The Impact of GA Optimization Model under the Constraint of Maximum Inventory on the Logistics Cost Control of Automotive Parts Production in the Factory

Abstract

The logistics of parts entering the factory is an important component of the cost source for manufacturing plants. How to efficiently transport is the key for its enterprises to achieve low-cost control. To address this issue, this study proposed a path planning model based on an improved genetic algorithm. Firstly, a circular distribution model suitable for component transportation logistics was selected, and then constraints such as maximum inventory at the line edge were introduced for design. The basic design of the genetic algorithm was also carried out. Subsequently, three neighborhood structures were introduced for optimization to address the convergence speed and other issues of the algorithm. In response to the demand fluctuation phenomenon in practical applications, a new coding design was carried out. To verify the impact of the model on inbound logistics costs, simulation experiments were conducted on the MATLAB platform. The results showed that the designed algorithm had an average decrease of 14% in total mileage compared to single objective nonlinear models and collaborative network models, while the total cost had decreased by 26.58%. In summary, the improved genetic algorithm model designed in this study has a positive impact on the cost control of inbound logistics.