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Evolutionary Inventory Control for Multi-Echelon Systems

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Intelligent Computational Optimization in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 366))

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Abstract

The purpose of this chapter is to present the use of Genetic Algorithm (GA) for solving multi-echelon inventory problems. The literature of GA dealing with inventory control problems is briefly reviewed with particular focus on multi-echelon systems. A novel GA based solution algorithm is introduced for effective management of a stochastic inventory system across a distribution network under centralized control. To demonstrate the performance of proposed GA structure, several test cases with different operational parameters are studied and experimented. The percentage differences between the total cost obtained by GA and the lower bounds and simulation results are used as performance indicators. Findings of the experiments show that the proposed GA approach can be very useful for obtaining feasible and satisfying solutions for the centralized inventory distribution problem.

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Author information

Authors and Affiliations

  1. Istanbul Technical University, 34367, Maçka, Istanbul

    Dilay Çelebi

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  1. Dilay Çelebi
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Editors and Affiliations

  1. Dept. Artificial Intelligence, Kyushu Institute of Technology, 680-4 Kawazu, 820-8502, Izuka-Shi, Fukuoka, Japan

    Mario Köppen

  2. School of Engineering and Applied Science, Aston University, Aston Triangle, B4 7ET, Birmingham, U.K.

    Gerald Schaefer

  3. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

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Çelebi, D. (2011). Evolutionary Inventory Control for Multi-Echelon Systems. In: Köppen, M., Schaefer, G., Abraham, A. (eds) Intelligent Computational Optimization in Engineering. Studies in Computational Intelligence, vol 366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21705-0_12

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  • DOI: https://doi.org/10.1007/978-3-642-21705-0_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21704-3

  • Online ISBN: 978-3-642-21705-0

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