Neural Computing and Applications

, Volume 21, Issue 6, pp 1087–1098 | Cite as

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

  • Yuli Zhang
  • Shiji SongEmail author
  • Heming Zhang
  • Cheng Wu
  • Wenjun Yin
LSMS2010 and ICSEE 2010


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.


Multi-echelon inventory Stochastic demand Heuristic search Hybrid genetic algorithm Monte Carlo method 



The authors thank the associate editor and the two anonymous referees for their constructive comments. The paper is supported by NSFC (No. 60874071, 60834004), Project of China Ocean Association (No. DYXM-115-03-3-01), RFDP (No. 20090002110035), Independent Research Project at Tsinghua University (No. 2010THZ07002), and Distinguished Visiting Research Fellow Award of Royal Academy of Engineering of UK, UK–China Bridge in Sustainable Energy and Built Environment (EP/G042594/1), and Foundation for Academic Communication of TNList.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Yuli Zhang
    • 1
  • Shiji Song
    • 1
    Email author
  • Heming Zhang
    • 1
  • Cheng Wu
    • 1
  • Wenjun Yin
    • 2
  1. 1.Department of Automation, TNListTsinghua UniversityBeijingChina
  2. 2.IBM China Research LabBeijingChina

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