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Optimizing the Joint Replenishment and Channel Coordination Problem under Supply Chain Environment Using a Simple and Effective Differential Evolution Algorithm

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  • Lin Wang
  • Hui Qu
  • Shan Liu
  • Can Chen

Abstract

This paper presents a useful and practical procurement approach using the joint replenishment and channel coordination (JR-CC) policy in a two-echelon supply chain considering the coordination cost. The objective is to determine a basic replenishment cycle time and the replenishment interval to minimize the total cost of the supply chain. To solve this NP-hard problem, a simple and improved differential evolution algorithm (IDE) is developed. The performance of the IDE is verified by benchmark functions. Moreover, results of comparative numerical example show the effectiveness of the proposed IDE. IDE can be used as a good candidate for the JR-CC model. Results of numerical examples also indicate that the JR-CC policy can result in considerable cost saving, and enhance the efficiency of a supply chain. But not all members in the supply chain can benefit a lot using this policy. Moreover, results of sensitivity analysis show that retailers have more willingness to adopt the JR-CC policy than the manufacturers because of the different cost savings.

Suggested Citation

  • Lin Wang & Hui Qu & Shan Liu & Can Chen, 2014. "Optimizing the Joint Replenishment and Channel Coordination Problem under Supply Chain Environment Using a Simple and Effective Differential Evolution Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-12, June.
    • Handle: RePEc:hin:jnddns:709856
    DOI: 10.1155/2014/709856
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    Cited by:

    1. Seyed Hamid Reza Pasandideh & Seyed Taghi Akhavan Niaki & Reza Abdollahi, 2020. "Modeling and solving a bi-objective joint replenishment-location problem under incremental discount: MOHSA and NSGA-II," Operational Research, Springer, vol. 20(4), pages 2365-2396, December.
    2. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.

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