| Summary: | 碩士 === 國立嘉義大學 === 運輸與物流工程研究所 === 97 === In order to respond to rapid economic and political changes, many enterprises have engaged actively in developing global production and a supply network whose members include a set of existing or potential manufacturing facilities, warehouses, distribution centers, customers, and so forth. One of the characteristics of the global supply chain is that their members can be located in different areas or in countries with different business conditions such as tax rates and currencies. This may lead these to the enterprises achieving some performance in profitability, cost reduction or customer service. However, in order to take the advantage of a global supply network, enterprises must face problems such as which plants to operate, what product mix is to be used per plant, which distribution centre supplies which customer, what inventory levels are necessary to maintain service levels, and which suppliers are to be used.
This paper deals with the optimal design and operation for a three-echelon supply chain system with multiple products, manufacturing sites at fixed locations, distribution centers and customer zones at fixed locations. A product can be produced at its manufacturers or obtained through subcontracting. Operational costs include those associated with production, material handling, transportation and duties. Most of the previous works assume that transportation costs are linear functions of the units of products between source stages and destination stages. However, since this assumption may ignore a situation in which transportation costs are counted by the number of cartons or by trucks loaded. In this paper, we assume that transportation costs are calculated based on the number of trucks loaded. The decisions to be determined include the product portfolio per production plant, production amounts, utilization level, and shipping flow. The purpose of this is to minimize the total annual cost of the network, taking into account both infrastructure and operating costs. The mathematical model established in this research is a constrained linear integer programming problem. Several examples are used to illustrate the insight into the problem. In addition, a sensitivity analysis was also conducted to illustrate the impact of variation within the parameters on the computational results.
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