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Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks

Author

Listed:
  • M. H. Alavidoost

    (Amirkabir University of Technology)

  • Mosahar Tarimoradi

    (Amirkabir University of Technology)

  • M. H. Fazel Zarandi

    (Amirkabir University of Technology
    University of Toronto)

Abstract

The competitive market and declined economy have increased the relevant importance of making supply chain network efficient. Up to now, this has resulted in great motivations to reduce the cost of services, and simultaneously, to improve their quality. A mere network model, as a tri-echelon, consists of Suppliers, Warehouses or Distribution Centers (DCs), and Retailer nodes. To bring it closer to reality, the majority of parameters in this network involve retailer demands, lead-time, warehouses holding and shipment costs, and also suppliers procuring and stocking costs which are all assumed to be stochastic. The aim is to determine the optimum service level so that total cost is minimized. Obtaining such conditions requires determining which supplier nodes, and which DC nodes in network should be active to satisfy the retailers’ needs, an issue which is a network optimization problem per se. The proposed supply chain network for this paper is formulated as a mixed-integer nonlinear programming, and to solve this complicated problem, since the literature for the related benchmark is poor, three numbers of genetic algorithm called Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Ranking Genetic Algorithm (NRGA), and Pareto Envelope-based Selection Algorithm (PESA-II) are applied and compared to validate the obtained results. The Taguchi method is also utilized for calibrating and controlling the parameters of the applied triple algorithms.

Suggested Citation

  • M. H. Alavidoost & Mosahar Tarimoradi & M. H. Fazel Zarandi, 2018. "Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 809-826, April.
  • Handle: RePEc:spr:joinma:v:29:y:2018:i:4:d:10.1007_s10845-015-1130-9
    DOI: 10.1007/s10845-015-1130-9
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    References listed on IDEAS

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    2. Majid Eskandarpour & Pierre Dejax & Olivier Péton, 2019. "Multi-Directional Local Search for Sustainable Supply Chain Network Design," Post-Print hal-02407741, HAL.
    3. Khodakaram Salimifard & Sara Bigharaz, 2022. "The multicommodity network flow problem: state of the art classification, applications, and solution methods," Operational Research, Springer, vol. 22(1), pages 1-47, March.
    4. Lienkamp, Benedikt & Schiffer, Maximilian, 2024. "Column generation for solving large scale multi-commodity flow problems for passenger transportation," European Journal of Operational Research, Elsevier, vol. 314(2), pages 703-717.
    5. Mustapha Anwar Brahami & Mohammed Dahane & Mehdi Souier & M’hammed Sahnoun, 2022. "Sustainable capacitated facility location/network design problem: a Non-dominated Sorting Genetic Algorithm based multiobjective approach," Annals of Operations Research, Springer, vol. 311(2), pages 821-852, April.

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