A Hybrid Fuzzy C-Means Heuristic Approach for Two-Echelon Vehicle Routing with Simultaneous Pickup and Delivery of Multi-Commodity

Given the transport efficiency of large vehicles in urban environments, an increasing number of enterprises are adopting the factory-warehouse-store transportation model. To address the limitations of previous models in practical transport operations, this study formulates a Two-Echelon Vehicle Routing Problem with Simultaneous Pickup and Delivery for Multi-Commodity. Furthermore, the model uniquely emphasizes certain considerations, such as prioritizing product distribution based on production batches and allowing vehicles to engage in flexible product redistribution. Considering the computational challenges arising from the substantial data involved in real-world problem instances, a hybrid heuristic algorithm is proposed. Initially, a hybrid Fuzzy C-Means is employed to decompose the problem by clustering chain stores, effectively reducing the solution space. Subsequently, an enhanced Multi-Population Genetic Algorithm, integrated with Variable Neighborhood Search, is introduced to solve the decomposed sub-problems. Experimental validation conducted with a food enterprise located in Zhengzhou, China, provides empirical support for the efficacy of the proposed model. Multiple test scenarios further illustrate the superior performance of the proposed algorithm. This study holds significant practical and theoretical implications, offering insights to aid decision-makers in reducing transportation costs and advancing the development and application of Vehicle Routing Problem models.