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Genetic algorithm for inventory positioning problem with general acyclic supply chain networks

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Listed:
  • Dali Jiang
  • Haitao Li
  • Tinghong Yang
  • De Li

Abstract

Inventory positioning, also known as safety stock placement, in supply chain networks is an important optimisation problem that has various applications in supply chain design and configuration. In this paper, we develop a new genetic algorithm (GA) for this NP-hard problem. Our new GA features custom designed procedure to generate feasible individuals by exploiting the problem structure. It also implements a multi-start strategy to enhance solution quality. Computational results show that our GA is able to offer near optimal solutions in reasonable computational time. [Received 4 October 2014; Revised 19 October 2015; Revised 14 January 2016; Accepted 18 January 2016]

Suggested Citation

  • Dali Jiang & Haitao Li & Tinghong Yang & De Li, 2016. "Genetic algorithm for inventory positioning problem with general acyclic supply chain networks," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 10(3), pages 367-384.
    • Handle: RePEc:ids:eujine:v:10:y:2016:i:3:p:367-384
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    References listed on IDEAS

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    1. Stephen C. Graves & Sean P. Willems, 2000. "Optimizing Strategic Safety Stock Placement in Supply Chains," Manufacturing & Service Operations Management, INFORMS, vol. 2(1), pages 68-83, June.
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    4. Chung, Ji-Won & Oh, Seog-Moon & Choi, In-Chan, 2009. "A hybrid genetic algorithm for train sequencing in the Korean railway," Omega, Elsevier, vol. 37(3), pages 555-565, June.
    5. Zhou, Gengui & Gen, Mitsuo, 1999. "Genetic algorithm approach on multi-criteria minimum spanning tree problem," European Journal of Operational Research, Elsevier, vol. 114(1), pages 141-152, April.
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