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A Multi-Objective Model for an End-of-Life Vehicle Recycling Network under a Reverse Logistics Approach, Considering Sustainability Dimensions

Falahati, Sajjad | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58710 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Akbari Jokar, Mohammad Reza
  7. Abstract:
  8. In recent decades, the increasing number of end-of-life vehicles (ELVs) and the growing consumption of natural resources have created significant economic and environmental challenges for industrial societies. One of the most effective solutions to address these challenges is the design of sustainable reverse logistics networks that can simultaneously reduce environmental impacts while creating new economic and social opportunities. This study aims to design and optimize a reverse logistics network for end-of-life vehicles through a multi-objective approach that simultaneously considers the three key dimensions of sustainability: economic, environmental, and social. To achieve this goal, a multi-objective mathematical model was developed, with the objectives of maximizing economic profit, minimizing environmental impacts, and maximizing employment generation. The proposed model optimizes decisions related to the location of collection, dismantling, remanufacturing, and recycling centers, as well as the material flow between different parts of the network. To solve this complex optimization problem, a multi-objective electric fish algorithm (MFEA) was employed in two versions clustering-based (CBEFO) and Pareto dominance-based (PDEFO) and the results were compared with the NSGA-II algorithm. The results showed that the proposed model successfully achieved a desirable balance among economic, environmental, and social goals. Moreover, the CBEFO algorithm outperformed NSGA-II in generating a more diverse and uniform Pareto front. Sensitivity analysis of key parameters such as resource capacity, transportation cost, energy consumption, and setup cost demonstrated the stability and robustness of the proposed model. These findings confirm the model’s capability in designing efficient reverse logistics networks for ELVs and its potential applicability in industrial decision-making and policy formulation. Overall, this research presents a comprehensive and adaptable framework for designing sustainable reverse logistics networks, contributing to economic efficiency improvement, environmental impact reduction, and local employment enhancement. The outcomes of this study can support the development of a circular economy and promote sustainable development goals
  9. Keywords:
  10. Reverse Logistics ; Meta Heuristic Algorithm ; Pareto Front ; Multi-Objective Modeling ; Economic-Environmental-Social Sustainability ; End-of-Life Vehicles

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