Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Joint Location-Inventory Model
Transportation Science
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Applications of particle swarm optimisation in integrated process planning and scheduling
Robotics and Computer-Integrated Manufacturing
Supply chain modeling in uncertain environment with bi-objective approach
Computers and Industrial Engineering
Computers and Operations Research
Global optimization of an optical chaotic system by Chaotic Multi Swarm Particle Swarm Optimization
Expert Systems with Applications: An International Journal
About selecting the personal best in multi-objective particle swarm optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A profit-maximizing supply chain network design model with demand choice flexibility
Operations Research Letters
Expert Systems with Applications: An International Journal
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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.