A differential evolution logistics distribution path optimization method based on parameter self-learning
A differential evolution and path optimization technology, applied in logistics, data processing applications, forecasting, etc., can solve problems such as low search efficiency and low reliability of distribution plans, so as to maintain population diversity, improve search efficiency and reliability, and avoid The effect of premature convergence
Active Publication Date: 2021-06-18
ZHEJIANG UNIV OF TECH
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[0005] In order to overcome the shortcomings of low search efficiency and low reliability of distribution schemes in the existing logistics distribution vehicle route optimization methods, the present invention proposes a parameter self-learning algorithm based on simple and efficient coding, fast search speed, and high reliability of distribution schemes. Differential evolution logistics distribution route optimization method
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[0046] The present invention will be further described below in conjunction with the accompanying drawings.
[0047] refer to figure 1 and figure 2 , a parameter self-learning based differential evolution logistics distribution path optimization method, including the following steps:
[0048] 1) Establish the following objective function with the goal of the shortest total distance of all delivery vehicles:
[0049]
[0050] in, is the number of delivery vehicles, q i Indicates the weight of the goods required by the i-th customer, α∈[0,1] is the constraint factor, Indicates rounding down; r ki Indicates that the customer point is the i-th in the order of customers delivered by the k-th car, r k0 Indicates the distribution center, n k Indicates the number of customers delivered by the kth car, Indicates the distance between the i-th customer delivered by the k-th car and the i-1th customer, Table kth car delivered n k The journey back to the distribution cent...
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A logistics distribution path optimization method based on parameter self-learning. First, the problem is encoded according to the distribution points and vehicles, and converted into variables that can be optimized by the algorithm; then, a differential evolution algorithm based on parameter self-learning is designed, and the progressive Combining the strategy of formula update step factor and crossover probability with the strategy of mutation acquisition based on local elite information, it not only improves the actual search efficiency and reliability of the algorithm, but also effectively avoids premature convergence; finally, according to the algorithm The designed code optimizes the actual vehicle distribution problem, and decodes the optimal solution to obtain the optimal distribution plan. The invention provides a logistics distribution path optimization method based on parameter self-learning with fast search speed and reliable results.
Description
technical field [0001] The invention relates to the fields of logistics distribution, commercial transportation, optimization algorithms and computer software applications, and in particular to a method for optimizing logistics distribution paths based on differential evolution based on parameter self-learning. Background technique [0002] Vehicle Routing Problem (VRP) is a very important problem in the research of modern logistics management. With the rapid development of society and economy, people's demand for cargo transportation and distribution is increasing, and the proportion of transportation and logistics services is rising. The pressure of distribution is a prominent problem faced by logistics companies responsible for terminal logistics distribution. At the same time, urban residents The continuous increase in the number of people, the continuous expansion of the city, and traffic congestion have seriously affected the distribution efficiency of the logistics te...
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IPC IPC(8): G06Q10/04G06Q10/08
CPCG06Q10/047G06Q10/083
Inventor 张贵军赵雨滴周晓根马来发谢腾宇
Owner ZHEJIANG UNIV OF TECH