臺灣博碩士論文加值系統

傳統裝配線平衡問題為一個多年的老問題，主要是以直線配置的方式在裝配線上設置工作站，來達到裝配線的平衡，但若同時有多條傳統裝配線在裝配產品，容易造成資源上的浪費，因此本研究提出一個新的編碼方式來探討多型態人力之多條平行裝配線平衡問題(Multiple Parallel Assembly Line Balancing Problem, MP-ALBP)，將傳統裝配線平衡問題中各自獨立的裝配線，以平行方式配置並在相鄰裝配線上設置工作站來減少工作站數，進而減少裝配線瓶頸狀況產生。此外，本研究加上多人力型態的概念，依照作業員工作熟練度給予不同的薪資，提供工作站該使用何種人力給企業當作參考依據。
本研究所提出的新的編碼方式，可以將一個不重複的整數隨機序列，轉換成此多型態人力之多條平行裝配線平衡問題的可行解，本研究再應用三種人工智慧演算法，包括基因演算法(Genetic Algorithm, GA)、免疫演算法(Immune Algorithm, IA)與粒子群演算法(Particle Swarm Optimization, PSO)，來求解兩部分研究問題。第一部分研究問題為單型態人力多條平行裝配線平衡問題，主要目的在於驗證多條平行裝配線可以減少工作站數，第二部分研究問題為多型態人力之多條平行裝配線平衡問題，主要目的在於最小化綜合目標，包括工作站數與人力成本。本研究比較三種人工智慧演算法求解多條平行裝配線平衡問題與多型態人力之多條平行裝配線平衡問題之表現，數值結果顯示，基因演算法和免疫演算法在求解問題品質方面不相上下，粒子群演算法在求解品質上為最差，但在求解速度方面優於其他兩個演算法。

The traditional assembly line balancing problem is a typical problem. It is mainly to set up stations on the assembly line in a linear configuration to achieve the balance of the assembly line. However, if there are multiple traditional assembly lines to assemble products at the same time, it is easy to cause waste of resources. Therefore, this research proposes a new encoding method to explore the multi-type manpower multiple parallel assembly line balancing problem. The independent assembly lines in the traditional assembly line balancing problem are configured in parallel and set up stations on adjacent assembly lines to reduce the number of stations and avoid bottlenecks in the assembly line. In addition, with the concept of multiple-type manpower, operators are given different salaries for their job proficiency and provide what kind of manpower the station should use as a reference for the enterprise.
The new encoding method proposed in this thesis is to convert an integer and non-repetitive random sequence into a feasible solution to the multi-type manpower multiple parallel assembly line balancing problem, and then we apply three artificial intelligence algorithms, including: Genetic Algorithm (GA), Immune Algorithm (IA) and Particle Swarm Optimization (PSO) to solve the two-parts of test problems. The first part of the test problem is the single type manpower multiple parallel assembly line balancing problem, the main purpose of the test problem is to show that multiple parallel assembly lines can reduce the number of stations. The second part of test problem is multi-type manpower multiple parallel assembly line balancing problem, the main purpose of the test problem is to find the optimal solution for the combined objective function containing the number stations and manpower costs. This thesis compares the performance of three artificial intelligence algorithms to solving the multiple parallel assembly lines balancing problem and multi-type manpower multiple parallel assembly line balancing problem. The numerical results show that Genetic Algorithm and Immune Algorithm have the same quality of solving the problem. The Particle Swarm Algorithm is the worst one in the quality of the solution, but it is faster than the other two algorithms.

摘要.....................................i
Abstract................................iii
誌謝.....................................v
目錄.....................................vi
表目錄...................................viii
圖目錄...................................ix
第一章 緒論...............................1
1.1研究背景與動機..........................1
1.2研究目的...............................1
1.3研究方法與步驟..........................2
1.4論文架構...............................4
第二章 文獻探討............................5
2.1傳統裝配線平衡問題......................5
2.2平行裝配線平衡問題......................8
2.3多型態人力裝配線平衡問題.................9
2.4多型態人力之多條平行裝配線平衡問題.......11
第三章 研究方法與研究問題..................12
3.1研究方法...............................12
3.1.1基因演算法...........................12
3.1.2免疫演算法...........................15
3.1.3粒子群演算法.........................18
3.1.4田口方法.............................20
3.2研究問題...............................25
3.2.1問題描述.............................25
3.2.3編碼方式.............................26
3.2.4參數設定.............................33
第四章 測試問題與結果討論..................37
4.1環境設備及參數設定......................37
4.2測試問題...............................37
4.3測試結果...............................38
4.3.1第一部分研究問題結果與分析.............39
4.3.2第二部分研究問題結果與分析.............49
4.4統計檢定...............................58
4.4.1第一部分研究問題統計檢定...............58
4.4.2第二部分研究問題統計檢定...............65
第五章 結論...............................72
5.1結論...................................72
5.2未來研究方向............................73
參考文獻..................................74
附錄A.....................................78
附錄B.....................................97
Extended Abstract........................116

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