A distribution center site selection method based on bacterial foraging optimization algorithm

Abstract

The invention discloses a distribution center site selection method based on bacteria foraging optimization algorithm, which comprises the following steps: 1) obtaining a distribution center and a collection of historical distribution addresses in a target city; 2) constructing a distribution center site selection model; 3) bacteria foraging Implementation of food optimization algorithm. The present invention can model by using historical delivery addresses and candidate distribution centers, which is more in line with the site selection of distribution centers with large-scale historical delivery addresses in reality, so that site selection and delivery can be combined to improve the accuracy of site selection for distribution centers Sexuality, reduce distribution costs, improve timeliness of distribution, and improve customer satisfaction.

Description

technical field [0001] The invention relates to the technical field of logistics, in particular to a distribution center site selection method based on a bacterial foraging optimization algorithm. Background technique [0002] The problem of location selection has a long history and exists in all aspects of life, such as the location of factories, logistics distribution centers, and public service facilities. With the development of e-commerce, logistics distribution has become an indispensable part of supply chain management, and the location selection of distribution centers is becoming more and more important. A better distribution center site selection will not only consider the cost of building the distribution center, but also consider the distribution costs of the distribution center and the historical distribution address, so as to improve the logistics transportation efficiency in the supply chain, reduce transportation costs, and Improve customer satisfaction, and...

AI Technical Summary

Problems solved by technology

The tabu search algorithm is the earliest algorithm applied to the problem of site selection. It can avoid falling into local optimum by establishing a tabu table, but its initial solution has a great influence on the quality of the final site selection result; the genetic algorithm solves it by simulating the natural evolution process , but it is easy to be premature; although the particle swarm optimization algorithm has a high solution efficiency, it is easy to fall into a local optimum; the ant colony algorithm solves the problem through the foraging behavior of ants, but it also has the disadvantage of being easy to fall into a local optimum

Bacterial foraging optimization algorithm, as a kind of swarm intelligence optimization algorithm, also has the disadvantage of being easy to fall into local optimum

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