研究生: |
Tran Dinh Duy Thao Tran - Dinh Duy Thao |
---|---|
論文名稱: |
混合式基因演算法求解製鞋業車縫線資源限制生產線平衡問題 Hybrid Genetic Algorithm for Solving Resource-Constrained Assembly Line Balancing Problem in Footwear Sewing Line |
指導教授: |
吳建瑋
Chien-Wei Wu 陳建良 James C. Chen |
口試委員: |
王孔政
Kung-Jeng Wang |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 工業管理系 Department of Industrial Management |
論文出版年: | 2012 |
畢業學年度: | 100 |
語文別: | 英文 |
論文頁數: | 43 |
中文關鍵詞: | assembly line design 、equipments assignment 、genetic algorithm 、ranked-positional-weighted heuristic 、heuristic procedure |
外文關鍵詞: | assembly line design, equipments assignment, genetic algorithm, ranked-positional-weighted heuristic, heuristic procedure |
相關次數: | 點閱:264 下載:5 |
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In this paper we observe the assembly line design problem in a sewing line of a shoe manufacturing company. One new shoe style is considered as one separate model. The combination of workstations, equipments and operators needs to be decided before running the production of each new model. The model parameters are improved during the production because of task combination or learning curve; therefore the optimal solution of the problem is changeable. As a result, it is critical to develop a robust procedure to rapidly deliver an “optimal” line design.
In the study, a rank-positional-weighted heuristics and hybrid genetic algorithm are proposed to solve the Resource Constrained Assembly Line Balancing Problem (RCALBP).. First the heuristics is developed to assign tasks and required machines into workstation. Then these solutions are used as an initiative population for the hybrid genetic algorithm. Experiment design is conducted to validate the performance of the proposed methods. One existing heuristics, new bidirectional heuristic for the assembly line balancing problem (ALBP), is chosen to compare. The output of these methods is analyzed and evaluated using statistical technique.
The result shows these methods do not reach different objective value for simple problem. When the difficulty of problem increases in term of size and shape, the proposed genetic algorithm achieves better results than existing heuristics. The developed problem is inherited from the work of researchers as well as the contribution of practitioners. Assumption is established and validated by experts in footwear making industry. Therefore, the proposed approaches are capable to apply in real manufacturing environment.
In this paper we observe the assembly line design problem in a sewing line of a shoe manufacturing company. One new shoe style is considered as one separate model. The combination of workstations, equipments and operators needs to be decided before running the production of each new model. The model parameters are improved during the production because of task combination or learning curve; therefore the optimal solution of the problem is changeable. As a result, it is critical to develop a robust procedure to rapidly deliver an “optimal” line design.
In the study, a rank-positional-weighted heuristics and hybrid genetic algorithm are proposed to solve the Resource Constrained Assembly Line Balancing Problem (RCALBP).. First the heuristics is developed to assign tasks and required machines into workstation. Then these solutions are used as an initiative population for the hybrid genetic algorithm. Experiment design is conducted to validate the performance of the proposed methods. One existing heuristics, new bidirectional heuristic for the assembly line balancing problem (ALBP), is chosen to compare. The output of these methods is analyzed and evaluated using statistical technique.
The result shows these methods do not reach different objective value for simple problem. When the difficulty of problem increases in term of size and shape, the proposed genetic algorithm achieves better results than existing heuristics. The developed problem is inherited from the work of researchers as well as the contribution of practitioners. Assumption is established and validated by experts in footwear making industry. Therefore, the proposed approaches are capable to apply in real manufacturing environment.
Ağpak, K., & Gökçen, H. (2005). Assembly line balancing: Two resource constrained cases. International Journal of Production Economics, 96, 129-140.
APICCAPS. (2011). World Footwear 2011 Yearbook. In: Orgal Impressores.
Bautista, J., & Pereira, J. (2009). A dynamic programming based heuristic for the assembly line balancing problem. European Journal of Operational Research, 194, 787-794.
Baybars, İ. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management Science, 32, 909–932.
Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168, 694-715.
Boysen, N., Fliedner, M., & Scholl, A. (2007). A classification of assembly line balancing problems. European Journal of Operational Research, 183, 674-693.
Boysen, N., Fliedner, M., & Scholl, A. (2008). Assembly line balancing: Which model to use when? International Journal of Production Economics, 111, 509-528.
Chen, J.-S., Pan, J. C.-H., & Lin, C.-M. (2008). A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem. Expert Systems with Applications, 34, 570-577.
Chen, J. C., Chen, C.-C., Su, L.-H., Wu, H.-B., & Sun, C.-J. (2012). Assembly line balancing in garment industry. Expert Systems with Applications, 39, 10073-10081.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Boston, MA: Addison-Wesley.
Haupt, R. L., & Haupt, S. E. (2004). Practical Genetic Algorithms: John Wiley & Sons.
Helgeson, W. R. a. B., D.P. (1961). Assembly line balancing using the ranked positional weight technique. Journal of Industrial Engineering, 12, 394-398.
KA, D. J. (1975). An analysis of the behavior of a class of genetic adaptive systems. Ann Arbor: Dissertation, University of Michigan.
Kao, H.-H., Yeh, D-H, Wang, Y-H. (2011). Resource Constrained Assembly Line Balancing Problem Solved with Ranked Positional Weigth Rule. Review of Economics & Finance, 1923-8401.
Kao, H. H., & Yeh, D. H. (2006). A new approach for assembly line balancing problems. In The 36th international conference on computers and industrial engineering (pp. 3886-3897).
Kim, Y. K., Kim, Y. J., & Kim, Y. (1996). Genetic Algorithms for Assembly Line Balancing With Various objectives. Computers ind. Engng, Vol. 30, No. 3, 397-409.
Marketline. (2012). Footwear Industry Profile: Global. In. London (UK).
. Meet the Shoe: See what you often can't. In. Foot Locker: Striperpedia.
Mitchell, M. (1999). An Introduction to Genetic Algorithms. (Fifth printing ed.). Cambridge, Massachusetts*London, England.
Scholl, A., Fliedner, M., & Boysen, N. (2010). Absalom: Balancing assembly lines with assignment restrictions. European Journal of Operational Research, 200, 688-701.
Scholl, A., & Klein, R. (1997). SALOME: A bidirectional branch and bound procedure for assembly line balancing. INFORMS Journal on Computing, 9, 319-334.
Tasan, S. O., & Tunali, S. (2008). A review of the current applications of genetic algorithms in assembly line balancing. J Intell Manuf, 19, 49-69.
Yeh, D.-H., & Kao, H.-H. (2009). A new bidirectional heuristic for the assembly line balancing problem. Computers & Industrial Engineering, 57, 1155-1160.