簡易檢索 / 詳目顯示

研究生: Duc-Hoc Tran
Duc-Hoc Tran
論文名稱: FUZZY CLUSTERING CHAOTIC-BASED DIFFERENTIAL EVOLUTION FOR SOLVING RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM
FUZZY CLUSTERING CHAOTIC-BASED DIFFERENTIAL EVOLUTION FOR SOLVING RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM
指導教授: 鄭明淵
Min-Yuan Cheng
口試委員: 周瑞生
Jui-Sheng Chou
曾惠斌
Hui-Ping Tserng
張陸滿
Luh-Maan Chang
學位類別: 碩士
Master
系所名稱: 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年: 2012
畢業學年度: 100
語文別: 英文
論文頁數: 77
中文關鍵詞: Resource constrainedDifferential Evolution
外文關鍵詞: Resource constrained, Differential Evolution
相關次數: 點閱:272下載:10
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
  • Project scheduling is an important part of project planning of many management companies. The resource constraint problem seeks to find optimal sequence for optimizing objective under the precedence constraint and limited amount of resource. In this research, a novel optimization model, named as Fuzzy Clustering Chaotic-based Differential Evolution for solving Resource Constrained Project Scheduling Problem (FCDE-RCPSP), is introduced. Fuzzy Clustering Chaotic-based Differential Evolution (FCDE) is developed by integrating original Differential Evolution with fuzzy c-means clustering and chaotic techniques to tackle complex optimization problems. Chaotic was exploited to prevent the new approach from premature convergence. Meanwhile, fuzzy c-means clustering acts as several multi-parent crossover operators to utilize the information of the population efficiently to enhance the convergence. Experimental studies revealed that the new optimization model is a promising alternative to assist project managers in dealing with resource constrained project scheduling problems.


    Project scheduling is an important part of project planning of many management companies. The resource constraint problem seeks to find optimal sequence for optimizing objective under the precedence constraint and limited amount of resource. In this research, a novel optimization model, named as Fuzzy Clustering Chaotic-based Differential Evolution for solving Resource Constrained Project Scheduling Problem (FCDE-RCPSP), is introduced. Fuzzy Clustering Chaotic-based Differential Evolution (FCDE) is developed by integrating original Differential Evolution with fuzzy c-means clustering and chaotic techniques to tackle complex optimization problems. Chaotic was exploited to prevent the new approach from premature convergence. Meanwhile, fuzzy c-means clustering acts as several multi-parent crossover operators to utilize the information of the population efficiently to enhance the convergence. Experimental studies revealed that the new optimization model is a promising alternative to assist project managers in dealing with resource constrained project scheduling problems.

    TABLE OF CONTENTS ABSTRACT i ACKNOWLEDGEMENTS ii TABLE OF CONTENTS iv ABBREVIATIONS AND SYMBOLS vii Abbreviations vii Symbols viii Open and Closed Intervals viii LIST OF FIGURES ix LIST OF TABLES xi CHAPTER 1. INTRODUCTION 1 1.1 Research Motivation 1 1.2 Research Objective 5 1.3 Scope Definition 6 1.4 Methodology 6 1.4.1 Introduction 9 1.4.2 Literature Review 9 1.4.3 New algorithm development 10 1.4.4 Model Construction 11 1.4.5 Case study 11 1.4.6 Conclusions and Recommendations 11 1.5 Study Outline 12 CHAPTER 2. LITERATURE REVIEW 13 2.1 Resource-constrained project scheduling 13 2.2 Differential Evolution optimization algorithm 14 2.3 Fuzzy c-means clustering 19 2.3.1 General introduction 19 2.3.2 Integrate Fuzzy c-means clustering with Differential Evolution 23 2.4 Chaotic Approach 25 2.4.1 General introduction 25 2.4.2 Integrate Chaos approach with Differential Evolution 31 CHAPTER 3. FUZZY C-MEANS CLUSTERING CHAOTIC BASED DIFFERENTIAL EVOLUTION (FCDE) 33 3.1 Proposed Model FCDE 34 3.1.1 Initialization of vectors and parameter setting 34 3.1.2 Mutation 35 3.1.3 Crossover 36 3.1.4 Selection 37 3.1.5 Chaos operator 37 3.1.6 Fuzzy c-means clustering 38 3.1.7 Stopping condition 38 3.2 Verify new approach with testing functions 38 3.2.1 Input Parameters 40 3.2.2 Testing results and comparison 41 3.3 Summary 45 CHAPTER 4. FUZZY CLUSTERING CHAOTIC BASED DIFFERENTIAL EVOLUTION FOR RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM 46 4.1 Model Architecture 46 4.2 Model adaptation process 47 4.3 Particle solution representation and serial generation for RCPSP 49 4.3.1 General introduction 49 4.3.2 Illustration of algorithm 53 CHAPTER 5. CASE STUDY 55 5.1 Optimization result of FCDE-RCPSP 58 5.2 Result Comparisons 59 5.3 Hypothesis test 60 CHAPTER 6. CONCLUSIONS AND RECOMENDATIONS 62 6.1 Conclusions 62 6.2 Recommendations and future directions 64 APPENDIX 65 Matlab of newly algorithm FCDE 65 Matlab of case study 71 BIBLIOGRAPHY 75

    BIBLIOGRAPHY
    1. Rainer, K., Efficient priority rules for the resource-constrained project scheduling problem. Journal of Operations Management, 1996. 14(3): p. 179-192.
    2. Zhang, H., H. Li, and C. Tam, Particle swarm optimization for resource-constrained project scheduling. International Journal of Project Management, 2006. 24(1): p. 83-92.
    3. Zhang, H., et al., Particle swarm optimization-based schemes for resource-constrained project scheduling. Automation in Construction, 2005. 14(3): p. 393-404.
    4. Yan, L., et al. Optimization of resource allocation in construction using genetic algorithms. in Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on. 2005.
    5. Bell, C.E. and J. Han, A new heuristic solution method in resource-constrained project scheduling. Naval Research Logistics (NRL), 1991. 38(3): p. 315-331.
    6. Fayer F, B., Some efficient multi-heuristic procedures for resource-constrained project scheduling. European Journal of Operational Research, 1990. 49(1): p. 3-13.
    7. Brucker, P., et al., Resource-constrained project scheduling: Notation, classification, models, and methods. European Journal of Operational Research, 1999. 112(1): p. 3-41.
    8. Das, S. and P.N. Suganthan, Differential Evolution: A Survey of the State-of-the-Art. IEEE Transactions on evolutionary computation, 2011. 15(1).
    9. Jeffcoat, D.E. and R.L. Bulfin, Simulated annealing for resource-constrained scheduling. European Journal of Operational Research, 1993. 70(1): p. 43-51.
    10. M.G.A, V., Tabu search for resource-constrained scheduling. European Journal of Operational Research, 1998. 106(2–3): p. 266-276.
    11. Tsai, Y.-W. and D. D. Gemmill, Using tabu search to schedule activities of stochastic resource-constrained projects. European Journal of Operational Research, 1998. 111(1): p. 129-141.
    12. Mori, M. and C.C. Tseng, A genetic algorithm for multi-mode resource constrained project scheduling problem. European Journal of Operational Research, 1997. 100(1): p. 134-141.
    13. Storn, R.M. and K. Price, Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 1997. 11: p. 341–359.
    14. Price, K.V., R.M. Storn, and J.A. Lampinen, Differential Evolution A Practical Approach to Global Optimization. Springer-Verlag Berlin Heidelberg 2005, 2005.
    15. Landa Becerra, R. and C.A.C. Coello, Cultured differential evolution for constrained optimization. Computer Methods in Applied Mechanics and Engineering, 2006. 195(33–36): p. 4303-4322.
    16. Jia, D., G. Zheng, and M. Khurram Khan, An effective memetic differential evolution algorithm based on chaotic local search. Information Sciences, 2011. 181(15): p. 3175-3187.
    17. Bedri Ozer, A., CIDE: Chaotically Initialized Differential Evolution. Expert Systems with Applications, 2010. 37(6): p. 4632-4641.
    18. Noman, N. and H. Iba, Accelerating Differential Evolution Using an Adaptive Local Search. Evolutionary Computation, IEEE Transactions on, 2008. 12(1): p. 107-125.
    19. Kim, J.H. and J. Stringer, Applied Chaos. Both of Electric Power Research Institute, Palo Alto, California, 1992.
    20. Gen, M. and R. Cheng, Genetic algorithms and engineering design. Ashikaga Institute of Technology, 1996.
    21. Davis, E. and G. Heidorn, An algorithm for optimal project scheduling under multiple resources constraints. Management Science, 1971. 21: p. B803-B816.
    22. Talbot, F. and J. Patterson, An efficient integer programming algorithm with network cuts for solving resource-constraints scheduling problems. Management Science, 1978. 24: p. 1163-1174.
    23. Blazewicz, J., Complexity of computer scheduling algorithms under resource constraints,. First meeting of the AFCET-SMF on Applied Mathematics, 1978: p. 169-187.
    24. Zhan, J., Heuristics for scheduling resource-constrained projects in MPM networks. European Journal of Operational Research, 1994. 76(1): p. 192-205.
    25. Boctor, F.F., Some efficient multi-heuristic procedures for resource-constrained project scheduling. European Journal of Operational Research, 1990. 49(1): p. 3-13.
    26. Chan, W.-T., D.K.H. Chua, and G. Kannan, Construction Resource Scheduling with Genetic Algorithms. Journal of Construction Engineering and Management, 1996. 122(2): p. 125-132.
    27. Mezura-Montes, E., et al., Simple Feasibility Rules and Differential Evolution for Constrained Optimization. In Proceedings of the 3rd Mexican International Conference on Artificial Intelligence (MICAI 2004), 2004.
    28. Kit Po, W. and D. ZhaoYang. Differential Evolution, an Alternative Approach to Evolutionary Algorithm. in Intelligent Systems Application to Power Systems, 2005. Proceedings of the 13th International Conference on. 2005.
    29. Liu, X., D. Zexi, and W. Lingling, A Chaotic Mutation Differential Evolution Algorithm. Energy Procedia, 2011. 13(0): p. 5242-5247.
    30. Cai, Z., et al., A clustering-based differential evolution for global optimization☆. Applied Soft Computing, 2011. 11(1): p. 1363-1379.
    31. Kwedlo, W., A clustering method combining differential evolution with the K-means algorithm. Pattern Recognition Letters, 2011. 32(12): p. 1613-1621.
    32. Wang, Y.-J., J.-S. Zhang, and G.-Y. Zhang, A dynamic clustering based differential evolution algorithm for global optimization. European Journal of Operational Research, 2007. 183(1): p. 56-73.
    33. Wiley, J. and L. Sons, Advances in Fuzzy Clustering and its Applications. 2007.
    34. Jain, A.K., M.N. Murty, and P.J. Flynn, Data clustering: a review. ACM Computing Surveys, 1999(31 (3)): p. 264–323.
    35. Han, J., M. Kamber, and K.H. Tung, Spatial Clustering Methods in Data Mining: A survey. School of Computing Science, Simon Fraser University, 2001.
    36. Bezdek, J.C., R. Ehrlich, and W. Full, FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 1984. 10(2–3): p. 191-203.
    37. Deb, K., A population-based algorithm-generator for real-parameter optimization. Soft Comput., 2005. 9(4): p. 236-253.
    38. Alligood, K.T., T.D. Sauer, and J.A. Yorke, Chaos: An Introduction to Dynamical Systems. Springer, 1996.
    39. Cheng, M.-Y. and K.-Y. Huang, Genetic algorithm-based chaos clustering approach for nonlinear optimization. Journal of Marine Science and Technology, 2010. 18(3): p. 435-441.
    40. Caponetto, R., et al., Chaotic sequences to improve the performance of evolutionary algorithms. Evolutionary Computation, IEEE Transactions on, 2003. 7(3): p. 289-304.
    41. Yibao, C., X. Hongmei, and M. Tiezhu. Chaos-Ant Colony Algorithm and its application in continuous space optimization. in Control and Decision Conference, 2008. CCDC 2008. Chinese. 2008.
    42. Ohya, M., Complexities and their applications to characterization of chaos. International Journal of Theoretical Physics, 1998. 37(1): p. 495-505.
    43. T.Alligood, K., T. D.Sauer, and J. A.Youke, Chaos: An Introduction to Dynamical Systems. Springer, 2000.
    44. Suganthan, P.N., et al., Problem definitions and evaluation criteria for the CEC2005 special session on realparameter optimization. 2005.
    45. Lee, J.K. and Y.D. Kim, Search heuristics for resource constrained project scheduling. Journal of the Operational Research Society, 1996. 47(5): p. 678– 689.
    46. Kolisch, R., Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation. European Journal of Operational Research, 1996. 90(2): p. 320-333.
    47. Sakalauskas, L. and G. Felinskas, Optimization of resource constrained project schedules by genetic algorithm based on the job priority list. Information Technology and Control, 2006. 35(4).
    48. K, S., S. G, and C. R, Construction Project Management: A Practical Guide to Field Construction Management (5th Edition). John Wiley and Son, Inc., Hoboken, New Jersey, 2008.

    QR CODE