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A robust bi-objective programming approach to environmental closed-loop supply chain network design under uncertainty

Author

Listed:
  • Zahra Homayouni
  • Mir Saman Pishvaee

Abstract

Imposition of strict environmental protection acts and the imperative need of the best possible allocation of resources have given birth to the concept of low carbon logistics. Environmental laws force the manufacturers to extend their existing supply chains to form a closed-loop supply chain (CLSC) through the setup of an efficient recovery system. In this paper, a multi-objective robust optimisation model is proposed for the design of CLSC network under uncertainty. First, a deterministic bi-objective mixed integer linear programming (BOMILP) model is developed for designing a CLSC network. Then, the robust counterpart of the proposed BOMILP is presented to cope with the real-world uncertainty. The first objective aims to maximise the total profit generated in the CLSC network and the second objective minimises the environmental pollution of the CLSC network. The proposed bi-objective model is solved using a multi-choice goal programming (MCGP) approach.

Suggested Citation

  • Zahra Homayouni & Mir Saman Pishvaee, 2020. "A robust bi-objective programming approach to environmental closed-loop supply chain network design under uncertainty," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 16(2), pages 257-278.
    • Handle: RePEc:ids:ijmore:v:16:y:2020:i:2:p:257-278
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    Cited by:

    1. Essam Kaoud & Mohammad A. M. Abdel-Aal & Tatsuhiko Sakaguchi & Naoki Uchiyama, 2022. "Robust Optimization for a Bi-Objective Green Closed-Loop Supply Chain with Heterogeneous Transportation System and Presorting Consideration," Sustainability, MDPI, vol. 14(16), pages 1-23, August.
    2. Abbas Al-Refaie & Yasmeen Jarrar & Natalija Lepkova, 2021. "Sustainable Design of a Multi-Echelon Closed Loop Supply Chain under Uncertainty for Durable Products," Sustainability, MDPI, vol. 13(19), pages 1-34, October.

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