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Location and transportation planning in supply chains under uncertainty and congestion by using an improved electromagnetism-like algorithm

Author

Listed:
  • Mohammad Fathian

    (Iran University of Science and Technology)

  • Javid Jouzdani

    (Iran University of Science and Technology)

  • Mehdi Heydari

    (Iran University of Science and Technology)

  • Ahmad Makui

    (Iran University of Science and Technology)

Abstract

Supply chain decision makers are constantly trying to improve the customer demand fulfillment process and reduce the associated costs via decision making models and techniques. As two of the most important parameters in a supply chain, supply and demand quantities are subject to uncertainty in many real-world situations. In addition, in recent decades, there is a trend to think of the impacts of supply chain design and strategies on society and environment. Especially, transportation of goods not only imposes costs to businesses but also has socioeconomic influences. In this paper, a fuzzy nonlinear programming model for supply chain design and planning under supply/demand uncertainty and traffic congestion is proposed and a hybrid meta-heuristic algorithm, based on electromagnetism-like algorithm and simulated annealing concepts, is designed to solve the model. The merit of this paper is presenting a realistic model of current issues in supply chain design and an efficient solution method to the problem. These are significant findings of this research which can be interesting to both researchers and practitioners. Several numerical examples are provided to justify the model and the proposed solution approach.

Suggested Citation

  • Mohammad Fathian & Javid Jouzdani & Mehdi Heydari & Ahmad Makui, 2018. "Location and transportation planning in supply chains under uncertainty and congestion by using an improved electromagnetism-like algorithm," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1447-1464, October.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:7:d:10.1007_s10845-015-1191-9
    DOI: 10.1007/s10845-015-1191-9
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    References listed on IDEAS

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    Cited by:

    1. Germán González Rodríguez & Jose M. Gonzalez-Cava & Juan Albino Méndez Pérez, 2020. "An intelligent decision support system for production planning based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1257-1273, June.
    2. Mingqiang Yin & Min Huang & Xiaohu Qian & Dazhi Wang & Xingwei Wang & Loo Hay Lee, 2023. "Fourth-party logistics network design with service time constraint under stochastic demand," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1203-1227, March.

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