臺灣博碩士論文加值系統

目前許多排程相關之學術研究多以單一決策目標為發展方向，然而，在真實的生產製造環境中，管理者於決策時往往需同時考量多項衡量指標。此外，多目標基因演算法(MOGA)經學者提出後已成功應用於排程問題，但其基因運算元演化過程及求解效率仍有很大改善空間，有鑑於此，本研究共提出三種不同之智慧型基因演算法(區間變動基因演算法、自我調適型基因演算法及兩階式基因演算法)求解完全相同平行機台之多目標排程問題，以總完工時間、總延遲時間最小化為排程目標，利用智慧型基因演算法獨特之搜尋特性求出多目標最佳化問題之柏拉圖最佳解。最後，以印刷電路板之鑽孔作業為案例，並以MOGA為比較標竿進行驗證，經實驗證明，二階式多目標基因演算法之求解效率最佳，且本研究所提出之三項智慧型基因演算法其求解效果及效率均優於MOGA，尤其在求解中大型問題時更能展現其求解能力之優越性。

The main task of scheduling problems in the past research is to solve the single objective problem. Nevertheless, there are many different targets in the real-world scheduling problem. Therefore, the main propose of this research is to develop intelligent multi-objective genetic algorithm that can find better Pareto optimal solutions among these conflicting objectives and help the manager to make a suitable scheduling decision in the manufacturing process. In this research, three novel multi-objective genetic algorithms including multi-objective genetic algorithm with variable rates (VMOGA), self-adaptive multi-objective genetic algorithm (SAMOGA) and two-phase multi-objective genetic algorithm (TPMOGA) are proposed to deal with such a complicated real-world case. Real-world instances are applied as well to evaluate the effectiveness and efficiency of VMOGA, SAMOGA and TPMOGA. The result indicates that TPMOGA is more effective than MOGA, VMOGA and SAMOGA in solution quality. Each intelligent multi-objective genetic algorithm are more efficiency than MOGA, especially apply to complicated multi-objective scheduling problems.

目 錄
中文摘要 i
英文摘要 ii
目錄 iii
表目錄 iv
圖目錄 v
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 3
1.4 研究方法 3
1.5 研究架構與流程 4
第二章 文獻探討 7
2.1 排程問題 7
2.1.1 排程類型 7
2.1.2 排程之績效目標 8
2.2 多目標排程 10
2.2.1 多目標最佳化問題 10
2.2.2 多目標排程之相關研究 13
2.3 基因演算法 17
2.3.1 基因演算法概述 17
2.3.2 基因演算法之演變 19
2.3.3 基因演算法於多目標排程之演變 20
2.3.4 基因演算法於多目標排程之應用 23
2.4 小結 26
第三章 問題定義與分析 28
3.1 問題定義 28
3.2 模式建構 30
3.2.1 模式之限制與假設 30
3.2.2 符號說明 31
3.2.3 排程績效衡量準則 31
3.3 應用範例現況說明 32
第四章 研究方法 34
4.1 多目標基因演算法 34
4.1.1 符號說明 34
4.1.2 多目標基因演算法演化流程 35
4.1.3 基因參數設定 41
4.2 區間變動多目標基因演算法 43
4.2.1 區間變動多目標基因演算法設計原理說明 43
4.2.2 區間變動多目標基因演算法演化流程 43
4.3 自我調適型多目標基因演算法 45
4.3.1 自我調適型多目標基因演算法設計原理說明 45
4.3.2 自我調適型多目標基因演算法演化流程 46
4.4 兩階式多目標基因演算法 49
4.4.1 兩階式多目標基因演算法設計原理說明 49
4.4.2 兩階式多目標基因演算法演化流程 49
第五章 實驗設計 53
5.1 多目標基因演算法之參數設計 53
5.2 區間變動多目標基因之參數設計 58
5.3 自我調適型多目標基因演算法之參數設計 60
5.4 兩階式多目標基因演算法之參數設計 63
第六章 實驗結果與分析 67
6.1 各式多目標基因演算法求解結果 67
6.2 綜合比較與分析 69
6.3 結語 74
第七章 結論與建議 77
7.1 結果與討論 77
7.2 建議 78
7.3 未來研究方向 79
參考文獻 80
表 目 錄
表2.1 排程績效目標 9
表2.2 排程績效目標分類 10
表2.3 多目標排程之相關研究(不含基因演算法) 16
表2.4 基因演算法的優缺點 18
表2.5 應用基因演算法於多目標排程之相關研究 25
表4.1 輪盤法圖例之一 38
表4.2 輪盤法圖例之二 39
表4.3 輪盤法圖例之三 39
表5.1 MOGA測試範例之參數設定 58
表5.2 VMOGA測試範例之參數設定 59
表5.3 SAMOGA測試範例之參數設定 66
表5.4 TPMOGA測試範例之參數設定 66
表6.1 現場排程系統與MOGA運算結果比較 70
表6.2 MOGA與VMOGA運算結果比較 70
表6.3 SAMOGA與TPMOGA運算結果比較 71
表6.4 VMOGA與TPMOGA運算結果比較 72
表6.5 MOGA與SAMOGA運算結果比較 72
表6.6 MOGA與TPMOGA運算結果比較 73
表6.7 基因演算法於雙目標問題之平均模擬時間比較 73
圖 目 錄
圖1.1 研究流程圖 5
圖2.1 固定權重之搜尋方向 11
圖2.2 變動權重搜尋方向 12
圖2.3 基因演算法之演變 19
圖2.4 基因演算法求解多目標排程之演變 20
圖2.5 VEGA搜尋方向 21
圖3.1 平行機台製造系統示意圖 28
圖3.2 印刷電路板製程 29
圖3.3 鑽孔機台 30
圖4.1 多目標基因演算法演算流程 35
圖4.2 輪盤法示意圖 39
圖4.3 順序編碼之雙點交配方式 40
圖4.4 順序編碼之移動式突變方式 41
圖4.5 區間變動多目標基因演算法之演化流程 44
圖4.6 自我調適型基因演算法之調整策略 46
圖4.7 自我調適型多目標基因演算法演化流程 47
圖4.8 兩階式多目標基因演算法交配、突變率之調整方略 50
圖4.9 兩階式多目標基因演算法之演算流程 51
圖5.1 多目標基因演算法於(n=50,m=20)之收斂圖 55
圖5.2 多目標基因演算法於(n=30,m=10)之收斂圖 64
圖5.3 多目標基因演算法於(n=40,m=15)之收斂圖 64
圖5.4 多目標基因演算法於(n=65,m=25)之收斂圖 65
圖5.5 多目標基因演算法於(n=80,m=28)之收斂圖 65
圖6.1 區間變動多目標基因演算法收斂圖 67
圖6.2 自我調適型多目標基因演算法收斂圖 68
圖6.3 兩階式多目標基因演算法收斂圖 68
圖6.4 比較不同演算法之柏拉圖最佳解 69
圖6.5 各式演算法於不同範圍題目之運算時間比較 74
圖6.6 柏拉圖非凌越解個數較MOGA改良比率 74
圖6.7 解之品質較MOGA改良比率 75
圖6.8 各演算法比較結果 75

參考文獻
1. Allahverdi, A. and J. Mittenthal, “Scheduling on M Parallel Machines Subject to Random Breakdowns to Minimize Expected Mean Flow Time,” Naval Research Logistics, 41, pp.677-682, 1994.
2. Bagchi, T. P., Multiobjective Scheduling by Genetic Algorithm, Kluwer Academic Publishers, 1999.
3. Baker, R. K., “Sequence Rules and Due-Date Assignments in A Job Shop,” Management Science, 30(9), 1984.
4. Balakrishnan, N., J. J. Kanet, and S. V. Sridharan, “Early /Tardy Scheduling with Sequence Dependent Setup on Uniform Parallel Machines,” Computers & Operations Research, 26(2), pp127-141, 1999.
5. Basseur, M., F. Seynhaeve, and E. Talbi, “Design of Multi-Objective Evolutionary Algorithms: Application to The Flow-Shop Scheduling Problem,” Proceedings of the 2002 Congress on Evolutionary Computation, 2, pp.1151-1156, 2002.
6. Celano, G., S. Fichera, V. Grasso, U. L. Commare, and G. Perrone, “An Evolutionary Approach to Multi-Objective Scheduling of Mixed Model Assembly Lines,” Computers and Industrial Engineering, 37(1-2), pp.69-73, 1999.
7. Chang, P. C., C. H. Hsiao, and Y. W. Wang, “Genetic Algorithms Applied in BOPP Film Scheduling Problems: minimizing total absolute deviation and setup times,” Applied Soft Computing, 3, pp. 139-148, 2003.
8. Chang, P. C., J. C. Hsieh, and C. H. Hsiao, “Application of Genetic Algorithm to the Unrelated Parallel Machine Problem Scheduling,” Journal of the Chinese Institute of Industrial Engineering, 19(2), pp.79-95, 2002.
9. Chang, P. C., J. C. Hsieh, and S. G. Lin, “The Development of Gradual Priority Weighting Approach for the Multi-Objective Flowshop Scheduling Problem,” International Journal of Production Economics, 79(3), pp.171-181, 2002.
10. Chang, P. C. and T. W. Liao, ”A Fuzzy Rule Based Knowledge Representation System for Due-Date Assignment Approach in Semi-conductor Manufacturing Factory,” Revision in IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews.
11. Cheng, R. and M. Gen, “Parallel Machine Scheduling Problems Using Memetic Algorithms,” Computers & Industrial Engineering, 33(3-4), pp.761-764, 1997.
12. Cochran, J. K., S. M. Horng, and J. W. Fowler, “A Multi-Population Genetic Algorithm to Solve Multi-Objective Scheduling Problems for Parallel Machines,” Computers & Operations. Research, 30, pp.1087-1102, 2003.
13. Cowling, P., “A Flexible Decision Support System for Steel Hot Rolling Mill Scheduling,” Computers & Industrial Engineering, 45(2), pp.307-321, 2003.
14. Deb, K, Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, New York, 2001.
15. Feldmann, M., and D. Biskup, “Single-Machine Scheduling for Minimizing Earliness and Tardiness Penalties by Meta-Heuristic Approaches,” Computers & Industrial Engineering, 44, pp.307-323, 2003.
16. Figielska, E., “Preemptive Scheduling with Changeovers: Using Column Generation Technique and Genetic Algorithm,” Computers & Industrial Engineering, 37(1-2), pp.81-84, 1999.
17. Fowler, J. W., S. M. Horng, and J. K Cochran, “A Hybridized Genetic Algorithm to Solve Parallel Machine Scheduling Problems with Sequence Dependent Setups,” International Journal of Industrial Engineering, 10(3), pp.232-243, 2003.
18. French, S., Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop, John Wiley & Sons, New York, 1982.
19. Funda, S. S. and G. Ulusoy, “Parallel Machine Scheduling with Earliness and Tardiness Penalties,” Computers & Operations. Research, 26(8), pp.773-787, 1999.
20. Gangadharan, R. and C. Rajendran, “A Simulated Annealing Heuristic for Scheduling in a Flowshop with Bicriteria,” Computers & Industrial Engineering, 27, pp.473-476, 1994.
21. Gao, Y., L. Shi, and P. Yao, “Study on Multi-Objective Genetic Algorithm”, Proceedings of 3rd World Congress on Intelligent Control and Automation, pp. 646-650, 2000.
22. Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, MA, 1989.
23. Gravel, M., W. L. Price, and C. Gagne, “Scheduling Continuous Casting of Aluminum Using A Multiple Objective Ant Colony Optimization Metaheuristic,” European Journal of Operational Research, 143, pp.218-229, 2002.
24. Grefenstette, J. J., “Optimization of Control Parameters for Genetic Algorithms,” IEEE Transactions on Systems, man & cybernetics, pp.122-128, 1986.
25. Gupta, J.N.D., V.R. Neppalli, and F. Werner, “Minimizing Total Flow time in A Two-Machine Flowshop Problem with Minimum Makespan,” International Journal of Production Economics, 69(3), pp.323-338, 2001.
26. Gupta, J. N. D., and J. C. Ho, “Minimizing Makespan Subject to minimum Flowtime on Two Identical Parallel Machines,” Computers & Operations. Research, 28, pp.705-717, 2001.
27. Han, S., H. Ishii, and S. Fujii, “One Machine Scheduling Problem with Fuzzy Due Date,” European Journal of Operational Research, 79, pp.1-12, 1994.
28. Hapke, M., A. Jaszkiewicz, and R. Slowinski, “Pareto Simulated Annealing for Fuzzy Multi-Objective Combinatorial Optimization,” Journal of Heuristics, 6, pp.329-345, 2000.
29. Ho, J. C. and Y. L. Chang, “A New Heuristic for The n-Job, M-Machine Flow-shop Problem,” European Journal of Operational Research, 52, pp.194-202, 1991.
30. Hsieh, J. C., P. C. Chang, and L. C. Hsu, “Scheduling of Drilling Operations in Printed-Circuit-Board Factory,” Computers and Industrial Engineering, 44(3), pp.461-473, 2003.
31. Ishibuchi, H. and H. Murata, “Local Search Procedures in a Multi-Objective Genetic Local Search Algorithm for Scheduling Problems,” Proceedings of IEEE International Conference on SMC, pp.119-124, 1996.
32. Ishibuchi, H. and H. Murata, “Multi-Objective Genetic Local Search Algorithm,” Proceedings of IEEE International Conference on Evolutionary Computation, pp.119-124, 1996.
33. Ishibuchi, H. and H. Murata, “Multi-Objective Genetic Local Search Algorithm and Its Applications to Flowshop Scheduling,” IEEE Transactions on SMC, 28, pp.392-403, 1998.
34. Ishibuchi, H., N. Yamamoto, S. Misaki, and H. Tanaka, “Local Search Algorithms for Flow Shop Scheduling with Fuzzy Due Dates,” International Journal of Production Economics, 33, pp. 53-66, 1994.
35. Ishibuchi, H., T. Murata, and K. H. Lee, “Formulation of Fuzzy Flowshop Scheduling Problems with Fuzzy Processing Time,” Proceedings of The 5th IEEE International Conference on Fuzzy Systems, New Orleans, USA, pp.199-205, 1996.
36. Ishibuchi, H., T. Murata, and K. H. Lee, “Relations Between Conventional Scheduling Problems and Fuzzy Scheduling Problems,” Proceeding of the 35th Conference on Decision and Control, pp.106-107, 1996.
37. Ishibuchi, H., T. Yoshida, and T. Murata, “Balance Between Genetic Search and Local Search in Memetic Algorithms for Multiobjective Permutation Flowshop Scheduling,” IEEE Transactions on Evolutionary Computation, 7(2), pp.204-223, 2003.
38. Ishii, H. and M. Tada, “Single Machine with Fuzzy Precedence Relation,” European Journal of Operational Research, 87, pp.284-288, 1995.
39. Itoh, T. and Ishii, H., “Fuzzy Due-Date Scheduling Problem with Fuzzy Processing Time”, International Transactions in Operational Research, 6, pp.639-647, 1999.
40. Jawahar, N., P. Aravindan, S. G. Ponnambalam, A. A. Karthikeyan, “A Genetic Algorithm-Based Scheduler for Setup-Constrained FMC,” Computers in Industry, 35(3), pp.291-310, 1998.
41. Kacem, I., S. Hammadi, and P. Borne, “Approach by Localization and Multiobjective Evolutionary Optimization for Flexible Job-Shop Scheduling Problems,” IEEE Transactions on Systems, Man and Cybernetics, 32(1), pp.1-13, 2002.
42. Kacem, I., S. Hammadi, and P. Borne, “Pareto-Optimality Approach for Flexible Job-Shop Scheduling Problems: Hybridization of Evolutionary Algorithms and Fuzzy Logic,” Mathematics and Computers in Simulation, 60(3-5), pp.245-276, 2002
43. Kacem, I., S. Hammadi, P. Borne, “Pareto-Optimality Approach Based on Uniform Design and Fuzzy Evolutionary Algorithms for Flexible Job-Shop Scheduling Problems (FJSPs),” 2002 IEEE International Conference on Systems Man and Cybernetics, 7,pp.1-7, 2002.
44. Ken, D.W, K. H. Kim, W. Jang, and F. F. Chen, “Unrelated Parallel Machine Scheduling with Setup Times Using Simulated Annealing,” Robotics and Computer Integrated Manufacturing, 18, pp.223-231, 2002.
45. Kim, J. O, and P. K. Khosla, “A Multi-Population Genetic Algorithm and Its Application to Design of Manipulators,” Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.279-286, 1992.
46. Kuroda, M. and Z. Wang., “Fuzzy Job Shop Scheduling,” International Journal of Production Economics, 44, p.45-51, 1996.
47. Lee, C. C., “Fuzzy Logic in Control Systems: Fuzzy Logic Controller — Part I,” IEEE Transactions on Systems, Man and Cybernetics, 20(2), pp.404-418, 1990.
48. Lee, H. Y., B. K. Lee, and B. Jeong, “Multi-Objective Production Scheduling of Probe Process in Semiconductor Manufacturing,” Production Planning & Control, 11(7), pp.660-669, 2000.
49. Liao, L. M., and C. J. Liao, “Single Machine Scheduling Problem with Fuzzy Due Date and Processing Time,” Journal of the Chinese Institute of Industrial Engineers,” 21, pp.189-196, 1998.
50. Low, C., and T. H. Wu, “Mathematical Modeling and Heuristic Approaches to Operating Scheduling Problems in an FMS Environment,” International Journal of Production Research, 39(4), pp.689-708, 2001.
51. Mellor, P., “A Review of Job Shop Scheduling,” Operational Research Quarterly, 17, pp.294-301, 1992.
52. Muthusamy, K., S. C. Sung., M. Vlach, and H. Ishii, “Scheduling with Fuzzy Delays and Fuzzy Precedence,” Fuzzy Sets and Systems, 134, pp.387-395, 2003.
53. Murata, T. and H. Ishibuchi, “Performance Evolution of Genetic Algorithms for Flowshop Scheduling Problems,” Proceedings of 1st IEEE International Conference on Evolutionary Computation,” pp.812-817, 1994.
54. Murata, T. and H. Ishibuchi, “MOGA: Multi-Objective Genetic Algorithms,” Proceedings of 2nd IEEE International Conference on Evolutionary Computation, pp.284-294, 1995.
55. Murata, T. and H. Ishibuchi, “Positive and Negative Combination Effects of Crossover and Mutation Operators in Sequencing Problems,” Proceedings of IEEE International Conference on Evolutionary Computation, pp.170-175, 1996.
56. Murata, T., H. Ishibuchi and H. Tanaka, ”Genetic Algorithm for Flowshop Scheduling Problem,” International Journal of Computers and Industrial Engineering, 30, pp.1061-1071, 1996.
57. Murata, T., H. Ishibuchi, and H. Tanaka, “Multi-Objective Genetic Algorithm and Its Applications to Flowshop Scheduling,” International Journal of Computers and Industrial Engineering, 30, pp.957-968, 1996.
58. Murata, T., H. Ishibuchi, and K. H. Lee, “Reformulation of Various Non-Fuzzy Scheduling Problems Using The Concept of Fuzzy Due-Date,” Proceedings of The 6th IEEE International Conference on Fuzzy Systems, Barcelona, Spain, pp. 447-452, 1997.
59. Murata, T., H. Ishibuchi, and M. Gen, “Neighborhood Structures for Genetic Local Search Algorithms,” Proceedings of 2nd IEEE International Conference on Knowledge-based Intelligent Electronic Systems, pp.259-263, 1998.
60. Murata, T., H. Ishibuchi, and M. Gen, “Multi-Objective Fuzzy Scheduling with The OWA Operator for Handling Different Scheduling Criteria and Different Job Importance,” 1999 IEEE International Fuzzy System Conference Proceedings, pp.773-778, 1999.
61. Nagar, A., S. S. Heragu, and J. Haddock, “A Branch and Bound Approach for a Two-Machine Flowshop Scheduling Problem,” Journal of the Operational Research Society, 46, pp.721-734, 1995.
62. Nagar, A., S. S. Heragu, and J. Haddock, “A Combined Branch-and-Bound and Genetic Algorithm Based Approach for a Flowshop Scheduling Problem,” Annals of Operations Research, 63, pp.397-414, 1996.
63. Nozaki, K., H. Ishibuchi, and H. Tanaka, “A Simple but Power Heuristic Method for Generating Fuzzy Rules from Numerical Data,” Fuzzy Sets and System, 86, pp.251-270, 1997.
64. Ponnambalam, S. G., V. Ramkumar, and N. Jawahar, “A Multiobjective Genetic Algorithm for Job Shop Scheduling,” Production Planning & Control, 12(8), pp.764-774, 2001.
65. Rajendran, C. and D. Chaudhuri, “An Efficient Heuristic Approach to The Scheduling of Jobs in a Flowshop,” European Journal of Operational Research, 61, pp.318-325, 1992.
66. Rajendran, C., “Heuristics for Scheduling in Flowshop with Multiple Objectives,” European Journal of Operational Research, 82, pp.540-555, 1995.
67. Sakawa, M., and R. Kibota, “Fuzzy Programming for Multiobjective Job Shop Scheduling with Fuzzy Processing Time and Fuzzy Duedate throught Genetic Algorithms,” European Journal of Operational Research, 120, pp.393-407, 2000.
68. Sankar, S. S., S. G. Ponnanbalam, and C. Rajendran, “A Multiobjective Genetic Algorithm for Scheduling A Flexible Manufacturing System,” International Journal of Advanced Manufacturing Technology, 22(3-4), pp.229-236, 2003.
69. Sasaki, M. and M. Gen, “Fuzzy Multiple Object Optimal System Design by Hybrid Genetic Algorithm,” Applied Soft Computing, 2, pp.189-196, 2003.
70. Schaffer, J. D., “Multiple Objective Optimization with Vector Evaluated Genetic Algorithms,” Proceedings of 1st International Conference on Genetic Algorithms, pp.93-100, 1985.
71. Schaffer, J. D.; A. Richard, L. Caruana; J. Eshelman, and D. Rajarshi, “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, 1989.
72. Serifoglu, F. S. and Ulusoy, G., “Parallel machine scheduling with earliness and tardiness penalties,” Computers and Operations Research, 26, pp.773-787, 1999.
73. Sivakumar, A. I. “Multiobjective Dynamic Scheduling Using Discrete Event Simulation,” International Journal of Computer Integrated Manufacturing, 14(2), pp.154-167, 2001.
74. Srinivas, N. and K. Deb, “Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms”, Evolutionary Computation, 2(3), pp.221-248, 1994.
75. Sun, H., and G. Wang, “Parallel Machine Earliness and Tardiness Scheduling with Proportional Weights,” Computers & Operations. Research, 30(5), pp.801-808, 2003.
76. Tamaki, H., E. Nishino, and S. Abe, “A Genetic Algorithm Approach to Multi-Objective Scheduling Problems with Earliness and Tardiness Penalties,” Proceedings of the 1999 Congress on Evolutionary Computation, 1, pp.46-52, 1999.
77. T’kindt, V., N. Monmarche, F. Tercinet, and D. Laugt, “An Ant Colony Optimization Algorithm to Solve A 2-Machine Bicriteria Flowshop Scheduling Problem,” European Journal of Operational Research, 142, pp.250-257, 2002.
78. Turkcan, A., and M.S. Akturk, “A Problem Space Genetic Algorithm in Multiobjective Optimization,” Journal of Intelligent Manufacturing, 14, pp.363-378, 2003.
79. Ulungu, E. L., J. Teghem, P. Fortemps, and D. Tugtens, “MOSA Method: A Tool for Solving Multiobjective Combinatorial Optimization Problems,” Journal of Multi-Criteria Decision Analysis, 8, pp.221-236, 1999.
80. Yi, Y and D. W. Wang, “Soft Computing for Scheduling with Batch Setup Times and Earliness-Tardiness Penalties on Parallel Machine,” Journal of Intelligent Manufacturing, 14, pp.311-322, 2003.
81. 王培珍，「應用遺傳演算法與模擬在動態排程問題之探討」，中原大學，碩士論文，1995。
82. 汪玉柏，「運用基因演算法求解流程型工廠之多目標排程」，國立台灣科技大學，碩士論文，1999。
83. 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，1994。
84. 林淳菁，「應用遺傳基因演算法求解不相關平行機台之排程問題」，朝陽科技大學，碩士論文，2001。
85. 許民聖，「運用模擬退火法求解流程型工廠之多目標排程」，國立台灣科技大學，碩士論文，2000。
86. 許志義，多目標決策，五南圖書出版公司，1995。
87. 駱景堯、吳泰熙、張俊仁，「結合模糊論與遺傳演算法於多目標彈性製造系統」，工業工程學刊，第16卷，第三期，1999。
88. 廖麗滿，「塔布搜尋法求解排程問題之研究」，國立台灣科技大學，博士論文，2001。