Location and allocation decisions for multi-echelon supply chain network – A multi-objective evolutionary approach

• Published in: Expert systems with applications Vol. 40; no. 2; pp. 551 - 562
• Main Authors: Latha Shankar, B., Basavarajappa, S., Chen, Jason C.H., Kadadevaramath, Rajeshwar S.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.02.2013; Elsevier
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Decision theory. Utility theory; Decisions; Demand; Evolutionary; Evolutionary approach; Exact sciences and technology; Four-echelon supply chain architecture; Inventory control, production control. Distribution; Logistics; MOHPSO; Networks; Non-dominated sorting algorithm; Operational research and scientific management; Operational research. Management science; Optimization; Position (location); Supply chains; Swarm intelligence; Non-dominated sorting algorithm; Swarm intelligence; Evolutionary approach; MOHPSO; Four-echelon supply chain architecture; Measurement; Costs; Material flow; Evolutionary algorithm; Multiobjective programming; Raw materials; Multiple production; Modeling; Location problem; Multiple decision; Facility location; Supplier; Monitoring; Plant production; Decision support system; Decision making; Particle swarm optimization; Supply demand balance; Surveillance; Genetic algorithm; Heuristic method; User requirement; Capacity constraint; Production capacity; Objective function; Artificial intelligence; Logistics
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.07.065

This paper aims at multi-objective optimization of single-product for four-echelon supply chain architecture consisting of suppliers, production plants, distribution centers (DCs) and customer zones (CZs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to DCs, from DCs to CZs so as to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met. To optimize these two objectives simultaneously, four-echelon network model is mathematically represented considering the associated constraints, capacity, production and shipment costs and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization (MOHPSO) algorithm. This evolutionary based algorithm incorporates non-dominated sorting algorithm into particle swarm optimization so as to allow this heuristic to optimize two objective functions simultaneously. This can be used as decision support system for location of facilities, allocation of demand points and monitoring of material flow for four-echelon supply chain network.