DOI QR코드

DOI QR Code

A Study on Combinatorial Dispatching Decision of Hybrid Flow Shop : Application to Printed Circuit Board Process

혼합 흐름공정의 할당규칙조합에 관한 연구: 인쇄회로기판 공정을 중심으로

  • Received : 2012.07.05
  • Accepted : 2012.11.27
  • Published : 2013.02.15

Abstract

Dispatching rule plays an important role in a hybrid flow shop. Finding the appropriate dispatching rule becomes more challenging when there are multiple criteria, uncertain demands, and dynamic manufacturing environment. Using a single dispatching rule for the whole shop or a set of rules based on a single criterion is not sufficient. Therefore, a multi-criteria decision making technique using 'the order preference by similarity to ideal solution' (TOPSIS) and 'analytic hierarchy process' (AHP) is presented. The proposed technique is aimed to find the most suitable set of dispatching rules under different manufacturing scenarios. A simulation based case study on a PCB manufacturing process is presented to illustrate the procedure and effectiveness of the proposed methodology.

Keywords

References

  1. Arbel, A. (1989), Approximate articulation of preference and priority derivation, European Journal of Operational Research, 43(3), 314-326.
  2. Arbel, A. and Vargas, L. G. (1999), Preference simulation and performance programming : robustness issues in priority derivation, European Journal of Operational Research, 43, 200-209.
  3. Arnold, J. R. T. (1992), Introduction to Materials Management, Prentice Hall, New Jersey.
  4. Banae Costa, C. A., Vansnick, J. C., and Vansnick (1999), The MAC BETH approach : basic ideas, software and application, N. Meskens, M. Roubens (Eds.), Advances in Decision Analysis, Kluwer Academic Publishers, Dordrecht, 131-157.
  5. Bareet, R. T. and Barman, S. (1986), A SLAMII simulation study of a simplified flow shop, Simulation, 47(5), 181-189. https://doi.org/10.1177/003754978604700502
  6. Blackstone, J. H. Phillips, D. T., and Hogg, G. L. (1982), A state of art survey of dispatching rules for manufacturing job shop operation, International journal of production research, 20(1), 27-45. https://doi.org/10.1080/00207548208947745
  7. Botta-Genoulaz, V. (2000), Hybrid flow shop scheduling with precedence constrains and time lags to minimize maximum lateness, International journal of production economics, 64(1-3), 101-111. https://doi.org/10.1016/S0925-5273(99)00048-1
  8. Braglia, M. and Petroni, A. (1999), Data envelopment analysis for dispatching rule section, production planning and control, 10(5), 454- 461. https://doi.org/10.1080/095372899232984
  9. Brah, S. A. and Loo, L. L. (1999), Heuristics for scheduling in a flow shop with multiple processors, European Journal of Operational Research, 113, 113-122. https://doi.org/10.1016/S0377-2217(97)00423-2
  10. Braman, S. (1997), Simple priority rule combinations : an approach to improve both flow time and tardiness, International journal of production research, 25, 2857-2870.
  11. Cha, Y. and Jung, M. (2003), Satisfaction assessment of multi-objective schedules using neural fuzzy methodology, International journal of production research, 41, 1831-1849. https://doi.org/10.1080/1352816031000074937
  12. Chan, F. T. S. and Chung, S. H. (2004), A multi-criterion genetic algorithm for order distribution in a demand driven supply chain, International Journal of Computer Integrated Manufacturing, 17, 339-351. https://doi.org/10.1080/09511920310001617022
  13. Chang, Y. L., Sueyoshi, T., and Sullivan, R. S. (1996), Ranking dispatching rules by data envelopment analysis in a job shop environment, IIE Transactions, 28, 631-642.
  14. Chen, L. H. and Chen, Y. H. (1996), A design procedure for robust job shop manufacturing system under a constraint using computer simulation experiments, Computers and Industrial Engineering, 30, 1-12.
  15. eM-plant User's Manual Version 5.5 (2001), Tecnomatix Technologies, Stuttgart, Germany.
  16. ExpertFit(R) User's Guide (1998), Averill M. Law and Associates, Tucson, AZ.
  17. Fanti, M. P., Maione, B., Naso, D., and Turchiano, B. (1998), Genetic multi-criteria approach to flexible line scheduling, International Journal of Approximate Reasoning, 19, 5-21. https://doi.org/10.1016/S0888-613X(98)00014-0
  18. Grabot, B. and Geneste, L. (1994), Dispatching rules in scheduling : a fuzzy approach, International journal of production research, 32, 903-915. https://doi.org/10.1080/00207549408956978
  19. Greco, S., Matarazzo, B., and Slowinski, R. (2002), Rough set methodology for sorting problems in presence of multiple attributes and criteria, European Journal of Operational Research, 138, 247-259. https://doi.org/10.1016/S0377-2217(01)00244-2
  20. Harris, J. M. and Roesch, E. B. (2001), US Passive Component Supplier, UBS Warburg LLC, New York.
  21. Holthaus, O. (1999), Scheduling in job shops with machine breakdowns : an experimental study, Computers and Industrial Engineering, 36, 137-162. https://doi.org/10.1016/S0360-8352(99)00006-6
  22. Hwang, C. L. and Yoon, K. P. (1981), Multiple Attribute Decision Making : Methods and Applications, Springer-Verlag, New York.
  23. Jayamohan, M. S. and Rajendran, C. (2000), A comparative analysis of two different approaches to scheduling in flexible flow shops, Production Planning and Control, 11, 572-580. https://doi.org/10.1080/095372800414133
  24. Kim, S. J., Kim, K. S., and Jang, H. (2003), Optimization of manufacturing parameters for a brake lining using Taguchi method, Journal of Materrials Processing Technology, 136, 202-208. https://doi.org/10.1016/S0924-0136(03)00159-6
  25. Kim, G., Park, C. K., and Yoon, P. (1997), Identifying investment opportunities for advanced manufacturing system with comparative-integrated performance Measurement, International Journal of Production Economics, 50, 23-33. https://doi.org/10.1016/S0925-5273(97)00014-5
  26. Kwong, C. K. and Bai, H. (2003), Determining the importance weights for the customer requirements in QFD using fuzzy AHP with an extent analysis approach, IIE Transactions, 35, 619-626. https://doi.org/10.1080/07408170304355
  27. LaForge, R. L. and Barman, S. (1989), Performance of simple priority rule combinations in a flow dominant shop, Production and Inventory Management Journal, 30, 1-4.
  28. Petroni, A. and Rizzi, A. (2002), A fuzzy logic based methodology to rank shop floor dispatching rules, International Journal of Production Economics, 76, 99-108. https://doi.org/10.1016/S0925-5273(01)00171-2
  29. Phadke, M. S. (1989), Quality Engineering Using Robust Design, Prentice Hall, London.
  30. Pinedo, M. (1995), Scheduling : Theory, Algorithms and Systems, Prentice Hall, New Jersey.
  31. Rajendran, C. and Holthaus, O. (1999), A comparative study of dispatching rules in dynamic flowshops and jobshops, European Journal of Operation Research, 116, 156-170. https://doi.org/10.1016/S0377-2217(98)00023-X
  32. Saaty, T. L. (1980), The Analytic Hierarchy Process : Planning, Priority Setting, Resource Allocation, McGraw-Hill, New York.
  33. Saaty, T. L. and Vargas, L. G. (1987), Uncertainty and rank order in the Analytic Hierarchy Process, European Journal of Operation Research, 32, 107-117. https://doi.org/10.1016/0377-2217(87)90275-X
  34. Salvador, M. S. (1973), A solution to a special case of flow-shop scheduling problem, in : S. E. Elmaghraby (Ed.), Symposium of the Theory of Scheduling and Applications, Springer-Verlag, New York, 83-91.
  35. Santos, D. L., Hunsucker, J. L., and Deal, D. E. (1996), An evaluation of sequencing heuristics in flow shops with multiple processors, Computers and Industrial Engineering, 30, 681-692. https://doi.org/10.1016/0360-8352(95)00184-0
  36. Sarper, H. and Heny, M. C. (1996), Combinatorial evaluation of six dispatching rules in a dynamic two-machine flow shop, Omega, 24, 73-81. https://doi.org/10.1016/0305-0483(95)00049-6
  37. Song, Q. and Jamalipour, A. (2005), Network selection in an integrated wireless LAN and UMTS environment using mathematical modeling and computing techniques, IEEE Wireless Communications, 12, 42-48.
  38. Sugihara, K., Ishii, H., and Tanaka, H. (2004), Interval priorities in AHP by interval regression analysis, European Journal of Operation Research, 158, 745-754. https://doi.org/10.1016/S0377-2217(03)00418-1
  39. TORA version 2.0 provided by H. Taha (1997), Operations Research, sixth edition, Prentice Hall, New Jersey.
  40. Wild, R. H. and Pignatiello Jr., T. T. (1991), An experimental design strategy for designing robust systems using discrete-event simulation, Simulation, 57, 358-368. https://doi.org/10.1177/003754979105700603
  41. Yang, T. and Chou, P. (2005), Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method, Mathematics and Computers in Simulation, 68, 9-21. https://doi.org/10.1016/j.matcom.2004.09.004
  42. Yang, T., Chen, M. C., and Hung, C. C. (2007), Multiple attribute decision- making methods for the dynamic operator allocation problem, Mathematics and Computers in Simulation, 73, 285-299. https://doi.org/10.1016/j.matcom.2006.04.002
  43. Yang, T., Kuo, Y., and Chang, I. (2004), Tabu-search simulation optimization approach for flow-shop scheduling with multiple processors- a case study, International Journal of Production Research, 42, 4015-4030. https://doi.org/10.1080/00207540410001699381
  44. Yang, T., Kuo, Y., and Cho, C. (2007), A genetic algorithms simulation approach for the multi-criteria combinatorial dispatching decision problem, European Journal of Operation Research, 176, 1859-1873. https://doi.org/10.1016/j.ejor.2005.10.048
  45. Yang, T., Lee, R. S., and Hsieh, C. (2003), Solving a process engineer's manpower-planning problem using analytic hierarchy process, Production Planning and Control, 14, 266-272. https://doi.org/10.1080/0953728031000111496
  46. Yurdakul, M. and IC¸Y. T. (2005), Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches, International Journal of Production Research, 43, 4609-4641. https://doi.org/10.1080/00207540500161746