A Two-Stage Genetic Algorithm-Based Approach for Fuzzy-Constrained Logistics Transportation Order Consolidation

This study addresses the logistics order consolidation problem with fuzzy constraint conditions by proposing a classification-consolidation two-stage iterative optimization approach. orders are precisely classified and fuzzily categorized based on their multi-dimensional constraint characteristics. The improved genetic algorithm is applied to search for optimal consolidation solutions, with performance metrics dynamically adjusting vehicle allocations in the classification stage, leading to iterative optimization and ultimately reduced transportation mileage. Experimental results demonstrate that this approach performs exceptionally well in small-, medium-, and large-scale tests, significantly outperforming traditional genetic algorithms and depth-first search by reducing total transportation distance while maintaining a consistently high load factor. This study provides an efficient and cost-effective engineering solution for complex logistics scenarios, demonstrating considerable application value.