Abstract:
A supply chain is a set of facilities, suppliers, customers, products and methods of controlling inventory, purchasing, and distribution. In a supply chain, the flow of goods between a supplier and customer passes through several stages, and each stage may consist of many facilities.
The planning is designed to integrate supplier selection, product assembly, distribution center (DC) location selection as well as the logistic distribution system of the supply chain in order to meet market demands. In this research, the supplier selection problem is integrated with production decision and distributor location problems and a new mathematical model is proposed. While the most successful companies are aiming for total customer satisfaction, it is important to quantify service quality of suppliers so Taguchi loss function is considered in decision making process for calculating the loss in monetary terms, in case of poor supply performance.
With multiple suppliers and multiple customer needs, the assembly model can be divided into several sub-assembly steps by applicable sequence. Considering three evaluation criteria, namely costs, delivery time, and quality a multi-objective optimizationmathematical model is established in this study. The multi-objective problems usually have no unique optimal solution, and the Pareto genetic algorithm (PaGA) can find good tradeoffs among all the objectives. Therefore, this study proposes a modified Pareto genetic algorithm (mPaGA) to improve the solution quality through revision of crossover and mutation operations.The results show that the proposed algorithm gives high quality solutions as well as better computational times.
