臺灣博碩士論文加值系統

在市場競爭激烈的環境下各家企業都希望以最低的成本將正確數目與品質的產品在正確的地方以及準確的時間送達顧客手中。然而在整體供應鏈管理(Supply Chain Management)中，由於供應鏈間的層層供應廠商為了因應終端產品的短生命週期及快速顧客回應，以致於造成各層供應廠商庫存的擠壓及需求放大，如此便使得製造過多的成品零件及物料的缺貨損失，此現象稱為長鞭效應(Bullwhip Effect)問題。因此在供應鏈管理中如何採行有效的存貨管理策略以滿足顧客持續變動與不固定性的需求是供應鏈存貨管理之首要任務。
本研究透過啤酒遊戲(Beer Game)，對單一供應鏈體系進行模擬，首先建構供應鏈物流架構與供應鏈成員的存貨策略，再以模擬的方法模擬各種存貨策略以建立最佳供應鏈存貨管理模式。運用基因演算法(Genetic Algorithm, GA)建構智慧型代理人(Intelligent Agent)系統以尋找最佳供應鏈存貨策略，研究代理人是否比人類能更有效的管理供應鏈存貨？代理人在面對固定與變動的環境中能否發現有效的供應鏈存貨管理策略？代理人是否能發現好的存貨管理策略以降低長鞭效應？

In the global supply chain management, because every supplier responds to the short life-cycle and quick customer response of the terminal products, each layer of suppliers squeezes the stock and enlarges the demand, such make the excessive products and shortage of materials, this phenomenon calls the Bullwhip effect. Therefore, how to adopt the valid inventory policies to satisfy stochastic customer demand is the first important thing of the supply chain management.
To illustrate and evaluate our research, we simulate the single supply chain system by MIT Beer game. First, we construct the supply chain structure and inventory policies of facilities then find the optimal inventory policies by computer simulation. We construct intelligent agents system by genetic algorithm to search optimal inventory policies.

摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章　緒論 1
1.1研究背景與動機 1
1.2研究目的 2
1.3研究流程 3
第二章　文獻探討 5
2.1供應鏈管理 5
2.2長鞭效應 5
2.3需求預測對於長鞭效應的效果 7
2.4存貨策略對於長鞭效應的效果 9
2.4.1主要的存貨策略 9
2.4.2存貨策略對於總需求變異的影響 10
2.5代理人 11
2.5.1代理人在供應鏈的應用 12
2.6基因演算法(GENETIC ALGORITHM) 13
第三章　研究方法與進行步驟 19
3.1 研究方法 19
3.2 MIT啤酒遊戲(BEER GAME) 21
3.3存貨策略 23
3.3.1混用存貨策略(Mixed inventory policies) 23
3.3.2訂購數量法(Ordering quantity, “x + y” rule) 25
3.3.3最佳參數策略(Optimal parameter policy) 26
3.4 建構代理人 26
3.4.1 成本函數 26
3.4.2 混用策略演算法(Mixed-policy algorithm)(MPA) 27
3.4.3 策略之規則表示 27
3.4.4 規則學習 29
3.5 系統實作 29
第四章　研究結果 34
4.1 實驗一 34
4.2 實驗二 38
4.3 實驗三 42
4.4 實驗四 47
4.5 實驗五 53
4.6 實驗六 57
第五章　結論與建議 61
參考文獻 63

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