臺灣博碩士論文加值系統

近年來，電子資訊產品的市場需求快速成長，電腦組裝常常被要求快速反應市場的需要，所以要如何快速、正確的產生產品的裝配規劃，在生產計劃中是很重要的。電腦組裝工廠有前置時間(lead time)極短之特性，使得電腦組裝工廠之指派方式難以規劃。其次，電腦組裝因為其外型之緣故，無法進行自動化或半自動化組裝，故皆為純人力組裝，因此需要考量學習曲線(learning curve)於指派之規劃。當作業員所分派之組裝作業之種類加倍時，因為熟練度不足之緣故，其所耗費之組裝時間超過兩倍組裝單一作業之時間，這便是學習曲線中規模效應所造成之影響。因此，如何在考量人力成本與makespan間選擇適宜之規模大小，便是電腦組裝規劃之問題所在。本研究希望能針對電腦組裝指派發展出一即時模式，同時考量人力成本與makespan之多目標規劃，即時決策訂單分割與否、組裝線數量、組裝步驟與作業員之指派並配合基因演算法求解，以作為實際電腦組裝工廠之參考。

In recent years, needs of electronic products grow rapidly. For the PC assembly, immediate response to the market needs is required. In the production planning, fast and precise product assignment design is, thus, very important. The characteristics of extreme short lead time of computer assembly factories makes the assignment difficult to be planned. In addition, because of the external form of computer, automatic or semi-automatic assembly is not possible. Manual assembly is the only method. Therefore, for the assigned program, learning curve should be taken into consideration. The size effect has influence on learning curve. For example, when the assigned assembly operation of operator varies, it consumes twice the time that a single machine requires. It is due to lacking adaptation. Thus, the main issue of computer assembly program is about choosing an adequate scale between manual cost and makespan.
This research targets on the development of a real-time model of computer assembly assignment. In order to be the reference of actual computer assembly factories, multiple aims arrangement between manual cost and makespan, decision of whether split the order, the quantity of assembly lines, the process of operation, and the assignment of operators are to be considered. At last, genetic algorithms will be applied in order to obtain the result.

第一章 緒論1
1.1 研究背景1
1.2研究動機與目的1
1.3 研究範圍、方法與流程2
1.4論文架構4
第二章 文獻探討5
2.1 生產組織的類型5
2.2 電腦組裝的類型8
2.3 訂單之批量分割14
2.4 指派問題與排程問題15
2.4.1 指派問題15
2.4.2 排程問題16
2.5 多目標規劃17
2.6 基因演算法17
2.6.1 基因演算法之組成要素18
2.5.2 基因演算法之演化流程21
2.5.3 基因演算法的參數設定22
第三章 模式建立26
3.1 問題特性26
3.2 模式建立26
3.2.1 第一部分之模式29
3.2.2 第二部分之模式31
3.2.3 適合度函數33
第四章 案例分析35
4.1 電腦組裝工廠簡介35
4.2 案例描述與假設36
4.3 實驗設計37
4.3.1 產品投料量之選取38
4.3.2 參數設定39
4.4 結果分析41
4.4.1 下限偏離率分析42
4.4.2 改善比例分析50
第五章 結論與建議55
5.1 結論55
5.2 未來研究建議56
參考文獻58
附錄A61
附錄B64

Andreas B., Hollnagel E. and Brendeford T. S.,1999“Human-machine function Allocation: A Functional Modeling Approach,” Rellability Engineering & System Safety 64,291-300Back, T., Fogel D. B., and Michalewicz, Z., 1997, Handbook Evolutionary Computation, A Joint Publication of Institute of Physics Publishing And Oxford University Press.Bazaraa, M., Jarvis., John, J., and Sherali, H. D., 1990, Linear Programming And Network Flows, Second Edition, John Wiley & Sons, New YorkBlazewicz, J., 1996, Scheduling Computer and Manufacturing Process, Springer-Verlag , Berlin,Germany.Chu, P. C. and Beasley, J. E., 1997, “A Genetic Algorithm for the Generalised Assignment Problem”, Computers & Operations Research, Vol. 24, No. 1, pp. 17-23.Costa, L. and Oliveria, P., 2001, “Evolutionary Algorithms Approach to the Solution of Mixed Integer Non-linear Programming Problems”, Computers and Chemical Engineering, Vol. 25, pp. 257-266.Goldratt, E. M., 1980, “Optimized production timetable: a revolution program for industry”, APICS 23th International Conference Proceedings, pp. 172-176.Gursel, A. S. and Bera, I. S., 1998, “Optimal Operator Assignment and Cell Loading when Lot-Splitting is Allowed”, Computer Ind. Engng., Vol. 35 , No. 3-4, pp. 431-434.Hinterding, R., Gielewski, H., and Peachey, T. C., 1995, “The nature of Mutation in Genetic Algorithms,” Proceedings of the Sixth International Conference on Genetic Algorithms, 15-19 July, pp 65-72Kimms, A., 1999, “A genetic algorithm for multi-level, multi-machine lot sizing and scheduling”, Computers & Operations Research , Vol. 26, pp. 829-848.Kropp, D. H. and Smunt, T. L., 1988, “Optimal and heuristic models for lot-splitting in a flow shop”, Decision Sciences, Vol. 21, No. 4, pp. 691-709.Lee, Y. H. and Pinedo, M., 1997, “Scheduling jobs on parallel machines with sequence-dependent setup times”, European Journal of Operational Research, Vol. 100, pp. 464-474.Liaw, C. F., 2000, “A Hybrid Genetic Algorithm for the Open Shop Scheduling Problem”, European Journal of Operational Research, Vol. 124, pp. 28-42.Macaskill, J. C., 1972, “Production-Line Balances for Mixed-Model Lines”, Management Science, Vol. 19, No. 4, pp. 423-434.Mayer, D. G., Belward, J. A., and Burrage, K., 2001, “Robust Parameter Setting of Evolutionary Algorithms for the Optimization of Agricultural Systems Models”, Agricultural Systems 69, pp. 119-213.Mayer, D. G., Belward, J. A., Widell, H., and Burrage, K., 1999, “Survival of the fittest-Genetic Algorithms versus Evolution Strategies in the Optimization of Systems Models”, Agricultural Systems 60, pp. 113-122.Michalewicz, Z., 1996, Genetic Algorithms + Data Structures = Evolution Programs, 3rd Edition ,Springer ,Berlin.Min, L. and Cheng, W., 1999, “A genetic algorithm for minimizing the makespan in the case of scheduling identical machines”, rtificial Intelligence in Engineering, Vol. 13, pp. 399-403.Moily, J. P., 1986, ”Optimal and Heuristic Procedure for Component Lot Splitting in Multi-stage Manufacturing System”, Management Sciences, Vol.32Srinivas, M. and Patnaik, L. M., 1994, “Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms”, IEEE Transactions on System, Man and Cybernetics, Vol. 24, No. 4, April.Vasconcelos, J. A., Ramirez, J. A., Takahashi, R. H. C., and Saldanha, R. R., 2001 , “Improvements in genetic algorithms”, Magnetics, IEEE Transactions on , Vol. 37 , pp. 3414 —3417.Vembu, S. and Srinivasan, G., 1997, “Heuristics for Operator Allocation and Sequencing In Product-line-cells with Manually Operated Machines”, Computer Ind. Engng., Vol. 32 ,No. 2, pp. 265-279.Venkatachalam, A. R., 1995, ”An Analysis of an Embedded Crossover Scheme On A GA-Hard Problem”, Computer Ops Res, Vol. 22, No. 1, pp. 149-157.