A robust multi-objective sustainable vehicle routing model with time windows and mixed fleet: an IACO-TOPSIS-PRCC-regression framework

Although several past studies have addressed sustainable transportation issues within the supply chain, many critical real-world factors such as time-dependent vehicle speeds, actual route distances, and load-dependent engine efficiency have not been sufficiently addressed. In order to address these limitations, present study proposes a sustainable transportation network that simultaneously optimizes economic, environmental, and social objectives. A capacitated vehicle routing and scheduling problem is formulated, incorporating heterogeneous vehicles, hard time windows, and multiple time periods. An improved Ant Colony Optimization algorithm is developed with 2-optimal local search for enhanced route quality and convergence. Pareto-optimal solutions are ranked using the technique for order preference by similarity to ideal solution. Partial rank correlation coefficient and regression analysis are conducted to interpret variable influence, offering insights into input-output relationships. A real-world case study from a rural logistics company demonstrates the practical applicability and effectiveness of the proposed framework in solving complex sustainable transportation problems.