基因演算法於預鑄工廠排程最佳化之研究 汪書帆、柯千禾
E-mail: [email protected]
摘 要
排程(Scheduling)在預鑄廠中扮演著重要角色，良好的排程會帶給公司資源上最有效的運用，減少不必要的浪費，然而目前 預鑄廠的排程大多仰賴經驗法則，如此作法可能導致資源無效的運用與錯失交期。電腦化排程技術可提供比人工排程更精 確之排程計畫，本研究提出一個符合預鑄廠生產情況的流程型生產排程模型，考慮生產暫存區容量，並以多目標基因演算 法對此模型進行搜尋，搜尋目標分別為總完工時間最小化與延遲懲罰值最小化，最後，以範例來測試基因演算法的效率與 績效，測試結果顯示基因演算法能夠有效地對此一模型進行求解，此外，本研究將生產暫存區容量納入排程考量，可獲得 較合理且可行之排程計畫。
關鍵詞 : 預鑄 ; 排程 ; 流程型生產排程 ; 基因演算法 ; 暫存區 目錄
目錄 封面內頁 簽名頁 授權書 iii 中文摘要 iv ABSTRACT v 誌謝 vi 目錄 vii 圖目錄 ix 表目錄 x 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 研究範圍與限制 3 1.4 論文架構 4 第二章 文獻探討 6 2.1 預鑄廠現況說明 6 2.2 預鑄廠生產排程文 獻探討 10 2.3 多目標基因演算法文獻探討 11 2.3.1 多目標規劃 11 2.3.2 基因演算法 13 2.3.3 多目標基因演算法 16 2.4 小結 17 第三章 預鑄廠生產模型 19 3.1生產模型符號說明 19 3.2 預鑄廠生產模型特性 20 3.3 排程衡量準則 26 第四章 多目標基因 演算法 29 4.1演算機制說明 29 4.2多目標最佳化方式選定 30 4.3 多目標基因演算法流程 31 4.4 多目標基因演算法範例說明 38 第五章 實驗結果與分析 41 5.1 實驗相關資訊 41 5.2求解績效衡量 42 5.3演算參數說明 44 5.4 實驗範例比較分析 46 5.4.1 單目標實驗範例分析 46 5.4.2多目標實驗範例分析 49 5.4.3排程最佳化實驗 55 5.5 系統說明 57 第六章 結論與建議 61 6.1 結 論 61 6.2 未來研究方向 62 參考文獻 63
參考文獻
參考文獻 [1] Dawood, N. N. (1993). “Knowledge elicitation and dynamic scheduling using a simulation model. An application to the precast manufacturing process.” Proceedings of the Civil-Comp93, Part 4: Knowledge Based Systems for Civil and Structural Engineering, 73 [2] Chan, W. T. and Hu, H. (2002a). “Constraint programming approach to precast production scheduling.”Journal of Construction Engineering and Management, ASCE 128(6),513-521.
[3] Chan, W. T. and Hu, H. (2002b). “Production scheduling for precast plants using a flow shop sequencing model.” Journal of Computing in Civil Engineering, ASCE 79(17), 1605-1616.
[4] Leu, S. S. and Hwang, S. T. (2001). “Optimal repetitive scheduling model with shareableresource constraint.”Journal of Construction Engineering and Management, ASCE 127(4),270-280.
[5] Palmer, D.S. (1965) Sequencing jobs through a multi-stage process in the minimum total time – A quick method of obtaining a near optimum.
Operations Research Quarterly,16(1), 101–7.
[6] Gupta, J.N.D. (1971) A functional heuristic algorithm forthe flowshop scheduling problem. Operational Research Quarterly, 22(1), 39–47.
[7] Campbell, H.G., Dudek, R.A. and Smith, M.L. (1970) A heuristic algorithm for the n job, m machine sequencing problem. Management Science, 16(10), 630–7.
[8] Benjaoran, V., Dawood, N., and Hobbs, B. (2005). “Flowshop scheduling model for bespokeprecast concrete production planning.”Journal of Construction Management and Economics, 23(1), 93-105.
[9] Cohon, J. L. (1978), Multiobjective Programming and Planning, New York: Academic Press.
[10] Goldberg, D.E. (1989), “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison-Wesley, Reading, Mass,.
[11] Murata, T. and H. Ishibuchi(1995), “MOGA: Multi-Objective Genetic Algorithms,” Proceedings of 2nd IEEE International Conference on Evolutionary Computation, pp.284-294 [12] Murata, T. and H. Ishibuchi(1996), “Positive and Negative Combination Effects of Crossover and Mutation Operators in Sequencing Problems,” Proceedings of IEEE International Conference on Evolutionary Computation, pp.170-175.
[13] Ishibuchi, H. and H. Murata,(1998) “Multi-Objective Genetic Local Search Algorithm and Its Applications to Flowshop Scheduling,” IEEE Transactions on SMC, 28, pp.392-403,.
[14] Gao, Y., L. Shi, and P. Yao (2000), “Study on Multi-Objective Genetic Algorithm”, Proceedings of 3rd World Congress on Intelligent
Control and Automation, pp. 646-650 [15] Sule, D. R.(1996), Industrial Scheduling, PWS. Publishing Company, 63-64.
[16] Johnson, S. M.(1954), “Optimal two- and three-stage production schedules with set-up times included”, Naval Research Logistics Quarterly, Vol.1, 61-68, [17] Coffman, E.G.,JR., (1976), “Computer and Job-shop Scheduling Theory”, Wiley, New York.
[18] Cochran, J.K., Horng, S.M. and Fowler, J.W. (2003) “A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines. ” Computers and Operations Research, 30(7), 1087–102 [19] Grefenstette, J. J.（1986） “Optimization of Control Parameters for Genetic Algorithms,” IEEE Transactions on Systems, man & cybernetics, pp.122-128 [20] Chang, P. C., J. C. Hsieh, and S. G.
Lin（2002）“The Development of Gradual Priority Weighting Approach for the Multi-Objective Flowshop Scheduling Problem,” International Journal of Production Economics, 79(3), pp.171-181.
[21] Schaffer, J. D.; A. Richard, L. Caruana; J. Eshelman, and D. Rajarshi （1989） “A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. ”, The 3rd International Conference on Genetic Algorithms and Their Applications, pp51-60.
[22] Kaige S., T. Murata, H.Ishibuchi,（2003）”Performance evaluation of memetic EMO algorithms using dominance relation-based replacement rules on MOO test problem,” IEEE international conference on system,man and cybernetics,Vol. 1, pp14-19 [23] 黃少廷，預鑄工 廠生產排程最適化模式之探討，國立台灣科 技大學營建工程所碩士論文，民國88年。
[24] 林我聰，現場排程專家系統-應用個體導向技術建立之研究， 資訊與電腦公司出版，民國83年。
[25] 王治元，智慧型基因演算法於多目標排程之發展與應用 – 以 PCB鑽孔作業為例，元智大學工業工程所碩士論文，民國 93年。
