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研究生： 葉煒揚
Dedy Suryadi
論文名稱： Machine-Process Grouping Considering Setup and Waiting Times Using Particle Swarm Optimization
指導教授： 許棟樑
Sheu, Daniel
口試委員:
學位類別： 碩士
Master
系所名稱： 工學院 - 工業工程與工程管理學系
Department of Industrial Engineering and Engineering Management
論文出版年： 2009
畢業學年度： 97
語文別： 英文
論文頁數： 78
中文關鍵詞： machine-process grouping 、setup time 、waiting time 、PSO
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    • This research focuses on developing a machine-process grouping algorithm in which taking waiting time and setup time into the optimization’s objective. The method used to find the best grouping is Particle Swarm Optimization (PSO). Thus, the algorithm is named SWPSO (Setup & Waiting, PSO). A particle representation is created with dimension length equals to (M + P), i.e. sum of number of machines and recipes to be processed. The position boundaries are between 0 and min{M,P}. For the fitness function in PSO, estimations are used for both setup and waiting time. In particular for waiting time, there is a proportion of non-processing time in the makespan which needs to be found. Through repeated simulation, it is found out that the actual makespan is 1.73 times the ideal one. Thus, there is a proportion of non-processing time as much as 0.73. The grouping result is compared with the result from previous research (Cheng, 2008). It is called SGRAM (Setup, Dendogram) because it considers only setup time and uses dendogram to find the best grouping. Comparison is also made with the original grouping, most number of groups, and no grouping. For most performance measures under different lot release policies, SWPSO shows better performance. In the comparison of two dispatching policies for SWPSO grouping, CR shows better performance than FIFO in all but average throughput. Furthermore, in additional cases where the numbers of machine and recipes to be processed are perturbed, SWPSO consistently shows better performance than or at least equal to SGRAM for most performance measures.

    Chapter 1 Introduction 1.1 Background 1 1.2 Problem Overview 1 1.3 Contribution 2 1.4 Research Structure 2 Chapter 2 Literature Review 2.1. Cellular Manufacturing 4 2.2 Solving Part-Machine Grouping Problem 4 2.2.1 Array-Based Methods 5 2.2.1.1 Bond Energy Analysis (BEA) 5 2.2.1.2 Rank Order Clustering (ROC) 6 2.2.1.3 Direct Clustering Analysis (DCA) 8 2.2.2 Similarity Coefficient 8 2.2.3 Performance-Enhancement Algorithm (PEA) 10 2.3 Particle Swarm Optimization (PSO) 11 2.3.1 PSO Parameters 11 2.3.2 PSO Termination Criterion 12 2.3.3 PSO Applications 12 Chapter 3 Research Methodology 3.1 Methodology Overview 16 3.2 Symbols Definition 16 3.2.1 Notation 16 3.2.2 Terms in Particle Swarm Optimization (PSO) 18 3.3 Problem Statement 19 3.4 Problem Modeling 21 3.5 Research Approach 22 3.5.1 Particle Swarm Optimization (PSO) Algorithm 22 3.5.2 Decoding Particle 33 3.5.2.1 Decoding First Part of Particle 33 3.5.2.2 Decoding Second Part of Particle 34 3.5.3 PSO Parameters 35 3.5.4 Position Boundaries 35 3.5.5 Penalty for Infeasible Solution 35 3.6 An Example for PSO Encoding/Decoding 36 Chapter 4 Computer Program and Simulation Design 4.1 Designing Program for SWPSO Algorithm 39 4.1.1 Pseudo-Code for Main Function 39 4.1.2 Pseudo-Code for Create Particle 41 4.1.3 Pseudo-Code for Calculating Expected Changeover Time 41 4.1.4 Pseudo-Code for Calculating Expected Waiting Time 42 4.2 The Ratio of Non-Processing Time 42 4.3 Simulation Conditions 47 4.3.1 Assumptions 47 4.3.2 Performance Measures and Scenarios 47 4.3.3 Product Composition and Due Date 48 4.3.4 Changeover Time 49 Chapter 5 Result and Analysis 5.1 Grouping Result 50 5.2 FlexSim Model 51 5.3 Grouping Comparison 52 5.3.1 Average Throughput 53 5.3.2 Average Cycle Time 56 5.3.3 Average WIP 58 5.3.4 Average Delivery Rate 60 5.3.5 Hypothesis Testing 63 5.4 Dispatching Rules Comparison 64 5.4.1 Average Throughput 65 5.4.2 Average Cycle Time 65 5.4.3 Average WIP 66 5.4.4 Average Delivery Rate 66 5.5 Additional Cases 67 5.6 Performance Comparison for Additional Cases 70 5.6.1 Case 1: Few Machines (10) and Few Recipes to Be Processed (10) 71 5.6.2 Case 2: Few Machines (10) and Many Recipes to Be Processed (20) 72 5.6.3 Case 3: Many Machines (25) and Few Recipes to Be Processed (10) 74 5.6.4 Case 4: Many Machines (25) and Many Recipes to Be Processed (20) 75 5.7 Summary for Additional Cases 76 Chapter 6 Conclusion and Future Research 6.1 Conclusion 78 6.2 Future Research 78

    1.Adenso-Diaz, B., S. Lozano, I. Eguia, “Part-machine grouping using weighted similarity coefficients”, Computers & Industrial Engineering, Vol. 48, pp553-570, 2005.
    2.Ai, T.J., V. Kachitvichyanukul, “A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery”, Computers and Operations Research, 2008.
    3.Cao, D., M. Chen, “Using Penalty Function and Tabu Search to Solve Cell Formation Problems with Fixed Cell Cost”, Computers and Operations Research, 2004.
    4.Chan, H.M., D.A. Milner, “Direct clustering algorithm for group formation in cellular manufacture”, Journal of Manufacturing Systems, Vol. 1, pp65-75, 1982.
    5.Cheng, C.E., “Time-based Machine-Process Grouping”, Thesis, National Tsing Hua University, Taiwan, 2008.
    6.Chu, C.H., M. Tsai, “A comparison of three array-based clustering techniques for manufacturing cell formation”. Int J of Prod Res, Vol. 28, No. 8, pp1417-1433, 1990.
    7.Duran, O., N. Rodriguez, L. A. Consalter, “A PSO-based clustering algorithm for manufacturing cell design”, Workshop on Knowledge Discovery and Data Mining, 2008.
    8.Hassan, R., B. Cohanim, O. de Weck, “A comparison of particle swarm optimization and the genetic algorithm”, American Institute of Aeronautics and Astronautics, 2004.
    9.J. Kennedy, R. C. Eberhart, “Particle swarm optimization”, Proceedings of the IEEE International Conference on Neural Networks, pp.1942-1948, 1995.
    10.Lee, W.S., “A performance-enhancement algorithm for the part-machine grouping problem”. Int J Adv Manuf Technol., 2008.
    11.Liu, Z., “Investigation of particle swarm optimization for job shop scheduling problem”, Third International Conference on Natural Computation, 2007.
    12.King, J.R., “Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm”. International Journal of Production Research, Vol. 18, pp213-232, 1980.
    13.McAuley, J., “Machine grouping for efficient production”. The Production Engineer, pp.53-57, 1972.
    14.McCormick, W.T., P.J. Schweitzer, T.W. White, “Problem decomposition and data reorganization by a clustering technique”, Operations Research, Vol. 20, pp. 993-1009, 1972.
    15.Mohemmed, A.W., et al., “Solving shortest path problem using particle swarm optimization”, Appl. Soft Comput. J., 2008.
    16.Trelea, I.C. “The particle swarm optimization algorithm: convergence analysis and parameter selection”. Inform Process Lett, Vol.85, No.6, pp317-325, 2003.
    17.Winston, W. L., “Operations Research Applications and Algorithms”. PWS-KENT, 2nd ed., 1991.
    18.Yin, Y., K. Yasuda, “Similarity coefficient methods applied to the cell formation problem: a comparative investigation”, Computers & Industrial Engineering, Vol.48, pp.471-489, 2005.
    19.Zhang, H., H. Li, C.M. Tam, “Particle swarm optimization for resource-constrained project scheduling”, International Journal of Project Management, Vol.24, pp.83-92, 2005.

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