Planning and coordination of production and distribution facilities for multiple commodities |
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| Institution: | 1. Department of Management and Decision Sciences, Washington State University, 14204 NE Salmon Creek Avenue, Vancouver, WA 98686 USA;2. School of Management, University of Texas – Dallas, Box 830688, Richardson, TX 75083 USA;1. Faculdade de Ciências Aplicadas, Univ. Estadual de Campinas, 13484-350 Limeira, SP, Brazil;2. HEC Montréal and CIRRELT, H3T 2A7 QC, Canada;3. Departamento de Matemática Aplicada, Univ. Estadual Paulista, 15054-000 São José do Rio Preto, SP, Brazil;1. School of Economics & Management, Tongji University, Shanghai 200092, China;2. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China;3. Business Analytics and Optimization, University of Phoenix, Apollo Group, Phoenix, AZ, USA;4. Logistics Engineering and Simulation Laboratory, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China;1. Department of Industrial Engineering, Bilkent University, Ankara, Turkey;2. Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran |
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| Abstract: | We study an integrated logistics model for locating production and distribution facilities in a multi-echelon environment. Designing such logistics systems requires two essential decisions, one strategic (e.g., where to locate plants and warehouses) and the other operational (distribution strategy from plants to customer outlets through warehouses). The distribution strategy is influenced by the product mix at each plant, the shipments of raw material from vendors to manufacturing plants and the distribution of finished products from the plants to the different customer zones through a set of warehouses. First we provide a mixed integer programming formulation to the integrated model. Then, we present an efficient heuristic solution procedure that utilizes the solution generated from a Lagrangian relaxation of the problem. We use this heuristic procedure to evaluate the performance of the model with respect to solution quality and algorithm performance. Results of extensive tests on the solution procedure indicate that the solution method is both efficient and effective. Finally a `real-world' example is solved to explore the implications of the model. |
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