Resilient Supply Chain Logistics: A Neutrosophic AbsoluteWidth Optimization Approach

Abstract

Modern supply chains suffer cost volatility, traffic ambiguity, and disruption threats that old transportation models cannot fully address.  This study proposes a Resilient Supply Chain Logistics framework based on a Neutrosophic Absolute Width (NAW) Optimization Approach, in which transportation costs are modelled using Pentagonal Neutrosophic Fuzzy Numbers (PNFNs) that capture truth (reliability), indeterminacy (uncertainty), and untruth (disruption risk).  The innovative NAW defuzzification technique turns these Neutrosophic costs into crisp representative values while maintaining uncertainty spread, allowing for practical decision-making.  The framework, which is presented as a transportation optimization issue and solved using Vogel’s Approximation Method (VAM) and the MODI technique, is validated through a real case study.  Direct costs, uncertainty-related expenses, and disruption losses have been reduced, improving supply chain resilience and supporting sustainability goals aligned with SDGs 9 (Industry, Innovation, and Infrastructure), 12 (Responsible Consumption and Production), and 13 (Climate Action).