Abstract
This paper considers a two-stage, multi-item inventory system with stochastic demand. First we propose two types of exact stochastic optimization models to minimize the long-run average system cost under installation and echelon (r, nQ) policy. Second we provide an effective hybrid genetic algorithm (HGA) based on the property of the optimization problem. In the proposed HGA, a heuristic search technique, based on the tradeoff between inventory cost and setup cost, is introduced. The long-run average cost of each solution in the model is estimated by Monte Carlo method. At last, computation tests indicate that when variance of stochastic demand increases, echelon policy outperforms installation policy and the proposed heuristic search technique greatly enhances the search capacity of HGA.
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References
Clark, A.J., Scarf, H.: Optimal Policies for a Multi-echelon Inventory Problem. Management Science 50(12), 1782–1790 (2004)
Bsssler, S.A., Veinott, A.F.: Optimal Policy for a Dynamic Multi-echelon Inventory Model. Naval Research Logistic Quarterly 13(4), 355–399 (2006)
Axsater, S., Rosling, K.: Installation vs. Echelon Stock Policies for Multi-level Inventory Control. Management Science 39, 1274–1280 (1993)
Zipkin, P.H.: Foundations of Inventory Management. McGraw-Hill, New York (2000)
Giimiis, A.T., Guneri, A.F.: Multi-echelon Inventory Management in Supply Chains with Uncertain Demand and Lead Times: Literature Review from an Operational Research Perspective. In: Proceedings of the Institution of Mechanical Engineers, Part B, pp. 1553–1570. Professional Engineering, Publishing, London (2007)
Axsater, S., Zhang, W.-F.: A Joint Replenishment Policy for Multi-echelon Inventory Control. Int. J. Prod. Econ. 59, 243–250 (1999)
Mohebbi, E., Posner, M.: Continuous-review Inventory System with Lost Sales and Variable Leadtime. Naval Research Logistic 45(3), 259–278 (1998)
Chiang, W., Monahan, G.E.: Managing Inventories in a Two-echelon Dual-channel Supply Chain. European Journal of Operational Research 162(2), 325–341 (2005)
Van Slyke, R.M.: Roger Wets.: L-Shaped Linear Programs with Application to Optimal Control and Stochastic Programming. Journal on Applied Mathematics 17(4), 638–663 (1969)
Pan, Z., Kang, L., Chen, Y.: Evolutionary Computation. Tsinghua University Press, Beijing (1998)
Moon, I.K., Cha, B.C., Bae, H.C.: Hybrid Genetic Algorithm for Group Technology Economic Lot Scheduling Problem. International Journal of Production Research 44(21), 4551–4568 (2006)
Axsater, S.: Inventory Control. Kluwer Academic Publishers, Boston (2000)
Bertsekas, D.P.: Nonlinear Programming. Athena Scientific, Belmont (1999)
Zhang, Y., Song, S., Wu, C., Yin, W.: Multi-echelon Inventory Management with Uncertain Demand via Improved Real-Coded Genetic Algorithm. In: Proceedings of the International Symposium on Intelligent Information Systems and Applications, pp. 231–236. Academy Publisher, Oulu (2009)
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Zhang, Y., Song, S., Wu, C., Yin, W. (2010). Stochastic Optimization of Two-stage Multi-item Inventory System with Hybrid Genetic Algorithm. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15597-0_53
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DOI: https://doi.org/10.1007/978-3-642-15597-0_53
Publisher Name: Springer, Berlin, Heidelberg
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