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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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    References listed on IDEAS

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    1. Amid, A. & Ghodsypour, S.H. & O'Brien, C., 2006. "Fuzzy multiobjective linear model for supplier selection in a supply chain," International Journal of Production Economics, Elsevier, vol. 104(2), pages 394-407, December.
    2. Lei Zhang & David Levinson, 2004. "An Agent-Based Approach to Travel Demand Modeling: An Exploratory Analysis," Working Papers 200405, University of Minnesota: Nexus Research Group.
    3. Sridharan, R., 1995. "The capacitated plant location problem," European Journal of Operational Research, Elsevier, vol. 87(2), pages 203-213, December.
    4. Kaihara, Toshiya, 2003. "Multi-agent based supply chain modelling with dynamic environment," International Journal of Production Economics, Elsevier, vol. 85(2), pages 263-269, August.
    5. Hadavandi, Esmaeil & Ghanbari, Arash & Shahanaghi, Kamran & Abbasian-Naghneh, Salman, 2011. "Tourist arrival forecasting by evolutionary fuzzy systems," Tourism Management, Elsevier, vol. 32(5), pages 1196-1203.
    6. Gebennini, Elisa & Gamberini, Rita & Manzini, Riccardo, 2009. "An integrated production-distribution model for the dynamic location and allocation problem with safety stock optimization," International Journal of Production Economics, Elsevier, vol. 122(1), pages 286-304, November.
    7. Roorda, Matthew J. & Cavalcante, Rinaldo & McCabe, Stephanie & Kwan, Helen, 2010. "A conceptual framework for agent-based modelling of logistics services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(1), pages 18-31, January.
    8. Chen, Chen-Tung & Lin, Ching-Torng & Huang, Sue-Fn, 2006. "A fuzzy approach for supplier evaluation and selection in supply chain management," International Journal of Production Economics, Elsevier, vol. 102(2), pages 289-301, August.
    9. Jonas Yu & Yu-Siang Lin & Kung-Jeng Wang, 2013. "Coordination-based inventory management for deteriorating items in a two-echelon supply chain with profit sharing," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(9), pages 1587-1601.
    10. Ruiz, Rubén & Maroto, Concepciøn & Alcaraz, Javier, 2006. "Two new robust genetic algorithms for the flowshop scheduling problem," Omega, Elsevier, vol. 34(5), pages 461-476, October.
    11. Nozick, Linda K. & Turnquist, Mark A., 2001. "Inventory, transportation, service quality and the location of distribution centers," European Journal of Operational Research, Elsevier, vol. 129(2), pages 362-371, March.
    12. Zuo-Jun Max Shen & Collette Coullard & Mark S. Daskin, 2003. "A Joint Location-Inventory Model," Transportation Science, INFORMS, vol. 37(1), pages 40-55, February.
    13. Sauvageau, Gabriel & Frayret, Jean-Marc, 2015. "Waste paper procurement optimization: An agent-based simulation approach," European Journal of Operational Research, Elsevier, vol. 242(3), pages 987-998.
    14. Cardona-Valdés, Y. & Álvarez, A. & Pacheco, J., 2014. "Metaheuristic procedure for a bi-objective supply chain design problem with uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 66-84.
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    2. Barenji, Ali Vatankhah & Wang, W.M. & Li, Zhi & Guerra-Zubiaga, David A., 2019. "Intelligent E-commerce logistics platform using hybrid agent based approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 15-31.

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