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Take-out delivery path optimization method based on hybrid genetic algorithm

A hybrid genetic algorithm and path optimization technology, applied in the field of path optimization for food delivery, can solve the problems of genetic algorithm time-consuming, low search efficiency, poor local search ability, etc., to achieve the effect of overcoming local search ability and reducing costs

Pending Publication Date: 2021-03-30
BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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AI Technical Summary

Problems solved by technology

Genetic algorithm is a global optimization probability algorithm and is very suitable for solving such complex optimization problems, but it also has some shortcomings, such as poor local search ability, which leads to time-consuming simple genetic algorithm and low search efficiency in the later stage of evolution Wait

Method used

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  • Take-out delivery path optimization method based on hybrid genetic algorithm
  • Take-out delivery path optimization method based on hybrid genetic algorithm
  • Take-out delivery path optimization method based on hybrid genetic algorithm

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Embodiment Construction

[0030] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0031] A specific embodiment can be described as a courier needs to complete 10 groups of takeaway delivery tasks, that is, needs to traverse 20 merchant or customer nodes. Part of the node information is shown in the table below. Node 0 represents the starting point of delivery, nodes 1-5 represent merchants, nodes 11-15 represent customers, and there is a one-to-one correspondence between merchants and customers. Assume that the courier keeps moving at a constant speed during the driving process, the speed is 20km / h, the cost per unit distance is 1, and the penalty cost per unit time is 2.

[0032]

[0033]

[0034] The mathematical model of food delivery is:

[0035] Objective function:

[0036]

[0037] minz represents the minimum cost of food delivery, c 1 Indicates the distribution cost per unit distance, i, j repres...

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Abstract

The invention discloses a take-out delivery path optimization method based on a hybrid genetic algorithm, and aims at the experience dependence problem of take-out distribution at present. The requirements of time window punishment and a distribution sequence are introduced, a mathematical model is established, the hybrid genetic algorithm is used for solving, and finally, an optimal take-out delivery path is obtained. According to the hybrid algorithm, a genetic algorithm and a hill climbing algorithm are combined, and the defects of the genetic algorithm in the aspect of local search capability and the defects of the hill climbing algorithm in the aspect of global search capability are overcome to a certain extent, so that a solution with relatively high quality can be obtained.

Description

technical field [0001] The invention relates to route optimization for takeaway delivery, in particular to a method for optimizing a takeaway delivery route based on a hybrid genetic algorithm. Background technique [0002] As an important part of the food delivery industry, takeaway delivery has attracted extensive attention from major food delivery platforms and customers. In the delivery process, if the delivery is based solely on the experience of the delivery staff, it is easy to have an unreasonable delivery route and cause unnecessary losses. Therefore, scientific route planning is needed to improve this phenomenon. In recent years, many scholars have studied this problem by adopting different heuristic algorithms. Li Taoying et al. used the k-means algorithm to cluster merchants-customers with the goal of the incremental sum of takeaway delivery costs, and used a genetic algorithm to solve the problem; Jin Zhihong et al. aimed at maximizing transportation efficiency...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/08G06N3/12
CPCG06Q10/047G06Q10/08355G06N3/126
Inventor 江欢朱桐
Owner BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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