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Neural Computing and Applications

Erschienen in:

01.09.2012 | LSMS2010 and ICSEE 2010

A hybrid genetic algorithm for two-stage multi-item inventory system with stochastic demand

verfasst von: Yuli Zhang, Shiji Song, Heming Zhang, Cheng Wu, Wenjun Yin

Erschienen in: Neural Computing and Applications | Ausgabe 6/2012

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Abstract

We study a two-stage, multi-item inventory system where stochastic demand occurs at stage 1, and nodes at stage 1 replenish their inventory from stage 2. Due to the complexity of stochastic inventory optimization in multi-echelon system, few analytical models and effective algorithms exist. In this paper, we establish exact stochastic optimization models by proposing a well-defined supply–demand process analysis and provide an efficient hybrid genetic algorithm (HGA) by introducing a heuristic search technique based on the tradeoff between the inventory cost and setup cost and improving the initial solution. Monte Carlo method is also introduced to simulate the actual demand and thus to approximate the long-run average cost. By numerical experiments, we compare the widely used installation policy and echelon policy and show that when variance of stochastic demand increase, echelon policy outperforms installation policy and, furthermore, the proposed heuristic search technique greatly enhances the search capacity of HGA.

Vorheriger Artikel Intelligent computing and applications (LSMS and ICSEE 2010)

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Titel: A hybrid genetic algorithm for two-stage multi-item inventory system with stochastic demand
verfasst von: Yuli Zhang
Shiji Song
Heming Zhang
Cheng Wu
Wenjun Yin
Publikationsdatum: 01.09.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 6/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0658-7

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