A Novel Approach for Optimizing Mixed Hazardous Material Fleet Vehicle Routing Problem Method Through Intermediate Bulk Container Sharing with Adaptive Intuitionistic Fuzzy Large Neighborhood Search

This study addresses the mixed fleet Vehicle Routing Problem (MFVRP) for transporting hazardous material (hazmat) goods in port hinterlands, aiming to balance transportation efficiency and sustainability. It refines transportation routes through intermediate bulk containers (IBCs) sharing, incorporating both fuel vehicles (FVs) and electric vehicles (EVs). The main contribution of this study is as follows. First, a mixed fleet strategy is introduced, integrating fuel hazmat vehicles (FHVs) and electric hazmat vehicles (EHVs) to boost efficiency and cut emissions. Second, this study develops a bi-objective Mixed-Integer Nonlinear Programming (MINLP) model with fuzzy time windows for flexible scheduling. Third, an adaptive intuitionistic fuzzy large neighborhood search (AIFLNS) algorithm is proposed, using intuitionistic fuzzy sets (IFSs) to refine scoring mechanisms and operation selection. In this way, this study not only enhances the efficiency of hazmat transportation, but also promotes green logistics in port hinterlands. Finally, experimental results show that the AIFLNS reduces transportation costs and enhances service satisfaction, outperforming adaptive large neighborhood search (ALNS), and ant colony optimization (ACO) algorithms.