대한산업공학회지 (Journal of Korean Institute of Industrial Engineers)

  • 제31권2호
  • /
  • Pages.106-119
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  • 2005
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  • 1225-0988(pISSN)
  • /
  • 2234-6457(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
권호 표지

비안정적인 고객수요를 갖는 공급사슬에서의 적응형 재고관리 모델

Adaptive Inventory Control Models in a Supply Chain with Nonstationary Customer Demand

  • 백준걸 (인덕대학 산업시스템경영과) ;
  • 김창욱 (연세대학교 정보산업공학과) ;
  • 전진 (고려대학교 정보통신기술공동연구소)
  • Baek, Jun-Geol (Department of Industrial System Engineering, Induk Institute of Technology) ;
  • Kim, Chang Ouk (Department of Information and Industrial Engineering, Yonsei University) ;
  • Jun, Jin (Research Institute for Information and Communication Technology, Korea University)
  • 발행 : 2005.06.30
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초록

Uncertainties inherent in customer demand patterns make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. In this paper, we propose two intelligent adaptive inventory control models for a supply chain consisting of one supplier and multiple retailers, with the assumption of information sharing. The inventory control parameters of the supplier and retailers are order placement time to an outside source and reorder points in terms of inventory position, respectively. Unlike most extant inventory control approaches, modeling the uncertainty of customer demand as a stationary statistical distribution is not necessary in these models. Instead, using a reinforcement learning technique, the control parameters are designed to adaptively change as customer demand patterns change. A simulation based experiment was performed to compare the performance of the inventory control models.

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참고문헌

  1. Andersson, J., Axsater, S., and Marklund, J. (1998),Decentralized multi-echelon inventory control, Production and Operations Management, 7(4), 370-386 https://doi.org/10.1111/j.1937-5956.1998.tb00130.x
  2. Axsater, S. (2001), A framework for decentralized multi-echelon inventorycontrol, lIE Transactions, 33(2), 91-97
  3. Azoury, K. (1985), Bayes solution to dynamic inventory models under unknown demand distribution, Management Science 31(9),1150-1160 https://doi.org/10.1287/mnsc.31.9.1150
  4. Brown, R.G. (1962), Smoothing, Forecasting, and Prediction of Discrete Time Series, Prentice-Hall
  5. Cachon, G. and Fisher, M. (2000), Supply chain inventory management andthge value of shared information, Management Science, 46(8), 1032-1048 https://doi.org/10.1287/mnsc.46.8.1032.12029
  6. Cachon, G. and Zipkin,P. H. (1999), Competitive and cooperative inventory policies in a two stage supply chain, Management Science, 45(7), 936-953 https://doi.org/10.1287/mnsc.45.7.936
  7. Corbett,L. M. (1992), Deliverywindows a new view on improving manufacturing flexibility and on-time delivery performance, Production andInventory Management, 33(3), 74-79
  8. Gavimeni, S., Kapuscinski, R., and Tayur, S. (1999), Value of information in capacitated supplychains, Management Science, 45(1), 16-24 https://doi.org/10.1287/mnsc.45.1.16
  9. Lee, H. and Whang, S. (1999), Decentralized multi-echelon supply chains: incentives and information, Management Science, 45(5),633-640 https://doi.org/10.1287/mnsc.45.5.633
  10. Lee, H. L., So, K. C., and Tang, C. S. (2000), The value of information sharing in a two-level supply chain, Management Science, 46(5),626-643 https://doi.org/10.1287/mnsc.46.5.626.12047
  11. Miller, B.L. (1986), Scarf's state reductionmethod, flexibility, and a dependent demand inventory model, Operations Research, 34(1), 83- 90 https://doi.org/10.1287/opre.34.1.83
  12. Moinzadeh, K. (2002), A multi-echelon inventory system with information exchange, Management Science, 48(3), 414-426 https://doi.org/10.1287/mnsc.48.3.414.7730
  13. Packer, A. H. (1967), Simulation and adaptive forecasting as applied to inventory control, Operations Research, 15, 660-679 https://doi.org/10.1287/opre.15.4.660
  14. Scarf,H. (1959), Someremarkson Bayes solutionto the inventory problem, Naval Research Logistics Quarterly, 7, 591-596 https://doi.org/10.1002/nav.3800070428
  15. Souza, R. D., Zice, S., and Chaoyang, L. (2000), Supply chain dynamics and optimization, Integrated Manufacturing Systems, 11(5),348-364 https://doi.org/10.1108/09576060010335627
  16. Sutton, R. S. and Barto, A. G. (1998), Reinforcement Learning, MIT Press
  17. Trigg,D.W. and Leach, A.G. (1967), Exponential smoothing with an adaptive response rate, Operational Research Quarterly, 18(1),53-59 https://doi.org/10.1057/jors.1967.5
  18. Waller, M., Johnson, M. E., and Davis, T. (1999), Vendormanaged inventory in the retail supply chain, Journal of Business Logistics, 20(1), 183-203
  19. Zhao, X. and Xie, J. (2002), Forecasting errors and the value of information sharing in a supply chain, International Journal of Production Research, 40(2), 311-335 https://doi.org/10.1080/00207540110079121
  20. Zhao, X., Xie, J., and Lau, R. S. M. (2001), Improving the supply chain performance: use of forecasting models versus early order commitments, International Journal of Production Research, 39(17), 3923-3939 https://doi.org/10.1080/00207540110072236