臺灣博碩士論文加值系統

過去對於供應鏈之管理著重於產品由原料進入製造工廠加工而後裝配，再由經銷商進行銷售予顧客，此流程為傳統正向供應鏈產品處理之過程，但是這樣的供應鏈管理並不完整，而且產品售出並不代表服務已結束，當產品出現損壞導致無法正常使用或者是在製造過程中所產生的瑕疵品，在此情況下就須將產品回收送回製造工廠進行重製，這樣的流程就稱為逆向物流(Reverse Logistics)。本研究之逆向物流著重於瑕疵品回收，在供應鏈中各成員在對於產品進行加工時所造成的產品瑕疵都需交由上ㄧ階供應鏈成員進行重製，而在複雜的供應鏈網路中，如何選擇供應商以及運用逆向物流將產品回收，並對瑕疵品進行處理，如此複雜之問題中包含了瑕疵品處理廠選擇、重製成本最小化之原則等考量因素，所以企業在面對此項挑戰時，供應鏈各成員之配合將是企業優勢保持之最大成敗關鍵。本研究為了解決此問題，將利用粒子群演算法來求解產品回流至供應鏈中所造成之生產系統不平衡問題，依據產品瑕疵情形將資訊回饋而後選擇供應商重製，並且考量相關處理成本(如生產成本、重製成本)與供應商特性(如產能上限、生產瑕疵率)加以建構模組。最後並以實例進行分析，結果顯示本研究所提出之求解方法可以有效的進行瑕疵品逆回至供應鏈中的途程規劃，並且在決策過程中全盤考量供應鏈成員之相關成本與限制，相信能有效降低決策時間進而提高供應鏈的管理能力。

In traditional supply chain management, the researchers focus on the production procedure in which the materials/WIPs/products are processed from the supplier, manufacturer, and distributor to the customer. However, several finish products, which have shipped to the customer, are needed to be retrieved and delivered back to the supplier or manufacturer to perform reworking for quality ensuring. The logistics system with retrieval processes, is called reverse logistics, is an important issues in the complete supply chain management recently. In this research, we focus on the multi-echelon supply chain design problem with reverse logistics and the main objective is to develop a mathematical model to make the partner selection and production-distribution planning strategies. Owing to the product loss and retrieve, and multi-period are considered in this problem the particle swarm optimization algorithm based model is developed and used to solve it. Based on the proposed model, the near-optimal supply chain design strategy is made with fulfilling the customer demand and capacities of all feasible elected partners. Finally, this model is applied on a multi-echelon supply chain system so as to show the ease of fulfillment and illustrate the feasible quality solution of real world application.

摘 要 ii
ABSTRACT iii
目 錄 iv
表 v
圖 vi
第一章 研究動機與目的 1
1.1 研究背景與動機 1
1.2 研究目的 2
第二章 文獻探討 4
2.1 學者與各國研究單位對物流之定義 4
2.2 逆向物流之定義 6
2.2.1 逆向物流的架構-產品回收程序 7
2.3 逆向物流之相關研究 12
2.4 供應商選擇之方法 16
2.5 粒子群演算法(PSO) 20
2.5.1 粒子群演算法(PSO)之更新法則 24
2.5.2 粒子群演算法的程式概念 25
2.5.3 粒子群演算法相關文獻 27
第三章 研究方法 30
3.1 問題描述與定義 30
3.2 研究架構 31
3.3 最佳化規劃模式 33
3.4 粒子群演算法模型建構 34
第四章 實證結果與分析 36
4.1 案例問題描述 36
第五章 結論與後續發展 42
5.1 結論 42
5.2 後續研究與發展 42
參考文獻 44

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