Title

應用基因演算法改進零工式生產排程之研究-以半導體封裝模具廠為例

Translated Titles

Applying Genetic Algorithm for Job-Shop Scheduling Problems in Semiconductor Packaging Mold Manufacturing

Authors

陳建安

Key Words

零工式排程生產 ; 基因演算法 ; 總完工時間 ; 廠內部門總閒置時間 ; Job-shop scheduling problem ; Genetic Algorithm ; Total completion time ; factory idle time

PublicationName

成功大學工業與資訊管理學系碩士在職專班學位論文

Volume or Term/Year and Month of Publication

2017年

Academic Degree Category

碩士

Advisor

翁慈宗

Content Language

繁體中文

Chinese Abstract

隨著科技的進步,工業生產模式的改變,製造產業的模式及環境也有重大的改變。相較於過去,以往的規模式大量製造生產,再透過配銷、零售等通路將產品配銷到消費者手上。這種大量生產模式的缺點在於,客戶對於商品的選擇權利有限。隨著市場需求的多元及多樣,傳統的生產模式無法立即打造專屬的生產線,以提供符合客戶心中理想的產品,同時客戶所需求的數量也未必足以提供大量生產所投資的利潤,因此有適合多樣少量的客製化零工式排程生產模式,將成為滿足人類多元需求的生產模式。在這種模式中,每張訂單在生產製造的流程上不盡相同且彼此獨立,為了完成這樣多元化的訂單需求,如何調度及安排生產製造的流程變成相當重要的課題,良好的排程不但可以在最短的時間內完成所有任務,更可以減少資源的花費、提高企業的獲利。 本研究以同時降低總完工時間及廠內部門總閒置時間為績效排程目標,利用基因演算法在有限時間內找出零工式生產的多目標排程解,並分析在不同規模大小的訂單數量下進行排程,與傳統的派工方法進行比較 以達成高稼動率且高效率生產這兩大目標。 透過研究分析結果顯示,本研究之基因演算排程方法不論排程訂單規模之大小,仍然可以在較短的時間內得到優於傳統派工方法的排程解,且在運算時間上較傳統排程方法有大幅度改善。雖然透過此法得到的排程僅是近似最佳解,但是其排程品質卻可以有效的幫助於決策者在排程策略上提供決策依據。

English Abstract

In the past mass production model, products are produced in a large quantity, through distributions, retails, ect., to delivery products into customer. Because of the diversity of market demand, the traditional mass production mode can’t create a dedicated production line to provide customers ideal products immediately. Therefore, the production model of job-shop with small amount will be the best choice to fulfilled the customer demand. In this situation, each order in the manufacturing process is not the same but independent of each other. In order to complete needs of diverse orders, how to arrange manufacturing process becomes a very important issue. Good schedule not only can complete all the tasks in the shortest time, but also can reduce the cost of resources and increase the profitability for enterprises. This study focus on two goals. First, reduce the total completion time and factory idle time by using the genetic algorithm to find out schedules for job-shop scheduling within limited time. Second, at the same time, comparing with the traditional dispatching rule to analysis the scales of order quantity to achieve the high rate of movement and production efficiency. Through the result of research and analysis, it showed that regardless of the scales of order quantity in the schedule, genetic algorithm can still has the better outcome of schedule arrangement within a short time which comp.

Topic Category 管理學院 > 工業與資訊管理學系碩士在職專班
工程學 > 工程學總論
社會科學 > 管理學
Reference
  1. Akpinar, S. Bayhan, G. M., & Baykasoglu, A. (2013). Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks. Applied Soft Computing, 13(1), 574-589.
    連結:
  2. Baker, K. R. (1984). Sequencing rules and due-date assignments in a job shop.Management Science, 30(9), 1093-1104.
    連結:
  3. Brown, J. R. & Ozgur, C. O. (1997). Priority class scheduling: production scheduling for multi-objective environments. Production Planning & Control,8(8), 762-770.
    連結:
  4. Chang, P. C. Hsieh, J. C., & Lin, S. G. (2002). The development of gradual-priority weighting approach for the multi-objective flowshop scheduling problem. International Journal of Production Economics, 79(3), 171-183.
    連結:
  5. Gao, J. Gen, M., Sun, L., & Zhao, X. (2007). A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Computers & Industrial Engineering, 53(1), 149-162.
    連結:
  6. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. U Michigan Press.
    連結:
  7. Ishibuchi, H., & Murata, T. (1998). A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(3), 392-403.
    連結:
  8. Ivens, P., & Lambrecht, M. (1996). Extending the shifting bottleneck procedure to real-life applications. European Journal of Operational Research, 90(2), 252-268.
    連結:
  9. Pezzella, F., Morganti, G., & Ciaschetti, G. (2008). A genetic algorithm for the flexible job-shop scheduling problem. Computers & Operations Research, 35(10), 3202-3212.
    連結:
  10. Ruiz, R., Maroto, C., & Alcaraz, J. (2006). Two new robust genetic algorithms for the flowshop scheduling problem. Omega, 34(5), 461-476.
    連結:
  11. Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221-248.
    連結:
  12. Sun, L., Lin, L., Wang, Y., Gen, M., & Kawakami, H. (2015). A Bayesian Optimization-based Evolutionary Algorithm for Flexible Job Shop Scheduling.Procedia Computer Science, 61, 521-526.
    連結:
  13. 中文文獻
  14. 王培珍(1996),「應用遺傳演算法與模擬在動態排程問題之探討」,中原大學工業工程研究所碩士論文。
  15. 英文文獻
  16. Deb, K. Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
  17. Lee, K. M., Yamakawa, T., & Lee, K. M. (1998, April). A genetic algorithm for general machine scheduling problems. In Knowledge-Based Intelligent Electronic Systems, 1998. Proceedings KES'98. 1998 Second International Conference on (Vol. 2, pp. 60-66). IEEE.
  18. Man, K. F., et. al. (1999), Genetic Algorithms, Springer-Verlag, London.
  19. Schaffer, J. D. (1985, July). Multiple objective optimization with vector evaluated genetic algorithms. In Proceedings of the 1st International Conference on Genetic Algorithms (pp. 93-100). L. Erlbaum Associates Inc.