Multi-parking-lot multi-vehicle-type vehicle path planning method

Abstract

The invention relates to a multi-parking-lot multi-vehicle-type vehicle path planning method, and the method comprises the steps: obtaining vehicle information and customer information, and determining a hybrid code; according to the hybrid codes, determining an initial feasible solution, retaining iteration elite individuals, updating a pheromone matrix, and performing individual screening according to individual target values of the iteration elite individuals to form a screened elite population; based on the above conditions, improving by using a two-side successive correction algorithm to determine an improved individual, and adding the improved individual into the screened elite population until an initial population scale is reached; and carrying out crossover variation on the individuals by adopting a fixed-point crossover algorithm, selecting orthogonal experimental parameters, optimizing the calculation capability of the algorithm, and outputting an iterative optimal solution. On the basis of an initial feasible solution, an improved pheromone updating strategy and an elite population strategy are combined, a two-side successive correction algorithm is utilized for improvement, a fixed-point crossover algorithm is adopted for crossover variation of individuals, the search space is expanded, the convergence speed is increased, and the calculation performance is optimized.

Description

technical field [0001] The invention relates to the technical field of path planning, in particular to a method for path planning of vehicles of multiple vehicle types in multiple parking lots. Background technique [0002] In logistics enterprises, the problem of vehicle routing is of great significance to the development of enterprises, which determines the level of logistics costs and the quality of transportation efficiency. In practical applications, reasonable vehicle route planning needs to consider many factors, such as car models, customer needs, time sequence, etc., and with the development of the logistics industry, the distribution scale continues to increase. Planning has obviously become less suitable for the real situation, and it is more common in real life to combine multiple distribution centers for transportation and use different models for distribution. However, more factors need to be considered in the vehicle scheduling and customer service sequence o...

AI Technical Summary

Problems solved by technology

[0003] In view of this, it is necessary to provide a multi-depot multi-model vehicle path planning method to overcome the shortcomings of poor versatility and slow solution of path planning in complex scenarios in the prior art

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