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A novel sustainable multi-objective optimization model for forward and reverse logistics system under demand uncertainty

Author

Listed:
  • Navid Zarbakhshnia

    (University of Southern Denmark)

  • Devika Kannan

    (University of Southern Denmark)

  • Reza Kiani Mavi

    (Edith Cowan University)

  • Hamed Soleimani

    (University of Melbourne)

Abstract

The paper aims to present a multi-product, multi-stage, multi-period, and multi-objective, probabilistic mixed-integer linear programming model for a sustainable forward and reverse logistics network problem. It looks at original and return products to determine both flows in the supply chain—forward and reverse—simultaneously. Besides, to establish centres of forward and reverse logistics activities and make a decision for transportation strategy in a more close-to-real manner, the demand is considered uncertain. We attempt to represent all major dimensions in the objective functions: First objective function is minimizing the processing, transportation, fixed establishing cost and costs of CO2 emission as environmental impacts. Furthermore, the processing time of reverse logistics activities is developed as the second objective function. Finally, in the third objective function, it is tried to maximize social responsibility. Indeed, a complete sustainable approach is developed in this paper. In addition, this model provides novel environmental constraint and social matters in the objective functions as its innovation and contribution. Another contribution of this paper is using probabilistic programming to manage uncertain parameters. Moreover, a non-dominated sorting genetic algorithm (NSGA-II) is configured to achieve Pareto front solutions. The performance of the NSGA-II is compared with a multi-objective particle swarm optimization (MOPSO) by proposing 10 appropriate test problems according to five comparison metrics using analysis of variance (ANOVA) to validate the modeling approach. Overall, according to the results of ANOVA and the comparison metrics, the performance of NSGA-II algorithm is more satisfying compared with that of MOPSO algorithm.

Suggested Citation

  • Navid Zarbakhshnia & Devika Kannan & Reza Kiani Mavi & Hamed Soleimani, 2020. "A novel sustainable multi-objective optimization model for forward and reverse logistics system under demand uncertainty," Annals of Operations Research, Springer, vol. 295(2), pages 843-880, December.
    • Handle: RePEc:spr:annopr:v:295:y:2020:i:2:d:10.1007_s10479-020-03744-z
    DOI: 10.1007/s10479-020-03744-z
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    References listed on IDEAS

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    Cited by:

    1. Govindan, Kannan & Gholizadeh, Hadi, 2021. "Robust network design for sustainable-resilient reverse logistics network using big data: A case study of end-of-life vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    2. Shoufeng Ji & Pengyun Zhao & Tingting Ji, 2023. "A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet," Sustainability, MDPI, vol. 15(7), pages 1-34, April.
    3. Xu, Yuqiu & Wang, Jia & Cao, Kaiying, 2023. "Logistics mode strategy of firms selling fresh products on e-commerce platforms with private brand introduction," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    4. Peter Shobayo & Edwin van Hassel & Thierry Vanelslander, 2023. "Logistical Assessment of Deep-Sea Polymetallic Nodules Transport from an Offshore to an Onshore Location Using a Multiobjective Optimization Approach," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    5. Navid Zarbakhshnia & Kannan Govindan & Devika Kannan & Mark Goh, 2023. "Outsourcing logistics operations in circular economy towards to sustainable development goals," Business Strategy and the Environment, Wiley Blackwell, vol. 32(1), pages 134-162, January.
    6. Alireza Bakhshi & Jafar Heydari, 2023. "An optimal put option contract for a reverse supply chain: case of remanufacturing capacity uncertainty," Annals of Operations Research, Springer, vol. 324(1), pages 37-60, May.
    7. Zhe Liu & Shurong Li, 2022. "A numerical method for interval multi-objective mixed-integer optimal control problems based on quantum heuristic algorithm," Annals of Operations Research, Springer, vol. 311(2), pages 853-898, April.
    8. Duong, Quang Huy & Zhou, Li & Meng, Meng & Nguyen, Truong Van & Ieromonachou, Petros & Nguyen, Duy Tiep, 2022. "Understanding product returns: A systematic literature review using machine learning and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 243(C).

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