| Summary: | 碩士 === 國立臺北科技大學 === 工業工程與管理系碩士班 === 101 === In general, there are more than one goal in supply chain management (SCM); such as,
total cost minimization and service level maximization for target planning when
choosing a supplier. There may be many constraints among suppliers, manufacturers,
distributors and customers in supply chain network analysis; however, how to meet
these conditions, to achieve each goal with an ideal level and to control input and
output of supply chain network as well as other decision variables are the
multi-objective decision problem. In the past, several feasible solutions of this kind of
problems could be provided for decision maker to choose from a set of solutions. A
decision maker could choose an optimal solution based on personal preference and
experience. Nevertheless, this kind of decision may vary from person to person. In
order to make each decision quality has rules to follow and stable, this study applies
data mining and derives some decision rules among a set of solutions. Moreover, the
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method could also become the base of knowledge management. The objective functions
of the supply chain network design are to minimize of total cost and maximize the service levels. Capacity, balance of supply and demand, customers’ requirement and other constraints are also taken into consideration. To help a decision maker to make the most practical decision, this study applies the Genetic Algorithm to obtain a set of feasible solutions. Then, we use the Cluster Analysis to categorize these solutions and generate the decision rules through Rough Set Theory. This integrated method considers various variables and makes supply and demand more stable. Based on decision rules, a decision maker could make a decision easily and objectively. In the study, a precision machinery assembly company is chosen as an example. According to this real data and supply chain network design, the empirical case study shows the model is practical and effective.
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