IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i16p10281-d891697.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/16/10281/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/16/10281/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fang Xu & Mengfan Yan & Lun Wang & Shaojian Qu, 2022. "The Robust Emergency Medical Facilities Location-Allocation Models under Uncertain Environment: A Hybrid Approach," Sustainability, MDPI, vol. 15(1), pages 1-23, December.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sarhadi, Hassan & Naoum-Sawaya, Joe & Verma, Manish, 2020. "A robust optimization approach to locating and stockpiling marine oil-spill response facilities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    2. Hasani, Aliakbar & Khosrojerdi, Amirhossein, 2016. "Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 20-52.
    3. Sun, Hao & Yang, Jun & Yang, Chao, 2019. "A robust optimization approach to multi-interval location-inventory and recharging planning for electric vehicles," Omega, Elsevier, vol. 86(C), pages 59-75.
    4. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 143-166, March.
    5. Shunichi Ohmori, 2021. "A Predictive Prescription Using Minimum Volume k -Nearest Neighbor Enclosing Ellipsoid and Robust Optimization," Mathematics, MDPI, vol. 9(2), pages 1-16, January.
    6. Wang, Tian & Deng, Shiming, 2019. "Multi-Period energy procurement policies for smart-grid communities with deferrable demand and supplementary uncertain power supplies," Omega, Elsevier, vol. 89(C), pages 212-226.
    7. Jiang, Sheng-Long & Peng, Gongzhuang & Bogle, I. David L. & Zheng, Zhong, 2022. "Two-stage robust optimization approach for flexible oxygen distribution under uncertainty in integrated iron and steel plants," Applied Energy, Elsevier, vol. 306(PB).
    8. Viktoryia Buhayenko & Dick den Hertog, 2017. "Adjustable Robust Optimisation approach to optimise discounts for multi-period supply chain coordination under demand uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 55(22), pages 6801-6823, November.
    9. Soheyl Khalilpourazari & Seyed Hamid Reza Pasandideh, 2021. "Designing emergency flood evacuation plans using robust optimization and artificial intelligence," Journal of Combinatorial Optimization, Springer, vol. 41(3), pages 640-677, April.
    10. Xin Zhang & Gang Zhao & Yingxiu Qi & Botang Li, 2019. "A Robust Fuzzy Optimization Model for Closed-Loop Supply Chain Networks Considering Sustainability," Sustainability, MDPI, vol. 11(20), pages 1-24, October.
    11. Chassein, André & Goerigk, Marc, 2018. "Variable-sized uncertainty and inverse problems in robust optimization," European Journal of Operational Research, Elsevier, vol. 264(1), pages 17-28.
    12. Wang, Yu & Zhang, Yu & Tang, Jiafu, 2019. "A distributionally robust optimization approach for surgery block allocation," European Journal of Operational Research, Elsevier, vol. 273(2), pages 740-753.
    13. Van Engeland, Jens & Beliën, Jeroen & De Boeck, Liesje & De Jaeger, Simon, 2020. "Literature review: Strategic network optimization models in waste reverse supply chains," Omega, Elsevier, vol. 91(C).
    14. Shin, Youngchul & Moon, Ilkyeong, 2023. "Robust building evacuation planning in a dynamic network flow model under collapsible nodes and arcs," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    15. Moret, Stefano & Babonneau, Frédéric & Bierlaire, Michel & Maréchal, François, 2020. "Overcapacity in European power systems: Analysis and robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    16. Jianzhe Zhen & Dick den Hertog, 2018. "Computing the Maximum Volume Inscribed Ellipsoid of a Polytopic Projection," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 31-42, February.
    17. Feng, Yuqiang & Che, Ada & Tian, Na, 2024. "Robust inventory routing problem under uncertain demand and risk-averse criterion," Omega, Elsevier, vol. 127(C).
    18. Chunlin Xin & Jie Wang & Ziping Wang & Chia-Huei Wu & Muhammad Nawaz & Sang-Bing Tsai, 2022. "Reverse logistics research of municipal hazardous waste: a literature review," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 1495-1531, February.
    19. Guo, Xiaotong & Caros, Nicholas S. & Zhao, Jinhua, 2021. "Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 161-189.
    20. Perraudat, Antoine & Dauzère-Pérès, Stéphane & Vialletelle, Philippe, 2022. "Robust tactical qualification decisions in flexible manufacturing systems," Omega, Elsevier, vol. 106(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10281-:d:891697. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.