Dynamic Fuzzy Risk-Averse Multi-Objective Capacitated Transportation Optimization: A Triangular Fuzzy Goal Programming Approach f or Sustainable Logistics

The transportation of goods in a supply chain must navigate multiple conflicting objectives, including minimizing cost, route risk, and environmental impacts. This study presents the development, formulation, and validation of a Dynamic Fuzzy Risk-Averse Multi-Objective Capacitated Transportation Optimization (DFRAMCTO) model for sustainable logistics planning. The model simultaneously minimizes transportation costs, route-specific risks, and carbon emissions within a fuzzy, risk-sensitive, and capacity-constrained environment. Leveraging triangular fuzzy numbers to capture parameter uncertainty, the model integrates dynamic cost structures, time-varying risk coefficients, and emission penalties into a unified optimization framework. A Triangular Fuzzy Goal Programming (TFGP) approach, based on the max–min compromise strategy, transforms the multi-objective fuzzy problem into a solvable linear program. The methodology includes model formulation, parameterization through simulated yet realistic datasets, defuzzification via the Graded Mean Integration Representation method. A numerical illustration demonstrates model applicability. Results revealed that incorporating dynamic risk-aversion and fuzzy multi-objective trade-offs significantly improves decision robustness in uncertain logistics networks. The DFRAMCTO model contributes to operational research by offering a transparent, adaptable, and sustainability-aware decision-support tool for transportation planners.