Improved Genetic Algorithm in Multi-objective Cargo Logistics Loading and Distribution

Abstract

To solve the problem of material distribution path planning in a production workshop, this paper proposes research on multi-objective cargo logistics loading and distribution based on an improved genetic algorithm. This paper improves the genetic algorithm to solve the problem (P), that is, the evolution model based on the genetic algorithm draws lessons from the coding mode of the genetic algorithm, and uses the row insertion method to obtain the initial population. The improved genetic algorithm is better than the traditional genetic algorithm. The rapid development of railway transportation towards high speed, high density, and heavy load has led to even higher requirements for the safety of railway signal equipment. The safety of railway signal equipment is an important part of ensuring railway traffic safety, thus, it is very necessary to study a system that can diagnose the fault of railway signal equipment according to the actual situation. This article utilizes the genetic algorithm of artificial intelligence for investigating the loading and distribution of logistics in transportation. It is demonstrated that genetic algorithm integration is an effective method to improve the performance of logistic distribution model. The convergence speed of the improved genetic algorithm is fast, and it shows a stable upward trend with the increase of the number of iterations.