanov, Berlin School of Economics and Law

Recent advances in agentic Large Language Models have created new opportunities for autonomous decision-making in supply chains. This special issue seeks to advance rigorous research on how these AI agents can transform supply chain management by integrating perspectives from operations, AI, complexity science, and industrial engineering. While multi-agent systems have been studied for years, recent LLM breakthroughs now enable more flexible, scalable, and practical implementations that major corporations and technology providers are already exploring.

The special issue welcomes diverse research methodologies including technical solutions, modeling, empirical studies, and experimental work with practical implications. Topics span from using agentic systems for optimization and forecasting to managing risks, designing interorganizational coordination systems, and addressing technical and governance challenges such as trustworthiness, safety, and performance evaluation.

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

• Discourse on utilising agentic systems for complex scenarios in supply chain management: Risk and disruption management, logistics and supply chain optimisation, transportation routes, inventory management, quality control, demand forecasting, and warehouse planning and location, and cash flow predictions
• Supply network design with agentic technology: Supplier relationship configurations, agentic digital twins to simulate inventory flows, sustainability implications across the supply chain, circular supply chains, supply chain visibility, and supply chain financing
• Interorganisational agentic systems: Effective multi-agent negotiation and coordination, the design of mediative and persuasive agentic systems, preservation of organisational privacy during multi-agent communication
• Hybrid systems: Integration of agentic systems with blockchain, IoT, Omniverse, and traditional multi-agent systems
• Emergence and Complexity: Unintended consequences of agentic deployment at the system scale, governance, trustworthiness and safety, centralised versus decentralised control, human-in-the-loop agentic systems
• Technical challenges: Performance evaluation, efficient task division, ablation analysis and back testing, sensitivity analysis, agentic architectures operating in high uncertainty environments, long-term horizon reasoning, overcoming hallucinations

Timeline

• September 30, 2026: Manuscript deadline