Genetic Algorithm-Based Approaches and Their Applications in Operations Research

Dear Colleagues,

Genetic algorithms, as powerful and broadly applicable stochastic search and optimization techniques, are perhaps the most widely known type of evolutionary computation method today. In recent years, research on genetic algorithms has turned much of its attention to various optimization problems in operations research fields. Recently, based on genetic algorithms, various hybrid approaches using genetic algorithms and conventional approaches (e.g., Tabu search, Cuckoo search, particle swarm optimization, ant colony optimization, etc.) have been proved to be effective and efficient in many optimization problems. Genetic algorithm-based approaches have also been adapted to logistics/supply chain management, advanced planning/scheduling, and production/distribution planning. This Special Issue of Mathematics aims to address the critical issues involved in “Genetic Algorithm-Based Approaches and Their Applications in Operations Research”.

The purpose of this Special Issue is to promote, exchange, and disseminate information and research results on genetic algorithm-based approaches and their applications in operations research. This Special Issue will address many important problems of interest in this area. Potential topics include, but are not limited to:

• Genetic algorithm-based approaches (e.g., hybrid genetic algorithm, adaptive hybrid genetic algorithm, etc.);
• Other intelligent-related algorithms (e.g., Tabu search, Cuckoo search, particle swarm optimization, ant colony optimization, etc.);
• Soft computing and metaheuristics applications in various operations research fields (e.g., linear/nonlinear optimization models, transportation/assignment problems, inventory models, network models, decision analysis, multicriteria decisions, etc.);
• Logistics/supply chain management, advanced planning/scheduling, and production/distribution planning using genetic algorithm-based approaches.

Prof. Dr. YoungSu Yun
Dr. JeongEun Lee
Guest Editors

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• Genetic algorithm
• Operations research
• Soft computing
• Metaheuristics
• Logistics
• Supply chain management