臺灣博碩士論文加值系統

近年來顧客需求型態改變已邁向需求變化大且產品多樣化之勢，企業在面對此需求不確定之生產環境下，端賴預測將產品且快速的推向市場之推式系統已無法滿足市場需求，相對的以市場需求牽引產品生產的拉式系統則逐漸受到重視，引導著企業走向顧客導向的經營模式。然而當產品具多品質特性時，工作站及檢驗系統之規劃與配置，均會影響整個系統之整體績效，因為企業所擁有的資源有限，將會面臨到製程應該選擇哪些類別工作站或配置哪一類別檢驗站以獲得具經濟效益的生產系統之難題，由此可知企業製造資源規劃與配置的重要性。
本研究以不完美拉式生產系統為基，在交貨量滿足顧客需求前提下，進行生產系統分析，當中考量了整體生產流程各相關資源之能力(製造能力/檢驗能力)及顧客需求(公差規格)間之關係，進而透過分析產品生產與作業流程、零組件採購與加工、資源規劃、以及相關成本等關鍵因素，以求得企業內、外部各項成本模式，並推導出此生產系統的期望總生產成本模式。藉此，一方面利用窮舉法找出此模式之最佳解當作依據，另一方面利用實驗設計從中求得可能影響成本模式的關鍵決策因子，並透過對關鍵決策因子測試及驗證，進而發展適用的基因演算法。
最後本研究所發展的基因演算法與窮舉法相比之結果，其兩者最佳配置方案之平均總生產成本相似度高達99.26%，而且基因所耗費的平均總執行時間也遠遠少於窮舉法的時間，平均節省98.01%以上的表現，可以得知透過本研究之基因演算法能有效率的獲得在拉式生產系統下最小期望總生產成本下之最佳製造資源規劃與配置，亦能夠提供決策者一個合理且有效率的評估依據。

In recent years, customer demand has been changed from demand stable to instable, and product categories more numerous. So that guide enterprises toward customer-oriented business model. Enterprises in the face of the demand uncertainty of the production environment in order to create competitive advantage using the push-type production system has been unable to meet market demand. Instead, demand-driven for pull production systems become an important, then production process has been changed from a single to multi-stage processing of the current production patterns and production system is composed by numerous of process, that is Products with multiple quality characteristics. However, enterprises have limited resources cause company will face to processes should choose which classes workstations and inspection station configuration for cost-effective production system problems. It can be seen enterprise manufacturing resource planning and allocation is very important.
This paper assumes the production system is imperfect. By analyzing the resources allocation, purchase parts and other considerations and resource constraints derived the cost of internal models for the study included the reasonable expectations of the total production cost model. And find the optimal exhaustive method to compare based on test and verify the developed genetic algorithm. To find more efficient access to the minimum expected total cost of production under the best manufacturing resource planning and allocation, in order to help decision-makers can effectively and clearly the assessment of a reasonable basis.
Finally, this study developed a genetic algorithm compare the results of exhaustive method, both algorithms of the best configuration the average total production cost of up to 99.26% of the performance of similarity, and heuristic save 98.01% average execution time. In this study demonstrate the development of genetic algorithm can be more efficient access to the minimum expected total cost of production under the best manufacturing resource planning and allocation, in order to help decision-makers can effectively and clearly the assessment of a reasonable basis.

摘要 I
ABSTRACT II
致謝 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 研究範圍 2
1.4 研究流程與架構 3
第二章 文獻探討 5
2.1 推式與拉式生產 5
2.2 不完美生產系統 8
2.3 品質成本 10
2.4 基因演算法 11
第三章 研究方法 13
3.1 系統描述: 14
3.2 模式分析與構建 17
3.2.1研究假設 17
3.2.2符號定義 18
3.2.3製造能力分析 20
3.2.4作業流程分析 22
3.2.5成本分析 31
3.2.6模式建立 35
第四章 演算法之構建與案例驗證 36
4.1模擬工具 36
4.2窮舉法之分析與構建 36
4.3.決策因子分析 40
4.4基因演算法之分析與構建 42
4.4.1 EVOLVER 42
4.4.2 基因演算法參數設定分析 43
4.4.3 EVOLVER 基因演算法之設定程序 44
4.4.3.1 設定編碼 45
4.4.3.2 初始族群的設定 45
4.4.3.3 族群數、交配率、突變率和終止條件 45
4.4.3.4 適應值函數 46
4.5案例分析與驗證 48
4.5.1參數設定 48
4.5.2案例描述 49
4.5.3測試案例結果 49
4.5.4測試案例結果分析 50
第五章、結論與未來發展 53
5.1結論 53
5.2未來發展 54
參考文獻 55
附錄一、10組測試案例設定 59
附錄二、50組測試案例結果 67

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