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Multi-Objective Optimization for Sustainable Supply Chain and Logistics: A Review

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  • Chamari Pamoshika Jayarathna

    (School of Civil and Environmental Engineering, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia
    Department of Commerce and Financial Management, University of Kelaniya, Dalugama 11300, Sri Lanka)

  • Duzgun Agdas

    (School of Civil and Environmental Engineering, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia)

  • Les Dawes

    (School of Civil and Environmental Engineering, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia)

  • Tan Yigitcanlar

    (School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia)

Abstract

There are several methods available for modeling sustainable supply chain and logistics (SSCL) issues. Multi-objective optimization (MOO) has been a widely used method in SSCL modeling (SSCLM), nonetheless selecting a suitable optimization technique and solution method is still of interest as model performance is highly dependent on decision-making variables of the model development process. This study provides insights from the analysis of 95 scholarly articles to identify research gaps in the MOO for SSCLM and to assist decision-makers in selecting suitable MOO techniques and solution methods. The results of the analysis indicate that economic and environmental aspects of sustainability are the main context of SSCLM, where the social aspect is still limited. More SSCLMs for sourcing, distribution, and transportation phases of the supply chain are required. Additionally, more sophisticated techniques and solution methods, including hybrid metaheuristics approaches, are needed in SSCLM.

Suggested Citation

  • Chamari Pamoshika Jayarathna & Duzgun Agdas & Les Dawes & Tan Yigitcanlar, 2021. "Multi-Objective Optimization for Sustainable Supply Chain and Logistics: A Review," Sustainability, MDPI, vol. 13(24), pages 1-31, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13617-:d:698854
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    References listed on IDEAS

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    2. Martins, C.L. & Pato, M.V., 2024. "Decomposition heuristics for multiobjective problems. The Food bank network redesign case," International Journal of Production Economics, Elsevier, vol. 268(C).

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