A Processing Method for Vehicle Routing Optimization

Abstract

The invention discloses a processing method for vehicle route optimization, comprising the following steps: a. collecting order quantity, network point quantity, network point distance matrix and vehicle information; b. setting time window and / or whether the route template is open; c. The number of orders, the number of outlets, the distance matrix of outlets, vehicle information, whether the time window is opened, and whether the route template is opened are used as constraints, and the optimal path is calculated by using ant colony genetic fusion algorithm iteratively, and the pheromone of the outlets is updated; this design makes reasonable use of Constraint conditions, the ant colony genetic fusion algorithm is iteratively calculated and then copied and transformed to obtain the shortest path, and the resulting performance index is better.

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...

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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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