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This paper presents the optimal design of in-plant logistics in manufacturing. Owing to rising energy costs and the supply-demand imbalance leading to price competition, resulting in a sharp decline in profit, business management is facing a severe test, especially heavy industry. In practice, the important decision-making in C Company has relied on expert experience and existing knowledge, the lack of systematic thinking, and technology applications, resulting in a waste of logistics costs. Therefore, a study on the application of genetic algorithms to optimize steel mill factory by-product transport and logistics is presented herein. The aim is to solve the bottleneck of traditional decision-making in order to achieve the goal of optimizing transportation logistics decision-making via artificial intelligence. In defining the problem, the model of the steel mill factory by-products transport and logistics is constructed through in-plant route information, vehicle routing systematization and consideration of the transport demand frequency. The modified variable length chromosome ending technique and bi-level genetic algorithm are used to effectively solve the problem of different zoning transportation on double layer genetic algorithm application. The results show that the total transport time is slightly better than the existing results.

Optimal design for transport and logistics of steel mill by-product based on double-layer genetic algorithms