Vehicle logistics scheduling method and device, storage medium, and terminal based on multi-objective ant colony algorithm

Abstract

A vehicle logistics scheduling method and device, storage medium, and terminal based on a multi-objective ant colony algorithm, the method comprising: acquiring vehicle logistics data, the vehicle logistics data including order data and capacity data; The vehicle logistics data obtains M candidate allocation schemes, where M≥1; the candidate allocation schemes are recorded as ants, and the set of M ants is recorded as an ant colony. During the transfer period of each ant in the ant colony, Abandon the ants whose target vectors are dominated in the ant colony to obtain a non-inferior solution set, and record the projection of the ants on each target as the target vector corresponding to the target; when the transition state of the ant colony When the preset termination condition is met, the optimal scheduling scheme is selected from the acquired non-inferior solution set according to the business scenario. The scheme provided by the invention can realize the automatic scheduling of the whole vehicle logistics, is beneficial to realize optimal scheduling, and reduces the dynamic scheduling cost of freight vehicles as a whole.

Description

technical field [0001] The invention relates to the technical field of automobile logistics, in particular to a vehicle logistics scheduling method and device, a storage medium, and a terminal based on a multi-objective ant colony algorithm. Background technique [0002] Complete vehicle logistics refers to a series of activities and processes in which complete vehicles are transported from OEMs, distribution sites, and dealers to end customers. Vehicle logistics scheduling needs to solve a series of problems such as logistics route planning, loading and vehicle scheduling. [0003] The factors involved in the existing vehicle logistics scheduling are relatively complex, with many constraints and multiple and mutually restrictive objectives, such as OEMs and their warehouses, logistics companies and their transit warehouses, carriers and their contract drivers, dealers and their warehouses, etc. In many aspects, it is a multi-objective optimization problem in general. [00...

AI Technical Summary

Problems solved by technology

Such a vehicle logistics scheduling method has many disadvantages, such as few variables to consider, non-optimal scheduling plan, low utilization rate of transportation resources, and slow order response speed, which cannot meet the expectations of automakers and customers.

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