Genetic Algorithm for Multi-Warehouse Item Allocation and Routing

This study introduces a genetic algorithm-based model designed to optimize item delivery within complex logistics networks involving multiple warehouses and demand centers, all constrained by specific delivery time windows. Unlike traditional routing approaches, the proposed framework em phasizes the grouping of items prior to vehicle assignment, aiming to minimize total transportation costs. The model incorporates vehicle type selection and item grouping, enabling more efficient fleet utilization and route planning. To enhance performance and robustness, the algorithm parameters are calibrated using manual parameter calibration and sensitivity analysis methods. The results demonstrate the model’s capacity to identify cost-effective delivery strategies, highlighting its poten tial applicability to real-world logistics operations seeking operational cost reductions and improved delivery efficiency.