A hybrid genetic algorithm for two-stage multi-item inventory system with stochastic demand
- 375 Downloads
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.
KeywordsMulti-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.
- 1.Giimiis AT, Guneri AF (2007) 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 J Eng Manuf. Professional Engineering, Publishing, London, pp 1553–1570Google Scholar
- 5.Zipkin PH (2000) Foundations of inventory management. McGraw-Hill, BostonGoogle Scholar
- 13.Gumus AT, Guneri A (2009) A multi-echelon inventory management framework for stochastic and fuzzy supply chains. Exp Syst Appl 36:5575Google Scholar
- 16.Kevin HS, Sean XZ (2009) Optimal and heuristic echelon (r, nQ, T) policies in serial inventory systems with fixed Costs. Oper Res, in advance, pp 1–14Google Scholar
- 19.Yuli Z, Shiji S, Cheng W, Wenjun Y (2010) Stochastic optimization of two-stage multi-item inventory system with hybrid genetic algorithm. LSMS/ICSEE 2010 Part II, LNCS 6329. Springer-Verlag, Berlin Heidelberg, pp 484–492Google Scholar
- 21.Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
- 22.Zhengjun P, Lishan K, Yuping C (1998) Evolutionary computation. Tsinghua University Press, BeijingGoogle Scholar
- 26.Yuli Z, Shiji S, Cheng W, Wenjun Y (2009) 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. Academy Publisher, Oulu, pp 231–236Google Scholar
- 27.Sven A (2000) Inventory control. Kluwer Academic Publishers, BostonGoogle Scholar
- 28.Baoding L, Ruiqing Z (1998) Stochastic programming and fuzzy programming. Tsinghua University Press, BeijingGoogle Scholar