Processing method for vehicle route optimization

Abstract

The invention discloses a vehicle route optimization processing method, comprising the following steps: a, collecting order quantity, network point quantity, network point distance matrix and vehicleinformation; B, setting whether the time window and/or the line template are open; (c) the order quantity, the number of dot, the distance matrix of dots, the vehicle information, whether the time window is open, and whether the route template is open are taken as constraint conditions, the ant colony genetic fusion algorithm is adopted to iteratively calculate the optimal path, and the dot pheromone is updated; Reasonable use of constraints in this design, ant colony genetic fusion algorithm iterative calculation after replication and transformation, the shortest path, and the results of performance indicators are good.

Description

technical field [0001] The invention relates to the field of vehicle logistics, in particular to a processing method for vehicle route optimization. Background technique [0002] The earliest study of logistics route planning in foreign countries was around the middle of the 20th century. Two mathematicians first proposed a model for the vehicle distribution problem, namely the VRP (Vehicle Routing Problem) model. Later, as the scope of its logistics became larger and larger, the practical application value of this problem became higher and higher, so more and more people studied it, and scholars from various fields participated in it. Based on these models, Bodin et al. summarized seven methods to solve these models. Based on the linear programming idea proposed by G.B.Dantzig in 1947, the implicit enumeration algorithm proposed by B.Jackowski et al. in 1985, and the iterative greedy algorithm proposed by J.Culberson in 1992, the precise algorithm successfully solves the s...

AI Technical Summary

Problems solved by technology

These experimental results will have relatively good performance under certain conditions, but they still cannot be used on a large scale. It is possible that the performance of the algorithm itself will vary greatly depending on the change of constraint conditions, resulting in poor reusability of the algorithm, and The optimal solution obtained by using the ant colony algorithm alone or the genetic algorithm alone is not accurate. After experimental analysis, the performance index is poor

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