Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm

Designing and planning a closed-loop supply chain in a comprehensive structure is vital for its applicability. To cope with the design and planning issue of a comprehensive closed-loop supply chain network, this paper develops an extended model, which is multi-echelon, multi-product, and multi-perio...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2013-09, Vol.68 (1-4), p.917-931
Main Authors: Soleimani, Hamed, Seyyed-Esfahani, Mirmehdi, Shirazi, Mohsen Akbarpour
Format: Article
Language:English
Subjects:
CAE) and Design
Computer-Aided Engineering (CAD
Distributors
Engineering
Genetic algorithms
Industrial and Production Engineering
Integer programming
Linear programming
Mechanical Engineering
Media Management
Mixed integer
Optimization
Original Article
Supply chains
Warehouses
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Summary:Designing and planning a closed-loop supply chain in a comprehensive structure is vital for its applicability. To cope with the design and planning issue of a comprehensive closed-loop supply chain network, this paper develops an extended model, which is multi-echelon, multi-product, and multi-period in a mixed integer linear programming framework. The word “comprehensive,” in our mathematical approach, in designing and planning a closed-loop supply chain problem, can be analyzed from two complementary angles: including all possible entities (facilities) of a real condition and considering minimum limitations on possible flows between entities. In our proposed model, customers can be supplied via manufacturers, warehouses, and distributors, as an example. The proposed model is solved by CPLEX optimization software and by a developed genetic algorithm. During this computational analysis, we compare results of proposed pretuned genetic algorithm with a global optimum of CPLEX solver. Then, a sufficient number of large-size instances are generated and solved by the proposed genetic algorithm. To the best of our knowledge, there has been no similar multi-period multi-product closed-loop supply chain design and planning problem utilizing any kind of meta-heuristics let alone genetic algorithms. Therefore, in this issue, it is an original research, and results prove the acceptable performances of the developed genetic algorithm.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-013-4953-6