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Multi-objective optimization of closed-loop supply chains to achieve sustainable development goals in uncertain environments

Khalili Fard, A ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1016/j.engappai.2024.108052
  3. Publisher: 2024
  4. Abstract:
  5. In the current global landscape where sustainability is increasingly critical, this study offers a significant contribution by redefining supply chain models through the lens of circular economy principles. It introduces a novel two-stage stochastic model tailored to designing sustainable closed-loop supply chains, adept at navigating uncertainties in location, allocation, and routing decisions. A notable feature of the model is its integration of a speed-variant fuel-efficient green vehicle routing system, which enables simultaneous pick-up and delivery, underscoring a commitment to environmental sustainability and circular economy principles. This model is distinct in its holistic approach, balancing total cost considerations with the creation of job opportunities to boost social benefits, while also addressing the pressing issue of fossil fuel consumption's environmental impact. The innovative use of the heuristic backward scenario reduction method to generate stochastic uncertainty scenarios enhances the model's applicability in real-world conditions. The study employs an upgraded ε-constraint method for smaller instances and a range of advanced metaheuristic algorithms, including Non-dominated Sorting Genetic Algorithm II, Multi-Objective Particle Swarm Optimization, Strength Pareto Evolutionary Algorithm version 2, Multi-Objective Evolutionary Algorithm based on Decomposition, and Pareto Envelope based Selection Algorithm II. This research provides critical insights and tools for managers and policymakers, particularly in scenarios where adherence to sustainability and circular economy principles is vital. This study not only advances academic discourse but also offers pragmatic solutions for real-world supply chain challenges, emphasizing the importance of integrating environmental and social considerations into economic decision-making. © 2024 Elsevier Ltd
  6. Keywords:
  7. Location-allocation-routing ; Simultaneous pick-up and delivery ; Decision making ; Economic and social effects ; Fossil fuels ; Fuel economy ; Genetic algorithms ; Heuristic methods ; Multiobjective optimization ; Particle swarm optimization (PSO) ; Screening ; Stochastic models ; Stochastic systems ; Supply chains ; Sustainable development ; Circular economy ; Closed-loop ; Fuel-efficient green vehicle routing ; Location-allocation ; Multi-objective metaheuristics ; Pickup and delivery ; Routings ; Sustainable closed-loop supply chain ; Vehicle routing
  8. Source: Engineering Applications of Artificial Intelligence ; Volume 133 , 2024 ; 09521976 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0952197624002100