Green-resilient model for smartphone closed-loop supply chain network design: A novel four-valued refined neutrosophic optimization

Abstract

This study endeavors to integrate green supply chain and resilience proactive strategies within a closed-loop supply chain network. The research employs a three-step methodology. In the first phase, a multi-objective, multi-period, multi-node, mixed integer linear programming model (MILP) is developed. The objective of this study is to maximize resilience while minimizing both greenhouse gas (GHG) emissions and costs. The environmental footprint is assessed using the life cycle assessment (LCA) tool as part of the green supply chain strategy. To optimize resilience strategies, proximate nodes in the closed-loop supply chain network implement measures involving node criticality and inventory availability. In the second step, a novel four-valued refined multi-objective neutrosophic optimization algorithm is introduced to resolve the model. In contrast to traditional optimization approaches, neutrosophic optimization integrates elements of truth, falsity, contradictions, and uncertainty into the decision-making process. In the third phase, a case study of the smartphone supply chain is presented, advocating for the adoption of circular economy practices to prevent future waste. To assess the resilience of the model, a sensitivity analysis has been conducted, examining various key parameters associated with the market, and studying their impact on the objective function. The model's performance is validated by comparing the solution methodology with other goal programming and interactive fuzzy multi-objective methods. The results highlight the better performance of four-valued refined neutrosophic optimization, achieving 3.59 and 3.38 times greater cumulative gap reduction than other methods. The optimal solution recommends green strategies for an optimal trade-off among cost, GHG emissions, and resilience.