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Differential evolution to solve the lot size problem in stochastic supply chain management systems

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  • Kris Lieckens

    (KU Leuven)

  • Nico Vandaele

    (KU Leuven
    KU Leuven Campus Kortrijk)

Abstract

An advanced resource planning model is presented to support optimal lot size decisions for overall performance improvement of real-life supply chain management systems in terms of either total delivery time or total setup costs. Based on a queueing network, a model is developed for a mix of products, which follow a sequence of operations taking place at multiple interdependent supply chain members. At the same time, various sources of uncertainty, both in demand and process characteristics, are taken into account. In addition, the model includes the impact of parallel servers for multiple resources with period dependent time schedules. The corrupting influence of variabilities from rework and breakdown is also explicitly modeled. This integer non-linear problem is solved by standard differential evolution algorithms. They are able to find each product’s lot size that minimizes its total supply chain lead time. We show that this solution approach outperforms the steepest descent method, an approach commonly used in the search for optimal lot sizes. For problems of realistic size, we propose appropriate control parameters for an efficient differential evolutionary search process. Based on these results, we add a major conclusion on the debate concerning the convexity between lot size and lead time in a complex supply chain environment.

Suggested Citation

  • Kris Lieckens & Nico Vandaele, 2016. "Differential evolution to solve the lot size problem in stochastic supply chain management systems," Annals of Operations Research, Springer, vol. 242(2), pages 239-263, July.
    • Handle: RePEc:spr:annopr:v:242:y:2016:i:2:d:10.1007_s10479-014-1778-0
    DOI: 10.1007/s10479-014-1778-0
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    References listed on IDEAS

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    Cited by:

    1. Kannan Govindan, 2016. "Evolutionary algorithms for supply chain management," Annals of Operations Research, Springer, vol. 242(2), pages 195-206, July.
    2. Suradej Duangpummet & Jessada Karnjana & Waree Kongprawechnon, 2021. "State-of-charge estimation based on theory of evidence and interval analysis with differential evolution optimization," Annals of Operations Research, Springer, vol. 300(2), pages 399-414, May.
    3. Rabin K. Jana & Dinesh K. Sharma & Peeyush Mehta, 2022. "A probabilistic fuzzy goal programming model for managing the supply of emergency relief materials," Annals of Operations Research, Springer, vol. 319(1), pages 149-172, December.
    4. Zhou, Shenghan & Zhou, Yuliang & Zuo, Xiaorong & Xiao, Yiyong & Cheng, Yang, 2018. "Modeling and solving the constrained multi-items lot-sizing problem with time-varying setup cost," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 202-207.

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