Aims and scope

The Annals of Operations Research publishes peer-reviewed original articles dealing with some aspects of operations research, including theory, practice, and computation. Submissions may include full-length research articles, short notes, expositions and surveys, reports on computational studies, and case studies of new or innovative practical applications.

The Annals of Operations Research also publishes special volumes focusing on well-defined fields of operations research, ranging from the highly theoretical to the algorithmic and the very applied. Such volumes have one or more Guest Editors who are personally responsible for collecting the papers to appear in the volume, for overseeing the refereeing process, and for keeping the volume on schedule. Potential Guest Editors of new refereed volumes (proceedings of conferences, monographs, or focused collections of papers) in major OR areas are cordially invited to put forward their suggestions to the Editor-in-Chief.

New submissions should be directed to the Editor-in-Chief, and manuscripts should be prepared following the "Instructions for Authors" on the journal’s homepage: www.springer.com/journal/10479. Manuscripts submitted for the Annals of Operations Research should report on original research, and should not have been previously published, or submitted for publication to any other journal.

Officially cited as: Ann Oper Res

Special Issue on Innovations in Maritime and Port Logistics: Optimization and Data-Driven Decision Making
Submission Date: 2026-02-15

Maritime logistics and port operations play a critical role in global trade, significantly influencing economic activities worldwide. The sector is currently experiencing increasing pressure to improve operational efficiency, reduce environmental impacts, and enhance resilience against disruptions. Growing freight volumes, more stringent regulations, and the need for sustainability are key drivers that shape the current landscape, presenting both challenges and opportunities for researchers and practitioners.

To address these challenges, there is an essential need for innovative operational research (OR) methods, sophisticated optimization algorithms, and advanced data-driven techniques. This special issue aims to highlight cutting-edge research that employs these approaches, showcasing novel solutions capable of improving decision-making and strategic planning in maritime logistics and port management.

This special issue seeks high-quality contributions promoting recent theoretical and practical advancements in maritime and port logistics optimization. The objective is to present state-of-the-art research that addresses contemporary issues in maritime transportation, port management, terminal operations, resource allocation, scheduling, routing, and intermodal connectivity within ports, among other relevant areas.

Contributions should provide innovative OR methods, applications or the integration of optimization techniques with data analytics to improve decision-making processes in maritime and port logistics are encouraged. Additionally, submissions presenting empirical analyses, practical case studies, or assessments of technological innovations within the scope of this special issue are particularly welcome.

Main topics of interest include, but are not limited to:

• Advanced optimization techniques in maritime logistics
• Data-driven decision making in port operations
• Integration of OR and machine learning
• Terminal operations and resource allocation optimization
• Intermodal connectivity and multimodal optimization
• Intelligent scheduling and routing in maritime and port logistics
• Innovative heuristics and metaheuristics for maritime logistics problems
• Real-time optimization methods for port operations
• Empirical analyses and case studies in maritime logistics
• Collaborative and cooperative logistics systems and approaches.
• Green maritime and port logistics
• Energy management in maritime and port systems

Special Issue on Advanced Machine Learning for System Reliability Management
Submission Date: 2026-05-30

Modern industrial systems generate massive datasets from sensor monitoring, which presents computational and memory challenges in data processing. Advanced machine learning technologies such as transfer learning, federated learning, quantum machine learning, and reinforcement learning offer effective and scalable solutions for processing and analyzing large volumes of data, as well as supporting subsequent decision-making. Recently, various new techniques of advanced machine learning have already been applied to system reliability management, such as fault diagnosis, health condition assessment, remaining useful life prediction, degradation modeling, and maintenance optimization. Yet, ongoing research works continue to explore this exciting research area. Therefore, we invite high-quality original research papers, review papers, and case studies that delve into these advancements.

Potential topics include, but are not limited to:

• Deep learning for anomaly detection and/or fault detection
• Machine learning-enhanced maintenance optimization
• Federated learning for system monitoring and predictive maintenance
• Reinforcement learning for maintenance decision making
• Transfer learning for cross-domain maintenance
• Generative models for synthetic data generation for predictive maintenance
• Self-supervised learning for unlabeled data for fault detection
• Large models for fault detection and/or diagnosis
• Generative models for reliability testing
• Meta-heuristics for large-scale reliability design optimization

Special Issue on Collaborative Intelligence in Operations Research: Models, Methods, and Applications
Submission Date: 2026-05-31

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.

Topics of Interest:

This special issue will focus on theoretical, computational, and applied research that extends OR methodologies to collaborative, multi-agent, and adaptive decision-making frameworks. Specific topics include, but are not limited to:

Human-AI Collaboration in OR Models:

• 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.

Optimization and Game-Theoretic Models for Multi-Agent Systems:

• 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.

Adaptive and Decentralized OR Frameworks:

• 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.

Data-Driven Intelligence and Learning-Based Optimization:

• 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.

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.

Special Issue on Global Supply Chain Reconfiguration Under Tariff Uncertainty
Submission Date: 2026-06-30

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 of Annals of Operations Research 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.

Topics of Interest (include but are not limited to):

• 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

Manuscripts should be original, unpublished, and prepared according to Annals of Operations Research 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

Special Issue on Stochastic Optimization in Agriculture
Submission Date: 2026-12-31

Stochastic optimization has seen many recent advances due to many reasons but mainly because of computer power, parallel programming, and AI hybridation. All this is impacting in the optimization of agricultural problems, either classical problems or new ones derived, for example, from the application of precision agriculture or precision livestock farming.

In this context, Annals of Operations Research invites submissions to this special issue from any theoretical area of stochastic, robust, and distributionally robust optimization with applications in agriculture.

The main topics of interest are, but not limited to:

• Optimization techniques in agriculture under uncertainty
• Advantages or inconveniences of stochastic optimization approaches
• Practical stochastic optimization for decision making in agriculture
• Agricultural and food supply chain management optimization
• Surrogate models for stochastic optimization
• New stochastic optimization methods in agriculture
• New trends in stochastic optimization in the age of artificial intelligence
• Multicriteria optimization methods under uncertainty
• Scenario analysis in agriculture
• Robust optimization and distributionally robust optimization
• Product or suppliers selection and risk management

Special Issue on Multiple Objective Programming and Goal Programming: Artificial Intelligence for Decision Making in Economic and Social Sciences
Submission Date: 2027-06-30

This special issue aims to publish selected papers presented during the 16th International Conference on Multiple Objective Programming and Goal Programming (MOPGP'25: http://mopgp.org/) that will be held on 1–3 July 2025, in Varese, Italy. Contributions arising from papers presented at the conference should be substantially extended and cite the conference paper where appropriate. The special issue will also consider papers not presented during the conference. We seek original and unpublished work not currently under consideration in any other journal.

The intersection of Multiple Objective Optimization (MOP), Goal Programming (GP), and Artificial Intelligence (AI) creates a robust framework for addressing complex decision-making challenges in Economic and Social Sciences. MOP provides structured methods to evaluate and prioritize conflicting objectives, while GP helps setting specific goals to be achieved by the decision maker. AI leverages data analytics and machine learning to process large datasets, revealing insights that improve the accuracy and the robustness of decision models and predictions.

To this extent, this special issue will cover theories and application of MOP, GP, and AI focusing on, but not limited to, the following topics:

1. Advancements in Multiple Objective Programming Techniques
2. Goal Programming Techniques and Formulations
3. Goal Programming Approaches in Public Policy
4. AI-Enhanced Decision Support Systems for Resource Allocation
5. Data-Driven Methods in Economic and Social Decision-Making
6. Integrating Machine Learning with MOP
7. Multicriteria Deep Learning
8. Applications of MOP and GP in Sustainable Development
9. Real-Time Decision-Making Frameworks Using AI
10. Comparative Studies of MOP and GP in Various Contexts
11. Multi-Criteria Decision Analysis in Sustainable Economics
12. Multiple Criteria Decision Making in Environmental Economics
13. Optimization Models for Social Welfare
14. Behavioural Insights in Multi-Objective Decision-Making
15. Metaheuristics and Computational Methods in MOP
16. MOP and MCDM in AI applications
17. Innovative Applications to Economic and Social Sciences

The scientific quality of the contributions is the main criterion in the selection process.