緩衝區配置問題(Buffer Allocation Problem)是在建立生產系統上很重要的問題，本研究所探討的問題同時求解生產線上機台選擇(Machine Selection Problem)與緩衝區配置問題是一個NP-hard問題，本研究提出的數學模型為最大化單位時間產線產出量，限制為緩衝區空間限制及機台購買成本限制，而本研究考慮一個複雜的多產品組裝生產線系統，此生產線可為任意機台數量且生產線樣式也不受限制，並同時加入不同的隨機因子如機台產出率、損壞間隔時間以及維修時間並給予機台產出有機率損壞進行報廢再製，這些隨機因子皆可服從任意機率分配，因此無法用解析方式直接求得產線產出，故本研究採用模擬最佳化的方式來求解。
本研究在模擬產線產出方面使用FlexSim模擬軟體來進行模擬，並發展一套全新的兩階段演算法(Revised RSP-APGHLS)來處理此問題，Revised RSP-APGHLS的概念是將求解機台選擇0-1變數的快速篩選法(Rapid Screening Procedure)與求解緩衝區大小離散型變數的適應性粒子全域與超盒子區域演算法(Adaptive Particle Global and Hyperbox Local Search)做結合，並將本研究所提出之Revised RSP-APGHLS與現有常用的基因演算法、Nelder-Mead演算法以及適應性禁忌搜尋法做比較，經過數值分析實驗結果顯示本研究所提出之演算法的計算效果與效率表現更佳。
而本研究之主要貢獻如下，將緩衝區配置問題與機台選擇問題做結合並應用於一個複雜的多產品組裝生產線上，相較過去的研究更能符合現實生產線決策情況。另一貢獻為提出一個全新且在求解此緩衝區配置結合機台選擇問題十分有效率的演算法。

Buffer allocation problems are well-known topics in manufacturing system research. A proper allocation can significantly improve the system performance. In this study, we combine the buffer allocation problem and machine selection problem. We focus on how to choose the appropriate type of machine and decide the buffer size to be allocated under the limited cost constraint and limited total buffer size constraint while maximizing the production line throughput. This problem is a type of combinatorial optimization problem. This problem is also a NP-hard problem.
This study use FlexSim to simulate the production line and also develop an efficiency two-staged optimization algorithms which based on the Rapid Screening Procedure(RSP) and Adaptive Particle Global and Hyperbox Local Search(APGHLS) is called Revised RSP-APGHLS. We use Revised RSP to decide the better machine selection solution which is a zero-one variable and use APGHLS to decide the buffer allocation solution which is a discrete variable. Furthermore, we compare our algorithm with two common existing methods (Genetic Algorithm, Nelder-Mead Algorithm and Adaptive Tabu Search), finding that the Revised RSP-APGHLS outperforms both in terms of effectiveness and efficiency.

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