Multi-Objective Optimization of Logistics Distribution Route for Industry 4.0 Using the Hybrid Genetic Algorithm

In the growth of Industry 4.0, logistics plays a critical role. Particularly, manufacturing-based sectors require contemporary and intelligent logistics distribution routes to optimize production-related processes. Smart approaches for optimizing logistic routes are required based on the widespread occurrence of vehicles in modern logistics distribution routes failing to complete tasks according to the distribution route and within the specified deadline. The logistics distribution route is unreasonable; thus, a multi-objective optimization approach based on a hybrid genetic algorithm is investigated, and experimentation is carried out to verify the findings. This study employs a hybrid evolutionary algorithm in conjunction with a simulated annealing approach to achieve priority grouping of distribution items and distribution route synthesis, with a single distribution center, as the starting point. The best route is then determined. The results of the experiments reveal that this strategy can successfully address the little difficulties in contemporary logistics distribution paths and accomplish logistics distribution path optimization. This strategy may be used by production-based enterprises in Industry 4.0 to optimize logistics distribution routes.