대한산업공학회지 (Journal of Korean Institute of Industrial Engineers)

  • 제40권5호
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  • Pages.481-491
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  • 2014
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  • 1225-0988(pISSN)
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  • 2234-6457(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
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예산과 공간 제약하에서 크랭크샤프트 생산라인의 생산능력 증대를 위한 시뮬레이션 기반의 연구

A Simulation Based Study on Increasing Production Capacity in a Crankshaft Line Considering Limited Budget and Space

  • 왕관 (창원대학교 산업시스템공학과) ;
  • 송수 (창원대학교 산업시스템공학과) ;
  • 신양우 (창원대학교 통계학과) ;
  • 문덕희 (창원대학교 산업시스템공학과)
  • Wang, Guan (Department of Industrial and Systems Engineering, Changwon National University) ;
  • Song, Shou (Department of Industrial and Systems Engineering, Changwon National University) ;
  • Shin, Yang Woo (Department of Statistics, Changwon National University) ;
  • Moon, Dug Hee (Department of Industrial and Systems Engineering, Changwon National University)
  • 투고 : 2014.03.21
  • 심사 : 2014.05.29
  • 발행 : 2014.10.15
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초록

In this paper, we discussed a problem for improving the throughput of a crankshaft manufacturing line in an automotive factory in which the budget for purchasing new machines and installing additional buffers is limited. We also considered the constraint of available space for both of machine and buffer. Although this problem seems like a kind of buffer allocation problem, it is different from buffer allocation problem because additional machines are also considered. Thus, it is not easy to calculate the throughput by mathematical model, and therefore simulation model was developed using $ARENA^{(R)}$ for estimating throughput. To determine the investment plan, a modified Arrow Assignment Rule under some constraints was suggested and it was applied to the real case.

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참고문헌

  1. Amiri, M. and Mohtashami, A. (2012), Buffer Allocation in Unreliable Production Lines Based on Design of Experiments, Simulation, and Genetic Algorithm. International Journal of Advanced Manufacturing Technology, 62(1-4), 371-383. https://doi.org/10.1007/s00170-011-3802-8
  2. Chan, F. T. S. and Ng, E. Y. H. (2002), Comparative Evaluations of Buffer Allocation Strategies in a Serial Production Line, International Journal of Advanced Manufacturing Technology, 19(11), 789-800. https://doi.org/10.1007/s001700200090
  3. Choi, S. D., Kumar, A. R., and Houshyar, A. (2002), A Simulation Study of an Automotive Foundry Plant Manufacturing Engine Blocks, Proceedings of the 2002 Winter Simulation Conference, 1035-1040.
  4. Dunbar, III, J. F., Liu, J. W. and Williams, E. D. (2009), Simulation of Alternatives for Transmission Plant Assembly Line, Proceedings of the Summer Computer Simulation Conference, 17-23.
  5. Huang, M. G., Chang, P. L., and Chou, Y. C. (2002), Buffer Allocation in Flow-shop-type Production System with General Arrival and Service Patterns, Computers and Operations Research, 29(2), 103-121. https://doi.org/10.1016/S0305-0548(00)00060-5
  6. Jayaraman, A. and Agarwal A. (1996), Simulating an Engine Plant, Manufacturing Engineering, 117(5), 60-68.
  7. Jayaraman, A. and Gunal, A. K. (1997), Applications of Discrete Event Simulation in the Design of Automotive Power Train Manufacturing Systems, Proceedings of the 1997 Winter Simulation Conference, 758-764.
  8. Kelton, W. D., Sadowski, R. P., and Sadowski, D. A. (2002), Simulation with ARENA 2nd Ed., McGraw Hill, New York, U.S.A.
  9. Kwon, C. M. and Lim, S. G. (2013), Bottleneck Detection Based on Duration of Active Periods, Journal of the Korea Society for Simulation, 22(3), 35-41. https://doi.org/10.9709/JKSS.2013.22.3.035
  10. Lawrence, S. R. and Buss, A. H. (1994), Shifting Production Bottlenecks : Cause, Curse and Conundrums, Journal of production and Operations Management, 3(1), 21-37.
  11. Li, J. and Meerkov, S. M. (2009), Production Systems Engineering, Springer Science+Business Media, New York, U.S.A.
  12. Li, L., Chang, Q., Xiao, G. X., and Ambani, S. (2011), Throughput Bottleneck Prediction of Manufacturing Systems Using Time Series Analysis, Journal of Manufacturing Science and Engineering, 133(2), DOI:10.1115/1.4003786.
  13. Moon, D. H., Sung, J. H., and Cho, H. I. (2003), A Case Study on the Verification of the Initial Layout of Engine Block Machining Line Using Simulation, Journal of Korea Society for Simulation, 12(3), 41-53.
  14. Moon, D. H., Wang, G., and Shin, Y. W. (2012), Effects of Failure Distribution Considering Various Types of Layout Structure in Automotive Engine Shops, Journal of the Korean Institute of Industrial Engineers, 38(1), 7-16. https://doi.org/10.7232/JKIIE.2012.38.1.007
  15. Powell, S. G. (1994), Buffer Allocation in Unbalanced Three-station Serial Lines, International Journal of Production Research, 32(9), 2201-2217.
  16. Seong, D., Chang, S. Y., and Hong, Y. (1995), The Buffer Allocation with Linear Resource Constraints in a Continuous Flow Line, Journal of the Korean Institute of Industrial Engineers, 21(4), 541-553.
  17. So, K. C. (1997), Optimal Buffer Allocation Strategy for Minimizing Work-in-process Inventory in Unpaced Production Lines, IIE Transactions, 29(1), 81-88. https://doi.org/10.1080/07408179708966314
  18. Gershwin, S. B. and Schor, J. E. (2000), Efficient Algorithms for Buffer Space Allocation, Analysis of Operational Research, 93(1-4), 117-144. https://doi.org/10.1023/A:1018988226612
  19. Ulgen, O., Gunal, A. Grajo, E., and Shore, J. (1994), The Role of Simulation in Design and Operation of Body and Paint Shops in Vehicle Assembly Plants, Proceedings of the European Simulation Symposium, 124-128.
  20. Xu, T., Moon, D. H., Shin, Y. W., and Jung, J. Y. (2010), An Effect Analysis of Layout Concepts on the Performances in Manufacturing Lines for Automotive Engine, Journal of the Korea Society for Simulation, 19(2), 107-118.
  21. Xu, T., Moon, D. H., and Baek, S. G. (2012), A Simulation Study Integrated with Analytic Hierarchy Process (AHP) in an Automotive Manufacturing System, Simulation, 88(4), 450-463. https://doi.org/10.1177/0037549711407781

피인용 문헌

  1. A Simulation Study on Improving Throughput in a Crankshaft Line Considering Minimizing Investment Cost under Throughput Target vol.5, pp.1, 2017, https://doi.org/10.18178/ijmmm.2017.5.1.284