Abstract
Based on the assumption that distributor and retailers adopt different replenishment policies, this article gives consideration to the relationship between the inventory strategies of the two echelon. The authors construct an inventory model of a two-echelon distribution system under stochastic demand, and solve the problem based on algorithm of Particle Swarm Optimization (PSO) to quantitatively get the optimal ordering strategies of distribution center and retailers.
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Federgruen, A., Zipkin, P.H.: Approximations of dynamic multi-location production and inventory problems. Management Science 30, 69–84 (1984)
Axsäter, S.: A simple decision rule for decentralized two-echelon inventory control. International Journal of Production Economics 93-94, 53–59 (2005)
Olsson, R.J., Hill, R.M.: A two-echelon base stock inventory model with Poisson demand and the sequential processing of orders at the upper echelon. European Journal of Operational Research 177, 310–324 (2007)
Thangam, A., Uthayakumar, R.: A two-level distribution inventory system with stochastic lead time at the lower echelon. The International Journal of Advanced Manufacturing Technology 41, 1208–1220 (2009)
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Han, J., Yu, M., Liu, L. (2014). Research into Inventory Optimization of a Two-Echelon Distribution System Based on Particle Swarm Optimization (PSO). In: Liu, K., Gulliver, S.R., Li, W., Yu, C. (eds) Service Science and Knowledge Innovation. ICISO 2014. IFIP Advances in Information and Communication Technology, vol 426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55355-4_45
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DOI: https://doi.org/10.1007/978-3-642-55355-4_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-55354-7
Online ISBN: 978-3-642-55355-4
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