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 Optimization Modeling in Business and Government
截稿日期: 2024-11-30

The special issue will highlight the diverse applications of large-scale optimization modeling to support real-world decision-making. We solicit high-quality, original contributions with applications in business and the government, including the military. All areas of optimization are welcome, including novel methodologies for optimal decision-making using machine learning, artificial intelligence, large language models, and so on.

All areas of business applications are encouraged: finance, logistics, transportation, marketing, supply chain, climate and environmental management, healthcare, and so on.

From the government applications, we are looking for contributions to improving government functions and policy analysis. Government, semi-government, and military applications are welcome, as well as applications to supranational institutions.

Purely methodological contributions will be considered to the extent that they are motivated by a demonstrably significant application. Purely applied contributions will be considered to the extent that they demonstrate the significant impact of the model(s) to decision-making.

Authors of accepted papers will be invited to give a talk at the conference that will (tentatively) take place upon completion of the volume.

Instructions for authors can be found at:

http://www.springer.com/business/operations-research/journal/10479

Authors should submit a cover letter and a manuscript by November 30, 2024, via the Journal's online submission site. please see the Author Instructions on the web site if you have not yet submitted a paper through Springer's web-based systems, Editorial Manager. When prompted for the article type, please select Original Research. On the Additional Information screen, you will be asked if the manuscript belongs to a special issue, please choose yes and the special issue's title, Optimization Modeling in Business and Government, to ensure that it will be reviewed for this special issue. Manuscripts submitted after the deadline may not be considered for the special issue and may be transferred, if accepted, to a regular issue.

Papers will be subjected to a strict review process under the supervision of the Guest Editors, and accepted papers will be published online individually, before print publication. 

Special Issue on Advances in Statistical Modelling for Social Science
截稿日期: 2024-12-31

Social science-related technical issues can advance statistical methodology. Solving practical issues, indeed, can still produce methodological improvements in the statistical domain. This special issue aims to advance the reconciliation of social science and statistics by collecting articles on cutting-edge statistical methods related to social changes, including articles on methodological developments in response to open questions in statistical modeling. Applications are also welcome to provide innovative use of statistical methods applied to relevant data problems in social studies.

We invite submissions that present new and original research on topics that include, but are not limited to, the following:

• Statistical methods for modeling and forecasting social, economic, and demographic phenomena
• Machine learning frameworks for social, economic, and demographic prediction
• Modeling the dependence structure of social phenomena
• High-dimensional data analysis
• Methodological issues in survival analysis
• Big data and data sources for digital and computational analysis
• Statistical advances in measurements of several social phenomena (sustainable development, well-being, poverty and inequality, labor and working conditions, etc.)

Special Issue on Decision-making under Uncertainty for Commodities and Financial Markets
截稿日期: 2024-12-31

Stochastic optimization has seen many recent advances with far-reaching impact involving risk measures, connections between robust and distributionally robust optimization, and applications in areas of portfolio selections, risk management, and credit risk management.

Annals of Operations Research invites submissions of manuscripts to this special issue from any theoretical area of stochastic, robust, and distributionally robust optimization with applications in commodities and financial markets.

We especially welcome innovative contributions related to, but are not limited to, the following main topics:

➢ Optimization techniques under uncertainty
➢ Stochastic programming
➢ Robust optimization
➢ Distributionally robust optimization
➢ Portfolio selections and risk management
➢ Credit risk management in financial institutions
➢ Machine Learning to identify risk drivers in ESG investments

All submissions will be reviewed according to the standards of Annals of Operations Research. The primary acceptance criterion for submission will be the high quality and originality of the contribution.

This is an open call for all researchers in this area. We encourage participants to the Stream “OR in banking, finance and insurance” of the EURO 2024 Conference; and to the EURO Summer Institute “Decision-making under Uncertainty for Commodities and Financial Markets” to be held at Sorriso Thermae Resort, Forio d'Ischia (Na) Italy on 15-25 September 2024 to submit extended versions of their presented papers

Please submit all manuscripts using the Annals of Operations Research style via the editorial system: https://www2.cloud.editorialmanager.com/anor/default2.aspx

The deadline for submission of full papers is December 31, 2024 with first-round reviews expected to be completed by April 30, 2025.

Papers will be subject to a strict review process managed by the Guest Editors, and accepted papers will be published online individually before print publication.

Please direct questions about the special issue to the guest editors.

Special Issue on Ensemble AI-Driven Metaheuristic Optimization in OR: Newest Contributions in Theory, Methods, and Applications
截稿日期: 2024-12-31

Operations research (OR) is an analytical method which attempts to achieve more optimality of the real system under the given circumstances. It interacts in various scientific fields ranging from statistics, mathematicians, management science, logistics and supply chain management, vehicle routing problem, economics and business intelligence, systems and control, big data mining and IoT, to ecology, psychology, biology, and education. Employing OR professionals can help companies in obtaining the best achievable performance considering all possible options and outcomes, while taking their risks into account.

Majority of optimization problems in OR suffer from various complex issues such as large number of objectives (many objective problems), large number of constraints (highly constrained problems), large number of decision variables with different types (binary, integer, permutation, discrete, continuous), non-linearity, discontinuities, time-dependency and uncertainty of objectives/constraints, the need for model and/or solution robustness, etc. Therefore, traditional optimization techniques cannot be effective enough to solve these problems, and thus, heuristic and/or metaheuristic techniques must be applied. Over the past years, a great deal of effort has been invested in field of nature-inspired metaheuristic algorithms, which have been established as the most practical approaches to tackle the complexities that arise in real-world OR problems.

In recent years, various ensemble AI-driven metaheuristic algorithms have been developed to intelligently deal with the complex issues in OR problems, based on the application specifications. These algorithms are designed by hybridization of metaheuristics with other soft computing and artificial intelligence tools such as knowledge-based heuristics, fuzzy sets and systems, artificial neural networks, and machine/deep/reinforcement learning. Ensemble AI-driven metaheuristics can be helpful for a better understanding of optimization/learning processes to provide an additional value on the sustainability and productivity of firms and organizations. An appealing solution to improve exploration-exploitation balance during the search process is to combine population- and solution-based metaheuristics via sequential/parallel hybridizations. By exploiting problem-dependent heuristic information, ensemble heuristic-metaheuristic algorithms achieve a better complexity-efficiency trade-off than the both techniques when applied separately. Metaheuristic-empowered crisp/fuzzy heuristics are interesting solutions to solve Just-in-Time problems through a heuristic-based solution generator and a metaheuristic-based model tuning procedure. Moreover, various hybridizations of metaheuristics with machine/deep/reinforcement learning have been performed to a wide range of applications in OR.

This special issue is designed to highlight recent theoretical and methodological advances in ensemble AI-driven metaheuristic optimization algorithms and their applications in all aspects of OR. Topics of interest include, but are not limited to:

• Ensemble population- and solution-based metaheuristics
• Ensemble knowledge-based heuristic-metaheuristic algorithms
• Ensemble metaheuristic-fuzzy learning models
• Hybridization of metaheuristics and machine/deep learning
• Ensemble multi-objective optimization techniques
• Ensemble constraint handling techniques
• Metaheuristics with multiple local search operators
• Metaheuristic-enabled heuristics for Just-in-Time problems
• Metaheuristics for resource allocation problems
• Metaheuristics for logistics and supply chain management
• Metaheuristics for cleaner production and manufacturing
• Metaheuristics for sustainable and renewable energy systems
• Metaheuristics for urban and agricultural planning
• Metaheuristics for structural and mechanical engineering
• Metaheuristics for wireless communication systems
• Metaheuristics for IoT and smart cities
• Metaheuristics for big data mining and analytics
• Metaheuristics for signal/image processing
• Metaheuristics for time-series forecasting
• Metaheuristics for economics and business intelligence
• Review state-of-arts in intersection of metaheuristics and OR

Special Issue on Integrating Data Science and Decision Analytics
截稿日期: 2024-12-31

The characterizations identified and modeled by data science can inform and enable rigorous decision analytics algorithms for large-scale, complex systems. Vice versa, the capabilities of operations research methods can enhance the learning and representation of patterns and structure in data science. This special volume seeks applied and methodological operations research papers addressing challenging problems using innovative approaches that integrate data science and decision analytics. Submissions should involve four elements: (i) a decision-making component motivating the research need; (ii) a data component, e.g., sensor data streams, surveys, design of experiments, meta-data from computer models, text data; (iii) a statistical learning component that appropriately leverages the data; and (iv) an operations research related methodology, e.g., statistical process monitoring, machine learning, mathematical programming, stochastic simulation modeling, operations management. Application areas include, but are not limited to health care, smart cities, environmental analysis and sustainability, energy and power systems, transportation and logistics, emergency management, law enforcement, manufacturing, supply chain management, finance, marketing, and risk assessment.

Special Issue on Machine Learning Models for Early Misinformation and Disinformation Detection Systems
截稿日期: 2024-12-31

The Annals of Operations Research is currently accepting submissions for a special issue dedicated to “Machine Learning Models for Early Misinformation and Disinformation Detection Systems. ”

The widespread availability of digital platforms and social media channels has made it incredibility easy for disinformation to be disseminated rapidly, reaching millions of people within minutes. However, the spread of misinformation or disinformation can often mislead people, leading them to make decisions based on false, or misleading information. The timely detection of such misleading events has become a critical research area with implications for journalism, social media, online platforms, and beyond. Operations Research (OR) and Machine Learning (ML) methods have shown promise in addressing these challenges by enabling the development of advanced algorithms, models, and systems for detecting and mitigating the spread of false information.

OR and ML fields are connected through their shared goal of optimization and data-driven decision-making. The Special Issue aims to bring together cutting-edge research and advancements in the field of detecting disinformation using OR and ML techniques. This issue will serve as a platform for researchers, academicians, and industry experts to share their original contributions, insights, methodologies, and innovative algorithms in developing cutting-edge models for identifying and mitigating disinformation.

The Special Issue invites original research articles, and application-oriented papers in the following (but not limited to) topics:

• Development of OR and ML-based models for disinformation detection
• Feature engineering and representation learning for disinformation detection
• Deep learning models development for rumour detection and propagation analysis
• Applications of natural language processing (NLP) and sentiment analysis in disinformation detection
• OR and ML techniques for assessing the credibility of information sources
• Early stress detection based on incomplete information
• Social network analysis to understand the dynamics of disinformation spread
• Social media analytics for monitoring and countering disinformation campaigns
• Identifying and countering algorithmic bias in disinformation detection systems
• Network analysis and graph-based algorithms to understand disinformation propagation.
• Case studies on real-world applications of OR and ML in predicting disinformation
• Real-world applications of disinformation detection models in information verification
• Explainable AI and interpretability in disinformation detection systems

Special Issue on Smart and Resilient Operations in the Age of Digitization
截稿日期: 2024-12-31

In the era of globalization, with intensified competition, a large quantity of enterprises face challenges of improving efficiency and coping with disruptions in the industries of manufacturing, storage, and transportation. Recently, the advanced digital technology and intelligent technology, such as big data and analytics, artificial intelligence (AI), blockchain, and internet of things have played increasingly important roles in the theory and methods of operations research. It is critical for enterprises to integrate advanced digital technology including big data and AI with the methodology of operations research to achieve smart and resilient operations. In this perspective, enterprises are able to pursue higher efficiency and stronger stability. This special issue aims to explore the integration between operations research methodology and advanced digital technology to explore smart and resilient operations and solve the rising issues in the improvement of efficiency and to deal with disruptions. This special issue seeks to publish papers that are closely related to operations research problems using digital or intelligent techniques.

The special issue invites submissions in all areas, including, but not limited to:

• Combination of Methods from AI and Operations Research
• AI-Based Modeling and Optimization
• Smart and Resilient Manufacturing
• Resilient Airline Planning and Scheduling
• Data-Driven Warehouse Management
• Explainable AI and Adaptable Optimization Methods
• AI-Driven Decision Making
• Smart and Resilient Supply Chain Management
• Digitization Operations in Healthcare Industry
• Distributed Resilient Optimization in Logistics

Special Issue on Advancing Operational Research with Artificial Intelligence: New Frontiers in Modelling and Simulation with Data driven Learning
截稿日期: 2025-03-10

Annals of Operations Research invites submissions for the special issue on Advancing Operational Research with Artificial Intelligence: New Frontiers in Modelling and Simulation with Data driven Learning. The issue aims to showcase the methodological advancements in combining Modelling and Simulation (M&S) techniques, such as discrete event and agent based simulation, with data driven approaches from Machine Learning (ML), Data Science (DS), Artificial Intelligence (AI), and related fields of study. Conceptual and case study papers are also welcome. Papers for the special issue must demonstrate the added value of combining approaches.

Operations Research (OR) has long been at the forefront of decision making. With the advent of data driven techniques, there are unprecedented opportunities to enrich classical OR approaches through mixing methods. The integrated approach holds the promise of offering new perspectives for understanding complex systems' phenomena, enhancing model accuracy, optimising processes, and reducing computational costs whilst improving decision support capabilities. The OR approach that we focus on is modelling and computer simulation.

We seek high quality contributions that explore the synergies between M&S and AI/ML/DS, particularly the application of AI and ML in enhancing modelling accuracy and efficiency and informing decision making in manufacturing and service sectors. We encourage contributions demonstrating the potential of combining M&S ML/AI/DS in solving complex problems. We welcome submissions that review the literature to identify the current state of the art and the opportunities for future research in this rapidly evolving field. Topics of interest include, but are not limited to:

- Reviews and meta analyses that map the current landscape and future directions of combining M&S with ML/AI/DS
- Theoretical advancements
- Conceptual modelling approaches, for example, using Soft Operations Research techniques such as problem structuring, SSM, and QSD for the development of hybrid AI/ML/DS M&S models
- Contributions related to framework development, for example, studies that aim to combine AI/ML/DS into existing OR and M&S frameworks
- Methodological innovations in integrating AI/ML/DS with traditional M&S techniques, such as discrete event simulation, agent based modelling, system dynamics, and hybrid simulation
- Industry case studies using primary data
- Optimisation and risk analysis using the hybrid M&S AI/ML/DS approaches
- Novel approaches in M&S that leverage AI/ML/DS for enhanced accuracy and efficiency of operations
- Case studies demonstrating the successful application of AI/ML/DS enhanced OR solutions in logistics, healthcare, finance, manufacturing, supply chain management, and other domains

Instructions for authors can be found at:

https://link.springer.com/journal/10479/submission-guidelines

Authors should submit a cover letter and a manuscript by March 31, 2025, via the Journal's online submission site. Please see the Author Instructions on the website if you have not yet submitted a paper through Springer's web based system, Editorial Manager (EM). When prompted for the article type, please select Original Research. On the Additional Information screen, you will then be asked if the manuscript belongs to a special issue, please select the special issue's title, Advancing Operational Research with Artificial Intelligence: New Frontiers in Modelling and Simulation with Data driven Learning, to ensure that it will be reviewed for this special issue. Manuscripts submitted after the deadline may not be considered for the special issue and may be transferred, if accepted, to a regular issue.

Papers will be subject to a strict review process under the supervision of the Guest Editors, and accepted papers will be published online individually, before print publication.

In case of any questions, please contact by email one of the Guest Editors

Special Issue on Operations Research Trends: CLAIO 2024
截稿日期: 2025-04-01

Annals of Operations Research seeks submissions for a special issue on Operations Research Trends: CLAIO 2024. We invite all participants of CLAIO 2024 to submit the extended full version of their presented contributions to this special issue. Papers not presented at the conference are welcome, thus this is open to the OR community worldwide.

The deadline for submission is April 1, 2025.

The main topics of interest are (but not restricted to):

● OR modeling, theory, and algorithms
● OR integration with AI and ML
● OR applications in health care, energy, agriculture, finance, etc.
● Supply chain management
● Transportation and logistics
● Stochastic and robust optimization
● Combinatorial optimization
● Constraint programming
● Multicriteria optimization
● Multicriteria decision-making and evaluation
● Sustainable development goals in OR
● Simulation

Instructions for authors can be found at:

http://www.springer.com/business/operations+research/journal/10479

Special Issue on Operations Research for Sustainable Development
截稿日期: 2025-04-04

The topic of sustainable development has become more important than ever in the academic and policy agendas. In its broader definition sustainability requires to ensure an appropriate balance of economic, health, and environmental needs. The complexity of the current socio-economic system characterized by uncertainty, irreversibility, and tipping points makes such a search of a proper balance particularly difficult.

This special issue will collect a series of works that contribute to the understanding of the challenges associated with the process of sustainable development by exploring the dynamic links among economic activities, health conditions, and environmental outcomes. Specifically, the main goal of this special issue is to develop innovative operations research methods to better support policymakers in their efforts to promote sustainability in a broader sense. It aims to address a number of issues in the context of sustainable development placing particular emphasis on the development and application of novel operations research techniques.

The main topics of interest are:

We welcome contributions addressing several aspects of the dynamic relation among economy, health, and the environment. Papers may rely on a wide range of economic, mathematical, and complexity approaches, making a novel contribution in OR theory and methodology. Topics of special interest are related to the following areas:

• Deterministic and stochastic optimization
• Complexity and network theory
• Dynamic and evolutionary games
• Mean field and stochastic games
• Spatial diffusion and spatial agglomeration
• Multicriteria methods and multi-objective optimization
• Agent-based and computable general equilibrium models
• Artificial intelligence and machine learning

This call for papers is related to the HEDGE (Health, Environment, Development, and Growth Economics) conference which will take place in Pisa on 2-4 September 2024, but it is open to everyone. Both conference participants and non-participants are encouraged to submit high-quality papers dealing with topics related to operations research for sustainable development.

Instructions for authors can be found at: http://www.springer.com/business/operations+research/journal/10479

Authors should submit a cover letter and a manuscript by April 30, 2025, via the Journal’s online submission site. Please see the Author Instructions on the web site if you have not yet submitted a paper through Springer's web-based system, Editorial Manager. When prompted for the article type, please select Original Research. On the Additional Information screen, you will be asked if the manuscript belongs to a special issue, please choose yes and the special issue’s title, Operations Research for Sustainable Development, to ensure that it will be reviewed for this special issue. Manuscripts submitted after the deadline may not be considered for the special issue and may be transferred, if accepted, to a regular issue.

Papers will be subject to a strict review process under the supervision of the Guest Editors, and accepted papers will be published online individually, before print publication.

Special Issue on Multiple Objective Programming and Goal Programming: Sustainability and Beyond
截稿日期: 2025-05-15

This special issue aims to publish selected papers presented during the 15th International Conference on Multiple Objective Programming and Goal Programming (MOPGP'23: http://mopgp.org/) that will be held on 02–04 October 2023, in İzmir, Türkiye. 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.

The topics include all areas of Multiple Objective Programming and Goal Programming (MOPGP) applied to sustainability and its economic, ecological, and social dimensions. Inherited to multi-criteria nature of the sustainability problems, the contribution of MOPGP studies is vital. MOPGP literature naturally embraces sustainability since they have already considered value/impact maximization, cost/risk minimization, increasing efficiency and effectiveness. To this extent, this special issue will cover theories and application of MOPGP focusing on, but not limited to United Nations sustainability goals:

• GOAL 1: No Poverty
• GOAL 2: Zero Hunger
• GOAL 3: Good Health and Well-being
• GOAL 4: Quality Education
• GOAL 5: Gender Equality
• GOAL 6: Clean Water and Sanitation
• GOAL 7: Affordable and Clean Energy
• GOAL 8: Decent Work and Economic Growth
• GOAL 9: Industry, Innovation and Infrastructure
• GOAL 10: Reduced Inequality
• GOAL 11: Sustainable Cities and Communities
• GOAL 12: Responsible Consumption and Production
• GOAL 13: Climate Action
• GOAL 14: Life Below Water
• GOAL 15: Life on Land
• GOAL 16: Peace and Justice Strong Institutions
• GOAL 17: Partnerships to achieve the Goal