一雙目標規劃模式進行組裝次序及供應商評選同步規劃

A Bi-objective Model for Concurrent Planning of Supplier Selection and Assembly Sequence Planning

林瑤瑩(Yao-Ying Lin)

指導教授 ： 車振華

國立臺北科技大學/管理學院/工業工程與管理研究所/碩士(2009年)

摘要

本研究針對中心組裝廠評選產品零件之供應商問題進行探討。研究中指出於供應商遴選(Suppliers selection)時除應考量供應商之供貨狀況，亦應同時顧及組裝廠之生產情態。因此，本研究針對此問題建構一雙目標最佳化數學模式，並將組裝次序規劃(Assembly sequence planning, ASP)同步納入考量。同時，本研究採用非支配解排序基因演算法II(Non-dominated sorting genetic algorithm II, NSGA-II)與非支配解排序粒子群最佳化(Non-dominated sorting particle swarm optimization, NSPSO)進行模式求解，並發展混合型多目標演算法(Hybrid multi-objective algorithm, HMOA)。HMOA結合二種全域最佳化演算法，採用基因作業及粒子群速度與位置更新機制同步進行模式求解，流程中加入非支配解排序、排擠比較機制與利基法。一訂書機組裝案例被用以驗證HMOA之求解績效，評估結果顯示HMOA在精確性量測顯著優於NSGA-II與NSPSO，在分佈均勻性與延展性的量測比較上也較NSGA-II優異。

關鍵字

供應商評選 ； 組裝次序規劃 ； 多目標最佳化 ； 非支配解排序基因演算法II ； 非支配解排序粒子群最佳化

並列摘要

This study discussed parts supplier selection for central assembly plant. In addition to the delivery state of suppliers, the production conditions of the central assembly plant should be considered. A bi-objective optimal mathematical model was thus established for supplier selection in this study, which incorporates assembly sequence planning (ASP). At the same time, the hybrid multi-objective algorithm (HMOA) which combines Non-dominated sorting genetic algorithm II (NSGA-II) and Non-dominated sorting particle swarm optimization (NSPSO) was used to solve the model for a case of stapler assembly, in order to validate the solving effects of HMOA. The results showed that HMOA is not only more exact than NSGA-II and NSPSO, but also have a better distribution and a wider extent than NSGA-II.

並列關鍵字

Supplier Selection ； Assembly Line Balancing ； Assembly Sequence Plan ； Non-dominated Sorting Genetic Algorithm II ； Non-dominated Sorting Particle Swarm Optimization

參考文獻

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