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Comparing performance of genetic and discrete invasive weed optimization algorithms for solving the inventory routing problem with an incremental delivery

Author

Listed:
  • Hadi Jahangir

    (Kharazmi University)

  • Mohammad Mohammadi

    (Kharazmi University)

  • Seyed Hamid Reza Pasandideh

    (Kharazmi University)

  • Neda Zendehdel Nobari

    (Kharazmi University)

Abstract

This article formulates an inventory routing problem in which backorders are allowed, each vehicle is used at most once, a central depot distributes a single product to a set of customers with an incremental delivery, each customer is served at most once over a finite planning period and the objective is minimizing transportation and inventory costs. Since the proposed model is an Np-Hard problem, for solving large scale problems two meta-heuristics, discrete invasive weed optimization and Genetic algorithm are presented. Tuning the parameters of the algorithms are performed by regression approach. In this approach, equation of fitness is found in terms of the parameters and the best value of the parameters is found in a way that the equation is minimized. Performance of the algorithms for solving the IRP is compared with statistical and multi-attribute decision making approach in terms of computational time and quality of solutions.

Suggested Citation

  • Hadi Jahangir & Mohammad Mohammadi & Seyed Hamid Reza Pasandideh & Neda Zendehdel Nobari, 2019. "Comparing performance of genetic and discrete invasive weed optimization algorithms for solving the inventory routing problem with an incremental delivery," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2327-2353, August.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:6:d:10.1007_s10845-018-1393-z
    DOI: 10.1007/s10845-018-1393-z
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    Cited by:

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    2. Sasan Harifi & Madjid Khalilian & Javad Mohammadzadeh & Sadoullah Ebrahimnejad, 2021. "Optimization in solving inventory control problem using nature inspired Emperor Penguins Colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1361-1375, June.
    3. Dezhi Zhang & Shuxin Yang & Shuangyan Li & Jiajun Fan & Bin Ji, 2020. "Integrated Optimization of the Location–Inventory Problem of Maintenance Component Distribution for High-Speed Railway Operations," Sustainability, MDPI, vol. 12(13), pages 1-25, July.
    4. Wenjie Wang & Guangdong Tian & Gang Yuan & Duc Truong Pham, 2023. "Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1065-1083, March.

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