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

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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.

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Notes

  1. Decision maker.

  2. Multi-agent system.

  3. Fuzzy multi-agent system.

  4. Small and medium-sized enterprises.

  5. Decision support system.

  6. Chain environment survey.

  7. Computer-human interaction center.

  8. Relative deviation index.

  9. Signal-to-noise.

  10. Membership functions.

  11. Center of gravity.

  12. Distribution center.

  13. Foundation for intelligent physical agents.

  14. Plan-commit-execute.

  15. Entity relationship diagram.

  16. Balanced supply chain network.

  17. Ordered weighting averaging.

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Acknowledgments

The authors express their gratitude to the editor and anonymous reviewers for their time and also for the constructive and valuable comments on the prior version of the paper. Taking care of the comments significantly improved the representation.

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Authors and Affiliations

  1. Department of Industrial Engineering, Computational Intelligent Systems Laboratory, Amirkabir University of Technology, 424 Hafez Ave, P.O. Box 15875-4413, Tehran, Iran

    Mosahar Tarimoradi & M. H. Fazel Zarandi

  2. Knowledge Intelligent Systems Laboratory, University of Toronto, Toronto, Canada

    M. H. Fazel Zarandi & I. B. Turksan

  3. Group for Research in Decision Analysis (GERAD), HEC Montréal, Montreal, Canada

    Hosain Zaman

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  1. Mosahar Tarimoradi
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Tarimoradi, M., Zarandi, M.H.F., Zaman, H. et al. Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art. J Intell Manuf 28, 1551–1579 (2017). https://doi.org/10.1007/s10845-015-1170-1

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