期刊信息

Annals of Operations Research

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影响因子:
4.5
出版商:
Springer
ISSN:
0254-5330
浏览:
27218
关注:
4

征稿

Annals of Operations Research is an academic journal published by Springer. (ISSN 0254-5330, impact factor 4.5).

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
由 Dou Sun 最后更新于

Special Issues

Special Issue on Stochastic Optimization in Agriculture 截稿日期: 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
由 Dou Sun 最后更新于
Special Issue on Multiple Objective Programming and Goal Programming: Artificial Intelligence for Decision Making in Economic and Social Sciences 截稿日期: 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.
由 Dou Sun 最后更新于

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