Journal of the Korean Operations Research and Management Science Society (한국경영과학회지)

The Korean Operations Research and Management Science Society (한국경영과학회)
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An Adaptive Vendor Managed Inventory Model Using Action-Reward Learning Method

행동-보상 학습 기법을 이용한 적응형 VMI 모형

  • 김창욱 (연세대학교 정보산업공학과) ;
  • 백준걸 (인덕대학 산업시스템경영과) ;
  • 최진성 (연세대학교 정보산업공학과) ;
  • 권익현 (고려대학교 산업시스템정보공학과)
  • Published : 2006.09.01
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Abstract

Today's customer demands in supply chains tend to change quickly, variously even in a short time Interval. The uncertainties of customer demands make it difficult for supply chains to achieve efficient inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. Un this paper, we propose an adaptive vendor managed inventory (VMI) model for a two-echelon supply chain with non-stationary customer demands using the action-reward learning method. The Purpose of this model is to decrease the inventory cost adaptively. The control Parameter, a compensation factor, is designed to adaptively change as customer demand pattern changes. A simulation-based experiment was performed to compare the performance of the adaptive VMI model.

Keywords

References

  1. Achabal, D.D., S.H. Mcintyre, S.A. Smith, and K. Kalyanam, 'A Decision Support System for Vendor Managed Inventory,' Journal of Retailing, Vol.76, No,4(2000), pp,430-454 https://doi.org/10.1016/S0022-4359(00)00037-3
  2. Andersson, J., S. Axsater, and J. Marklund, 'Decentralized Multi-echelon Inventory Control,' Production and Operations Management, Vol.7, No,4(l998), pp.370-386 https://doi.org/10.1111/j.1937-5956.1998.tb00130.x
  3. Axsater, S., 'A framework for decentralized multi-echelon inventory control,' IIE Transactions,' Vol.33, No.2(2001), pp.91-97
  4. Disney, S.M. and D.R.Towill, 'The effect of vendor managed inventory (VMD dynamics on the Bullwhip Effect in supply chains,' Iruerratonal Journal of Production Economics, Vol.85, No.2(2003), pp.l99-216 https://doi.org/10.1016/S0925-5273(03)00110-5
  5. Gavimeni, S. and S. Tayur, 'An efficient procedure for non-stationary inventory control,' IIE Transactions, Vol.33(2000), pp.83-89
  6. Graves, S.C., 'A Single-Item Inventory Model for a Non-Stationary Demand Process,' Manufacturing and Service Operations Management, Vol.1, No.1(1999), pp.50-61 https://doi.org/10.1287/msom.1.1.50
  7. Kaipia, R, J. Holmstrom, K. Tanskanen, 'VMl : What are you losing if you let your customer place orders?,' Production Planning and Control, Vol 13, No.1(2002), pp.17-25 https://doi.org/10.1080/09537280110061539
  8. Lee, H.L., V. Padmanabhan, and S. Whang, 'Information Distortion in Supply Chain: The Bullwhip Effect,' Management Science, Vol. 43, No,4(1997), pp.546-558 https://doi.org/10.1287/mnsc.43.4.546
  9. Lee, H.L., K.C. So, and C.S. Tang, 'The Value of Information Sharing in a Two-Level Supply Chain,' Management Science, Vol.46, No.5 (2000), pp.626-643 https://doi.org/10.1287/mnsc.46.5.626.12047
  10. Mitchell, T.M, Machine Learning, McGraw-Hill, 1997
  11. Moinzadeh, K, 'A Multi-Echelon Inventory System with Information Exchange,' Management Science, Vol.48, No.3(2002), pp.414-426 https://doi.org/10.1287/mnsc.48.3.414.7730
  12. Nahmias, S., Production and Operations Analysis, McGraw-Hill, 2000
  13. Patel, N.S. and S.T. Jenkins, 'Adaptive Optimization of Run-To-Run Controllers: The EWMA Example, IEEE Transactions on Semiconductor Engineering, Vol.13, No.1(2000), pp.97-107 https://doi.org/10.1109/66.827349
  14. Simchi-Levi, D, P. Kaminsky, and E. SimchiLevi, Designing and Managing the Supply Chain. McGraw-Hill, 2000
  15. Sutton, R.S. and A.G. Barto, Reinforcement Learning, MIT Press, 1998
  16. Trigg, D.W. and A.G. Leach, 'Exponential smoothing with an adaptive response rate,' Operational Research Quarterly, Vol.18, No.1 (1967), pp.53-59 https://doi.org/10.1057/jors.1967.5