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Optimization method of distribution center location selection based on adaptive levy distribution mixed mutation improved artificial fish swarm algorithm

An artificial fish swarm algorithm and distribution center technology, applied in computing, computational models, artificial life, etc., can solve problems such as difficulty in finding the global optimum, slow convergence of the basic fish swarm algorithm, and easy stagnation.

Active Publication Date: 2019-11-26
TIANJIN UNIV OF COMMERCE
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] 1. The basic fish swarm algorithm needs to spend a lot of money to find an optimization solution for the location of the distribution center
The artificial fish swarm algorithm has good exploration ability in the early stage of algorithm execution, but in the later stage of algorithm execution, it is difficult to find the global optimal solution because the fish swarm can only find a satisfactory solution domain.
[0006] 2. When the basic fish swarm algorithm solves the distribution center location problem, it takes a long time to complete the convergence process of the algorithm, so the convergence speed of the basic fish swarm algorithm is slow
[0007] 3. When the basic fish swarm algorithm is looking for the distribution center location optimization scheme, it is easy to stagnate when finding the local optimal location optimization scheme
At the same time, the cost from the factory to the distribution center is not optimized, but the total cost of the system from the distribution center to the customer demand point is optimized
[0009] Chinese patent CN 104077629 A provides an improved artificial fish swarm algorithm with variable step size and self-adaptation, which only improves the basic artificial fish swarm algorithm. It is possible to apply this improved fish swarm algorithm to this patent, but The minimum system cost it finds is far inferior to the method proposed in this patent
[0020] Therefore, we can further study the use of adaptive Levy distribution to improve the artificial fish swarm algorithm for location selection, so as to overcome the shortcomings of the basic fish swarm algorithm in the distribution center location problem, such as high cost, slow convergence speed, and easy stagnation.

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  • Optimization method of distribution center location selection based on adaptive levy distribution mixed mutation improved artificial fish swarm algorithm
  • Optimization method of distribution center location selection based on adaptive levy distribution mixed mutation improved artificial fish swarm algorithm
  • Optimization method of distribution center location selection based on adaptive levy distribution mixed mutation improved artificial fish swarm algorithm

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Embodiment 1

[0177] The present invention will be described in detail below by taking a distribution center location optimization method for 10 distribution centers and 20 customer demand points as an example.

[0178] The production company has a factory with the coordinates of (2545, 2357), and 10 distribution centers are selected to deliver to 20 customer demand points. It is required that the maximum number of distribution centers to be built is 3. Table 1 is the coordinates of customer demand points, and Table 2 is the coordinates of 10 alternative distribution centers. The transportation cost per unit distance between the supply point, distribution center, and demand point is 1. Table 3 shows the distribution center capacity, fixed assets and circulation and transfer costs. Table 4 shows the demand of customer demand points. Set the number of artificial fish to 50, the number of attempts to 100, the field of view of the artificial fish to 300, the crowding factor to 0.618, the max...

Embodiment 2

[0198] Assuming that a manufacturing company has a factory with coordinates (85, 80), 10 distribution centers are selected to deliver to 15 customer demand points. It is required that the maximum number of distribution centers to be built is 4. Table 8 is the coordinates of customer demand points, Table 9 is the coordinates of 10 alternative distribution centers, Table 10 is the table of transport costs per unit distance between factories, distribution centers, and demand points, and Table 11 is the distribution center capacity, fixed assets and circulation transfer Fees, Table 12 shows the demand of customer demand points. Set the scale of the artificial fish to 50, the number of trials to 80, the field of view to 18, the crowding factor to 0.618, the moving step of the artificial fish to 5, the characteristic parameter α of the Levy distribution to 0.8, the control parameter of chaotic variation to 4, and the maximum iteration The number of times is 30.

[0199] Table 8 Co...

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Abstract

The invention belongs to the logistics distribution site selection technical field and relates to an adaptive Levy distribution hybrid mutation improved artificial fish swarm algorithm-based distribution center site selection optimization method. The method includes the following steps that: (1) relevant parameters are initialized, and a distribution center site selection optimization model is established; (2) the distribution center site selection optimization model is solved through using the optimization method according to which adaptive Levy distribution hybrid mutation is utilized to improve an artificial fish swarm algorithm; and (3) a distribution center site selection result is compared with the result of using the adaptive Levy distribution hybrid mutation to improve the artificial fish swarm algorithm in solving a distribution center site selection problem. According to the method of the invention, Levy mutation and chaotic mutation are introduced into the basic fish swarm algorithm, so that the diversity of artificial fish states in the basic artificial fish swarm algorithm can be increased, the capability of the basic artificial fish swarm algorithm to jump out of local optimum can be improved, and the optimization of distribution center site selection can be enhanced.

Description

technical field [0001] The invention belongs to the technical field of location selection for logistics distribution, and in particular relates to an optimization method for location selection of a distribution center based on an adaptive Levy distribution mixed variation improved artificial fish swarm algorithm. Background technique [0002] With the continuous development of economic globalization and science and technology, and the increasing pace of reform of my country's economic system, the importance of the logistics industry in the national economy has become prominent, and it has gradually become an indispensable link in the industrial structure. As the artery of the national economy, logistics connects all elements of social production, provides a strong guarantee for the creation of social material wealth, and provides means for production enterprises to obtain profits. Therefore, the research on the logistics industry is not only of macroscopic significance to the...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/387G06Q10/04G06N3/00G06Q10/08
CPCG06N3/006G06Q10/04G06Q10/08
Inventor 费腾张立毅孙云山陈雷张勇
Owner TIANJIN UNIV OF COMMERCE
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