跳到主要內容

簡易檢索 / 詳目顯示

回結果列表
研究生： 陳貝生
Pei-Sheng Chen
論文名稱： 應用基因演算法於多部門多重專案選擇與排程問題
A GA-based Approach for Solving Project Selection and Scheduling Problems in a Multiple-Department Environment
指導教授： 何應欽
Ying-Chin Ho
曾清枝
Ching-Chih Tseng
口試委員:
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理研究所
Graduate Institute of Industrial Management
畢業學年度： 93
語文別： 英文
論文頁數： 101
中文關鍵詞： 基因演算法 、田口方法 、專案組合 、專案選擇與排程
外文關鍵詞： Project portfolio, Project selection and scheduling, Genetic algorithm, Taguchi method
相關次數： 點閱：12  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 中文摘要：
    專案選擇與排程問題至目前已被深入的探討，但探討的觀點卻只著重在單一部門的環境，至於多部門的環境至目前並沒有被深入的著墨。有鑑於此，此篇論文考慮在多部門環境中的專案選擇與排程問題，並以公司整體利益最大化為主要的目標函數，發展此問題的數學模型。
    而專案選擇與排程問題本質上屬於一種組合問題，以解析式的方法來解決組合問題當問題的複雜度增加時，求解的時間必定以指數型的方式漸增。因此，我們試著提出可以解決組合問題的啟發式方法—基因演算法，來解決多部門環境中的專案的選擇與排程問題。
    在模擬實驗的過程中，田口方法會被使用於所發展的基因演算法中主要參數的決定，而所模擬出來的結果，會在目標函數值與執行時間此兩個比較維度下與用解析式方法(AMPL modeling language)所得到的結果做比較。

    Abstract:
    Before implementing projects, project selection is a very important proceeding work. An organization can’t possibly implement all coming projects because of some limitations such as budgets and human resources. Every project selection result sifting from candidate projects in an organization can be called a “project portfolio”.
    Further, after getting a project portfolio in an organization, deciding when to implement (schedule) these selected projects is also an important issue. By scheduling, we can make the resource consumption in each period satisfies the budget constraints. In reality, organizations in a firm such as departments are facing this problem. Multiple departments have multiple candidate projects to choose in a company. How to decide the project portfolio in each department so as to gain the overall maximum profit in a firm? This is a very complex problem in reality.
    Although project selection and scheduling problem has been discussed in depth and several related models have been proposed, none of them discussed this problem in a multiple-department environment. For this reason, our paper focuses on the project selection and scheduling problem in a multiple-department environment. Owing to project selection and scheduling problem belongs to a typical combination problem, we try to propose a problem-specific genetic algorithm in project selection and scheduling problem to find a satisfactory result in our paper.
    In addition, due to deciding the parameters of the proposed genetic algorithm are necessary, Taguchi method will be applied in the process for deciding the most suitable parameters.

    Table of contents Abstract in Chinese I Abstract II Table of contents V Figures VII Tables VIII Chapter 1 Introduction 1 1.1 Background and motivation 1 1.2 Problem statement 2 1.3 Research objective 4 1.4 Research framework 5 Chapter 2 Literature review 6 2.1 Project portfolio 6 2.2 Project selection 7 2.2.1 Project selection without scheduling function 8 2.2.2 Project selection with scheduling function 10 2.3 Genetic algorithms 11 2.3.1 Basic principles of genetic algorithms 12 2.3.2 A general procedure of genetic algorithms 15 2.3.3 The reason of applying genetic algorithms 17 2.4 Taguchi method 17 Chapter 3 The proposed model 19 3.1 Problem formulation 19 3.1.1 Decision variables 20 3.1.2 Objective function 20 3.1.3 Total budget constraints 21 3.1.4 Department budget constraints 21 3.1.5 Period budget constraints 21 3.1.6 Completing time constraints 22 3.1.7 Technical interdependence constraints 22 3.1.8 Risk-related constraints 23 3.1.9 Other constraints 23 3.2 A genetic algorithms for project selection and scheduling in multi- department 26 3.2.1 Direct representation of problem 26 3.2.2 Initialization 29 3.2.3 Fitness function 30 3.2.4 Elitism 31 3.2.5 Crossover 31 3.2.6 Mutation 31 3.2.7 New generation 32 3.3 Outline of the proposed model 34 Chapter 4 Simulation and result analysis 36 4.1 The experimental environment and design 36 4.2 Case generator 38 4.3 Experimental results 39 4.3.1 The generated cases 40 4.3.2 Experiment results 41 4.4 Comparison with AMPL modeling language 48 4.5 Application for large-scale problem 50 Chapter 5 Conclusions and suggestions 57 5.1 Conclusions 57 5.2 Further research suggestions 58 Reference 59 Appendix A The generated project cases for Case 1 to Case 6 62 Appendix B The data of Taguchi method process of each case 86 Appendix C The APML model file 96 Appendix D The results solved by AMPL for each case 99

    Reference
    1. 丁信志(2002)，「田口方法應用於高速放電加工製程之最佳參數設計」，碩士論文，國立高雄第一科技大學機械與自動化工程所。
    2. 姜台林(2001)，「整合式智慧型最佳化參數設計之研究」，博士論文，國立交通大學工業工程與管理研究所。
    3. 劉偉遠(2003)，「應用基因演算法於批次生產排程系統做為電力預估最佳化之研究」，碩士論文，國立高雄第一科技大學機械與自動化工程所。
    4. 蘇筵仁(2003)，「應用田口方法於0201被動元件製程參數優化」，碩士論文，華梵大學工業管理研究所。
    5. Alidi, A. (1996). “Use of the analytical hierarchy process to measure the initial viability of industrial projects”. International Journal of Project Management, 14, pp. 205-208.
    6. Antony, J., Antony, F. J. (2001). “Teaching the Taguchi method to industrial engineers”. Work Study, 50, pp. 141-149.
    7. Ghasemzadeh, F., Archer, N., Iyogun, P. (1999). “A zero–one model for project portfolio selection and scheduling”. Journal of the Operational Research Society, 50, pp. 745–755.
    8. Archer, N., Ghasemzadeh, F. (1999). “An integrated framework for project portfolio selection”. International Journal of Project Management, 17, pp. 207-216.
    9. Biethahn, J., Nissen, V. (1995). Evolutionary Algorithms in Management Applications. Springer, Berlin.
    10. Bohanec, M., Rajkovic, V., Semolic, B., Pogacnik, A. (1995). “Knowledge-based portfolio analysis for project evaluation”. Information & Management, 28, pp. 293-302.
    11. Brussion, N. (1980). “The function of project evaluation”. R&D management, 10, pp. 61-65.
    12. Buss, Martin, D. J. (1983). “How to rank computer projects”. Harvard Business Review, 61, pp. 118-25.
    13. Coffin, M. A., Taylor, B. W. (1996). “R&D project selection and scheduling with a filtered beam search approach”. IIE Transactions, 28, pp. 167-176.
    14. Evans, G. W., Fairbairn, R. (1989). “Selection and scheduling of advanced missions for NASA using 0-1 integer linear programming”. Journal of the Operational Research Society, 40, pp. 971-981.
    15. Fend, C. W., Liu, L. (1997). “Using genetic algorithms to solve construction time-cost trade-off problems”. Journal of Computing in Civil Engineering, 11, pp. 184-189.
    16. Gen, M., Cheng, R. (2000). Genetic Algorithms & Engineering Optimization. Wiley, New York.
    17. George, P. M., Pillai, N., Shah, N. (2004). “Optimization of shot peening parameters using Taguchi technique”. Journal of Materials Processing Technology, 153–154, pp. 925–930.
    18. Greer, J., Nussbaum, W. K. (1990). Cost Analysis and Estimating: Tools and Techniques. Springer-Verlag, Berlin.
    19. Gupta, S. K., Kyparisis, J., Ip, C. M. (1992). “Project selection and sequencing to maximize net present value of the total return”. Management Science, 35, pp. 751-752.
    20. Holland, J. H. (1976). Adaptation in Nature and Artificial Systems. Univ. of Michigan Press, Ann Arbor.
    21. Humphreys, K. K., English, L. (1993). Project and Engineers’ Handbook. Marcel Dekker, Inc., New York.
    22. Kemerer, C. F. (1987). “An empirical validation of software cost estimation models”. Communications of the ACM, 30, pp. 416-442.
    23. Khaw, F. C., Lim, B. S., Lim, E. N. (1995). “Optimal design of neural networks using the Taguchi method”. Neurocomputing, 7, pp. 225-245.
    24. Kim, S. J., Kim, K. S., Jang, H. (2003). “Optimization of manufacturing parameters for a brake lining using Taguchi method”. Journal of Materials Processing Technology, 136, pp. 202–208.
    25. Lucas, H. C., Moor, Jr. (1976). “A multiple-criterion scoring approach to information system project selection”. INFO, 14, pp. 1-2.
    26. Michalewicz, Z. (1996). Genetic Algorithm + Data Structure = Evolution Programs. Springer-Verlag, New York.
    27. Mikkola, J. H. (2001). “Portfolio management of R&D projects: implications for innovation management”. Technoration, 21, pp. 423-435.
    28. Muralidhar, K., Santhanam, R., Wilson, R. L. (1990). “Using the analytic hierarchy process for information system project selection”. Information & Management, 18, pp. 87-95.
    29. Raz, Tzvi. (1997). “An iterative screening methodology for selecting project alternatives”. Project Management Journal, 28, pp. 34-39.
    30. Saaty, T. (1980). The analytic hierarchy process, McGraw-Hill, New York.
    31. Santhanam, R., Muralidhar, K., Schniederjans, M. (1989). “A Zero-One Goal Programming Approach for Information System Project Selection”. OMEGA, 17, pp. 583-593.
    32. Santos, B. L. (1989). “Selecting information system projects: problems, solutions and challenges”. Hawaii Conference on System Sciences, pp. 1131-1140.
    33. Schniederjans, M., Wilson, R. L. (1991). “Using the analytic hierarchy process and goal programming for information system project selection”. Information & Management, 20, pp. 333-342.
    34. Schniederjans, M., Santhanam, R. (1993). “A multi-objective constrained resource information system project selection method”. European Journal of Operational Research, 70, pp. 244-253.
    35. Stamelos, I., Angelis, L. (2001). “Managing uncertainty in project portfolio cost estimation”. Information and Software Technology, 43, pp. 759-768.
    36. Sun, H., Ma, T. (2005). “A packing-multiple-boxes model for R&D project selection and scheduling”. Technovation, 25, pp. 1355-1361.
    37. Tseng, C. C. (1996). ”Multi-mode resource constrained project scheduling problems”. Ph.D. dissertation, Department of Industrial Engineering and Management Tokyo Institute of Technology.
    38. AMPL website. (2005).
    http://www.ampl.com/
    39. Siemens Corporation website. (2005). http://www.siemens.com/index.jsp?sdc_p=t4cz3s7uo1031318dpnflmi1032487&sdc_sid=16158509655&

    QR CODE
    :::