臺灣博碩士論文加值系統

要了解供應鏈體系，可以藉由供應鏈模型來加以了解。固定的模型，可以表示目前的情況，但是外在環境是會改變的。今天的模型經過了一段時間後，先前所決定的策略已經不適用於經過時間和環境變動後的情況，必須要更改策略，決定出適合現今狀態的策略。為了讓模型能夠達到此一目標，所以要讓模型具有學習及適應的能力，以適應時間及環境的改變。
在供應鏈系統中，分布於不同階層的各單元，像是供應商、經銷商、物流中心、零售商。自主但是互相依賴地做其決策。利用代理人程式，可以表現出像人類般的思考及行為，所以將供應鏈系統不同階層的各單元像是供應商、經銷商、物流中心、零售商等，以代理人程式來表達並建立供應鏈模型，則更能夠真實的表達出真實世界裡的供應鏈體系。
在用代理人來建構供應鏈模型時，對於代理人所要採行的策略，是依據初始建構模型時的情況先行定義出一較佳策略。經過時間和環境的變化以及供應鏈上代理人之間的互動之後。先前的策略可能已經不是較佳的策略，甚至此策略可能已經變成無效了。這時代理人應該要調整它的策略，應用它的智慧找出適合此情況的策略。為了達到此一目標，則代理人應該要能夠根據過去的經驗來學習，對於環境的變化，依據過去學習的經驗找出較佳的策略。本研究設計了供應鏈上代理人內部學習的機制並說明代理人如何在供應鏈上收集資訊以進行學習。最後使用模擬工具建構具有學習能力的多位代理人供應鏈模型和原始不具有學習能力的模型比較，實驗結果顯示具有學習能力的模型具有較好的績效表現。

To provide customer with low cost of products and high service level, logistics plays an important role. As companies move into internationalization, logistics systems become much more complex and difficult to manage. Many international companies implemented global logistics systems, or manugisitics to restructure their logistics structure and product design module in order to get closer to market to provide responsive but low cost product and service. Before manugistics is implemented, companies have to consider strategic decision variables such as manufacturing strategy (make to stock, make to order, and assemble to order), batch quantity, product design postponement strategy, information sharing, etc. Logistics systems can be viewed as supply chain networks, with entities spreading out echelons of channels working autonomous and interdependent toward a common goal of providing end customers with high service level but low cost. Multi-agent approach, therefore, can be used for modeling entities of supply chain networks that are intelligent and adaptive. Learning capability of agents is considered in the model for further improvement on the ability of agents. Traditional knowledge base method, as well as neural network approach is considered complementarily.
The model built can evolve with time as more rules are implemented in the model to represent closer real world supply chain business operations. For international companies, the model can help them determine significant strategic decision variables before implementation.

誌謝 i
中文摘要 ii
ABSTRACT iii
目 錄 iv
表 目 錄 vi
圖 目 錄 vii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究範圍與限制 4
1.5 論文架構與研究流程 4
第二章 文獻回顧 7
2.1 供應鏈管理 7
2.1.1 供應鏈管理的定義 7
2.1.2 供應鏈模式 10
2.2 代理人理論 16
2.2.1 代理人的定義 17
2.2.2 代理人的分類 18
2.2.3 代理人在供應鏈管理上的應用 20
2.3 代理人的學習 23
2.3.1 代理人學習領域的相關研究 23
2.3.2 類神經網路 25
2.3.3 類神經網路理論 26
2.3.4 倒傳遞類神經網路 28
2.3.5 增強式學習簡介 31
第三章 模式建構 35
3.1 多位代理人供應鏈模型 35
3.1.1 模擬軟體適用性說明 35
3.1.2 使用SIMPLE ++建構模型 40
3.2 具學習能力之多位代理人供應鏈模型 48
3.2.1 學習角色的界定 48
3.2.2 學習代理人 49
3.2.3 學習代理人的學習過程 58
第四章 實驗設計與資料分析 61
4.1 實驗假設 61
4.2 代理人學習過程實驗 62
4.3 模式比較實驗 65
4.4 實驗總結 74
第五章 結論與建議 75
5.1 研究結論 75
5.2 後續研究建議 76
參考文獻 77

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