Intelligent vehicle scheduling method and device, electronic equipment and storage medium

Abstract

The invention provides an intelligent vehicle scheduling method and device based on a genetic algorithm, electronic equipment and a storage medium. The method comprises steps of the number K of vehicles required for distribution being determined according to a current distribution task of a distribution center and the load capacity of a single vehicle, and 2N vehicle path schemes for executing the current distribution task by using K vehicles being determined; taking the 2N vehicle path schemes as 2N chromosomes, calculating the fitness of each chromosome, and constructing an initial population by using the N chromosomes with optimal fitness; setting a crossover probability and a mutation probability in stages, carrying out staged iterative operation on the initial population by adopting a genetic algorithm according to the crossover probability and the mutation probability until a preset maximum number of iterations, and determining the vehicle path scheme corresponding to the chromosome which has the highest fitness and is not overloaded in the population after evolution as the vehicle path scheme of the distribution center. According to the method, a premature phenomenon of a modern heuristic algorithm can be well avoided, the global optimization capability of the algorithm is enhanced, and an optimal solution with relatively high quality can be solved.

Description

technical field [0001] The invention relates to the technical field of logistics distribution, in particular to a vehicle intelligent scheduling method, device, electronic equipment and storage medium. Background technique [0002] The vehicle routing problem is one of the core issues in the optimization of transportation organization. It is the key decision-making layer for formulating an efficient vehicle distribution plan. It mainly realizes the delivery of goods with the minimum transportation cost and expense under the condition of meeting the actual needs of customers. destination. The vehicle routing problem has been proven to be an NP-Hard problem, which has attracted great attention from scholars in the field, because the problem itself is closely related to real life, and the research results are highly practical. [0003] At present, the main solutions to vehicle routing problems include exact algorithms, classic heuristic algorithms, and modern heuristic algorit...

AI Technical Summary

Problems solved by technology

Among them, modern heuristic algorithms, such as genetic algorithm, ant colony algorithm, and tabu search, belong to the algorithm in the field of computational intelligence. This type of algorithm is based on the idea of ​​population evolution and iteratively optimizes. It is currently the most researched and applied method in the field. In the search process of population evolution, it is easy to produce premature and excessive convergence, resulting in the final solution quality being disturbed by the local optimal solution

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