Computers and Operations Research
Network design techniques using adapted genetic algorithms
Advances in Engineering Software
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Proceedings of the 5th International Conference on Genetic Algorithms
A multiobjective hybrid genetic algorithm for the capacitatedmultipoint network design problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Review article: A review of soft computing applications in supply chain management
Applied Soft Computing
Computers and Industrial Engineering
Computers and Industrial Engineering
Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains
Information Sciences: an International Journal
Genetic algorithm with iterated local search for solving a location-routing problem
Expert Systems with Applications: An International Journal
Survey: Facility location dynamics: An overview of classifications and applications
Computers and Industrial Engineering
Supply Hub in Industrial Park (SHIP): The value of freight consolidation
Computers and Industrial Engineering
An integrated supply chain network design problem for bidirectional flows
Expert Systems with Applications: An International Journal
International Journal of Knowledge-based and Intelligent Engineering Systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, we deal with a production/distribution problem to determine an efficient integration of production, distribution and inventory system so that products are produced and distributed at the right quantities, to the right customers, and at the right time, in order to minimize system wide costs while satisfying all demand required. This problem can be viewed as an optimization model that integrates facility location decisions, distribution costs, and inventory management for multi-products and multi-time periods. To solve the problem, we propose a new technique called spanning tree-based genetic algorithm (hst-GA). In order to improve its efficiency, the proposed method is hybridized with the fuzzy logic controller (FLC) concept for auto-tuning the GA parameters. The proposed method is compared with traditional spanning tree-based genetic algorithm approach. This comparison shows that the proposed method gives better results.