Emergency medical material distribution optimization method based on ant colony algorithm

Abstract

The invention relates to an emergency medical material distribution optimization method based on an ant colony algorithm, which is established on the basis of a regional group concept, k-means clustering, the ant colony, a fair algorithm and a binary expression mode. The method comprises the following steps: firstly, classifying by adopting k-means clustering according to coordinates of distribution points; secondly, further classifying load capacities of distribution vehicles by utilizing binary full traversal; then, determining a group distribution path by adopting an ant colony algorithm; sequentially returning to judge that conditions are met and setting parameters to reduce the operand; and finally, adopting a fair algorithm to give a specific distribution scheme according to emergencies. All steps are simple and clear, and the invention has great application value in the field of emergency medical logistics distribution.

Description

technical field [0001] The invention relates to the technical field of logistics scheduling, in particular to an ant colony algorithm-based distribution optimization technology for emergency medical materials. Background technique [0002] The material distribution scheduling problem belongs to the vehicle routing problem, which can be described as: in the case of ensuring the completion of the distribution task, rationally use limited resources, organize and plan a series of loading points (distribution centers) and unloading points (distribution destinations) The driving route is a route planning method to make vehicles transport in an orderly manner, improve transport efficiency and save resources. Under certain constraints (such as the demand of distribution points, the load capacity of distribution vehicles, and the limitation of the length of distribution routes, etc.), to achieve certain goals (the distribution route should be as short as possible, the vehicle full ra...

AI Technical Summary

Problems solved by technology

There is a gap in the market for intelligent and optimized distribution systems with corresponding requirements

Secondly, under emergencies, there is a certain amount of control over the provision of a large number of medical delivery vehicles for emergency medical treatment; there is also a shortage of staffing

Although artificial intelligence can alleviate the problem to a certain extent, most of the medical distribution problems still need to be solved manually under real conditions.

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