An evolutionary approach to multi-objective scheduling of mixed model assembly lines
Computers and Industrial Engineering
Computers and Industrial Engineering - Supply chain management
Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach
Computers and Industrial Engineering - Supply chain management
A strategic model for supply chain design with logical constraints: formulation and solution
Computers and Operations Research
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
A steady-state genetic algorithm for multi-product supply chain network design
Computers and Industrial Engineering
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers and Industrial Engineering
Hybrid genetic algorithm for multi-time period production/distribution planning
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
Network optimization in supply chain: A KBGA approach
Decision Support Systems
Computers and Industrial Engineering
International Journal of Computer Applications in Technology
Reliability evaluation of a stochastic-flow distribution network with delivery spoilage
Computers and Industrial Engineering
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Supply chain network (SCN) design is a strategic issue which aims at selecting the best combination of a set of facilities to achieve an efficient and effective management of the supply chain. This paper presents an innovative encoding-decoding procedure embedded within a genetic algorithm (GA) to minimize the total logistic cost resulting from the transportation of goods and the location and opening of the facilities in a single product three-stage supply chain network. The new procedure allows a proper demand allocation procedure to be run which avoids the decoding of unfeasible distribution flows at the stage of the supply chain transporting products from plants to distribution centers. A numerical study on a benchmark of problems demonstrates the statistical outperformance of the proposed approach vs. others currently available in literature in terms of total supply chain logistic cost saving and reduction of the required computation burden to achieve an optimal design.