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Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art

Author

Listed:
  • Mosahar Tarimoradi

    (Amirkabir University of Technology)

  • M. H. Fazel Zarandi

    (Amirkabir University of Technology
    University of Toronto)

  • Hosain Zaman

    (HEC Montréal)

  • I. B. Turksan

    (University of Toronto)

Abstract

Supply chain network designing and programming is a momentous issue that many practitioners have focused on and contributed numerous novelties for this prompt. This paper puts forward a fuzzy multi-agent system according to which compatible with the decision makers’ interests and environmental survey, identifies the parameters of the mathematical model. An embedded optimization party including evolutionary-based optimizer intelligent agents, obtains non-dominated potential solutions. The output of these optimizer agents during the calibration process is an underpinning for evaluating the performance of the party. The system makes the policy of optimization complying with the results evaluation as well as the decision makers’ elaborated desires. Afterwards, in step with this policy, it sets a pool from obtained Pareto Fronts and aggregates them to extract a set of the best individuals. It interactively represents this set to the decision makers and catches their desired circumstance amongst these optional solutions. Proposing the network graph and program—which its generic morphography is determined—for decision makers is contrived as the system last stage. The main competencies of this system could be contemplated regarding the facts that it interactively fulfills the decision makers’ utilities relying on its robustness in optimization, self-tuning, training loop, ambient intelligence and consciousness toward the changes in environment.

Suggested Citation

  • Mosahar Tarimoradi & M. H. Fazel Zarandi & Hosain Zaman & I. B. Turksan, 2017. "Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art," Journal of Intelligent Manufacturing, Springer, vol. 28(7), pages 1551-1579, October.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:7:d:10.1007_s10845-015-1170-1
    DOI: 10.1007/s10845-015-1170-1
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    2. Asrat Mekonnen Gobachew & Hans-Dietrich Haasis, 2023. "Scenario-Based Optimization of Supply Chain Performance under Demand Uncertainty," Sustainability, MDPI, vol. 15(13), pages 1-32, July.

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