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Robust Optimization for a Bi-Objective Green Closed-Loop Supply Chain with Heterogeneous Transportation System and Presorting Consideration

Author

Listed:
  • Essam Kaoud

    (Department of Mechanical Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Japan)

  • Mohammad A. M. Abdel-Aal

    (Industrial and Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
    Interdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Tatsuhiko Sakaguchi

    (Department of Informatics, Faculty of Engineering, Kindai University, Higashi-Hiroshima 739-2116, Japan)

  • Naoki Uchiyama

    (Department of Mechanical Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Japan)

Abstract

In this study, we propose a robust bi-objective optimization model of the green closed-loop supply chain network considering presorting, a heterogeneous transportation system, and carbon emissions. The proposed model is an uncertain bi-objective mixed-integer linear optimization model that maximizes profit and minimizes carbon emissions by considering uncertain costs, selling price, and carbon emissions. The robust optimization approach is implemented using the combined interval and polyhedral, “Interval+ Polyhedral,” uncertainty set to develop the robust counterpart of the proposed model. Robust Pareto optimal solutions are obtained using a lexicographic weighted Tchebycheff optimization approach of the bi-objective model. Intensive computational experiments are conducted and a robust Pareto optimal front is obtained with a probability guarantee that the constraints containing uncertain parameters are not violated (constraint satisfaction).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10281-:d:891697
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    References listed on IDEAS

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    1. 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.
    2. Soheyl Khalilpourazari & Shima Soltanzadeh & Gerhard-Wilhelm Weber & Sankar Kumar Roy, 2020. "Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study," Annals of Operations Research, Springer, vol. 289(1), pages 123-152, June.
    3. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    4. Yu, Yang & Li, Shuangqi & Sun, Huaping & Taghizadeh-Hesary, Farhad, 2021. "Energy carbon emission reduction of China’s transportation sector: An input–output approach," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 378-393.
    5. Chen, Cheng-Kang & Ulya, M. Akmalul ', 2019. "Analyses of the reward-penalty mechanism in green closed-loop supply chains with product remanufacturing," International Journal of Production Economics, Elsevier, vol. 210(C), pages 211-223.
    6. Samanlioglu, Funda, 2013. "A multi-objective mathematical model for the industrial hazardous waste location-routing problem," European Journal of Operational Research, Elsevier, vol. 226(2), pages 332-340.
    7. Aliakbar Hasani & Seyed Hessameddin Zegordi & Ehsan Nikbakhsh, 2015. "Robust closed-loop global supply chain network design under uncertainty: the case of the medical device industry," International Journal of Production Research, Taylor & Francis Journals, vol. 53(5), pages 1596-1624, March.
    8. Gorissen, Bram L. & Yanıkoğlu, İhsan & den Hertog, Dick, 2015. "A practical guide to robust optimization," Omega, Elsevier, vol. 53(C), pages 124-137.
    9. Zahra Ghasemzadeh & Ahmad Sadeghieh & Davood Shishebori, 2021. "A stochastic multi-objective closed-loop global supply chain concerning waste management: a case study of the tire industry," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5794-5821, April.
    10. Micheli, Guido J.L. & Mantella, Fabio, 2018. "Modelling an environmentally-extended inventory routing problem with demand uncertainty and a heterogeneous fleet under carbon control policies," International Journal of Production Economics, Elsevier, vol. 204(C), pages 316-327.
    11. Omid Abdolazimi & Farzad Bahrami & Davood Shishebori & Majid Alimohammadi Ardakani, 2022. "A multi-objective closed-loop supply chain network design problem under parameter uncertainty: comparison of exact methods," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 10768-10802, September.
    12. Ivan Darma Wangsa & Hui Ming Wee, 2020. "Integrated inventory system with freight costs and two types of quantity discounts," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 35(1), pages 119-147.
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    Cited by:

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    2. Prasit Kailomsom & Charoenchai Khompatraporn, 2023. "A Multi-Objective Optimization Model for Multi-Facility Decisions of Infectious Waste Transshipment and Disposal," Sustainability, MDPI, vol. 15(6), pages 1-16, March.

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