1PhD Candidate, Industrial Engineering Department, Amirkabir University of Technology, Tehran, Iran
2Assistant Professor, Industrial Engineering Department, Amirkabir University of Technology, Tehran, Iran
3Associate Professor, Industrial Engineering Department, Amirkabir University of Technology, Tehran, Iran
A comprehensive and integrated study of any supply chain (SC) environment is a vital requirement that can create various advantages for the SC owners. This consideration causes productive managing of the SC through its whole wide components from upstream suppliers to downstream retailers and customers. On this issue, despite many valuable studies reported in the current literature, considerable gaps still prevail. These gaps include integration and insertion of basic concepts, such as queuing theory, facility location, inventory management, or even fuzzy theory, as well as other new concepts such as strategic planning, data mining, business intelligence, and information technology. This study seeks to address some of these gaps. To do so, it proposes an integrated four-echelon multi-period multi-objective SC model. To make the model closer to the real world problems, it is also composed of inventory and facility location planning, simultaneously. The proposed model has a mixed integer linear programming (MILP) structure. The objectives of the model are reducing cost and minimizing the non-fill rate of customer zones demand. The cost reduction part includes cost values of raw material shipping from suppliers to plants, plant location, inventory holding costs in plants, distribution cost from plants to warehouses or distribution centers (DCs), and shipping costs from DCs to customer zones. Finally, since the literature of SC lacks efficient Pareto-based multi-objective evolutionary algorithms (MOEAs), a new multi-objective version of the biogeography-based optimization algorithm (MOBBO) is introduced to the literature of the SC. The efficiency of the algorithm is proved through its comparison with an existing algorithm called multi-objective harmony search (MOHS).