Aims & Scope:
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 knowledgebased 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
Instructions for authors can be found at: https://www.springer.com/journal/10479/submission-guidelines
Authors should submit a cover letter and a manuscript by June 30, 2024, 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, Ensemble AI-Driven Metaheuristic Optimization in OR: Newest Contributions in Theory, Methods, and Applications, 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.
• Dr. Mohammad Shokouhifar
Department of Electrical and Computer Engineering, Shahid Beheshti University, Tehran, Iran.
Email: email@example.com & firstname.lastname@example.org
Google Scholar: https://scholar.google.com/citations?user=Of8s98UAAAAJ&hl=en&oi=ao
• Dr. Alireza Goli
Department of Industrial Engineering, University of Isfahan, Isfahan, Iran.
Google Scholar: https://scholar.google.com/citations?user=dthFAvwAAAAJ&hl=en&oi=ao
• Dr. Zaoli Yang
College of Economics and Management, Beijing University of Technology, Beijing, China.
Google Scholar: https://scholar.google.com/citations?user=R-0th5EAAAAJ&hl=en&oi=ao
• Prof. Dr. Gerhard-Wilhelm Weber
Faculty of Engineering Management, Poznan University of Technology, Poland.
Google Scholar: https://scholar.google.com/citations?user=zOiT4ZQAAAAJ&hl=en&oi=ao