近年來，隨機資源分配問題（Stochastic resource allocation problem, SRAP）已經被學者們應用在製造系統或是服務系統當中，之所以被人們重視的原因是因為在一個系統當中，資源分配的妥當會影響到整個系統的績效指標（system performance）。資源分配問題的最終目標無非是在滿足資源限制的條件下將資源分配給各個活動(activity)，以及尋求一個可以使成本最小化的方案。然而這個問題的困難之處在於系統輸出的績效指標有著隨機性以及無法透過一個解析函數來表示系統績效指標，必須透過重複模擬的結果來評估系統的績效。
在本篇研究當中，我們以簡化群體演算法(simplified swarm optimization, SSO)結合Optimal computing budget allocation 和區域搜尋法來解決流線型生產線的資源分配問題。SSO被用於大範圍的全域搜尋，OCBA用於分配重複擬次數來選出群體中最佳解。在求解的過程中，進行模擬來評估系統績效是最耗時的一個步驟，透過OCBA的模擬資源分配我們可以有效率地將模擬資源花在變異較大或是較佳的解，可以有效率的辨識出群體中的最佳解。隨後在利用先前的模擬結果進行區域搜尋法，將機台的使用率作為區域搜尋的依據，透過區域搜尋法尋找出鄰近的最佳解。最後，本篇研究所提出的融合式簡化群體演算法(hybrid simplified swarm optimization, HSSO)，非常適合用於在隨機資源分配問題，相較於其他演算法，HSSO可以尋找到更佳品質的解。

Stochastic resource allocation problem (SRAP) has been applied in manufacturing system and service system. Resource allocation is a crucial step in system because system performance and cost can be improved by properly allocating resource to each activity. Allocating resource to each activity under resource constraints and minimizing overall costs are the goal of SRAP. However, the system performance can’t be formulated as a closed-form expressions function and system performance is uncertainly.
In this study, we proposed a hybrid algorithm based on Simplified Swarm Optimization (SSO) combining with Optimal Computing Budget Allocation (OCBA) and a local search method for SRAP. SSO is applied for global search and OCBA is used to allocate the simulation replications for recognizing the best solution in particle. The core concept of OCBA is allocating the simulation budget to the solutions which has larger standard deviation or superior system performance. Therefore, we can avoid to wasting simulation budget on the solutions with worse system performance and providing reliable solutions. Afterwards, we proposed a local search strategy based on the utilization of machines. By using this local search method we can search local optimum. Finally, the experimental results show that the proposed Hybrid Simplified Swarm Optimization is better than other algorithms.

摘要 I
ABSTRACT II
目錄 III
圖目錄 VI
表目錄 VII
簡稱 VIII
第一章、 研究介紹 1
1.1 隨機資源分配問題的重要性與背景 1
1.2 多產品生產系統之問題與動機 3
1.3 研究架構 4
第二章、 文獻回顧 5
2.1 模擬最佳化 5
2.2 製造系統資源配問題 6
2.3 簡化群體演算法(SIMPLIFIED SWARM OPTIMIZATION, SSO) 7
2.4 最佳資源分配(OPTIMAL COMPUTING BUDGET ALLOCATION, OCBA) 8
第三章、 模型描述 10
3.1 符號 10
3.2 模型假設 11
3.3 模型說明 11
3.4 數學模型 14
3.5 研究模型總結 15
第四章、 研究方法 16
4.1 編碼 16
4.2 融合式簡化群體演算法流程說明 16
4.21 簡化群體演算法 16
4.22 OCBA (Optimal Computing Budget Allocation) 19
4.23 區域搜尋法 22
4.3 融合式演算法總結 25
第五章、 實驗結果 27
5.1 簡化群體演算法實驗設計 27
5.2 OCBA參數設定 28
5.3 實驗情境說明 30
5.31 實驗說明 30
5.32 實驗情境 35
5.33 實驗結果 36
第六章、 結論與未來研究方向 39
6.1 結論 39
6.2 未來研究方向 40
參考文獻 41

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