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Multi-species coevolution method for solving warehousing operation optimization problem with aisles

A technology for job optimization and co-evolution, applied in logistics, biological models, instruments, etc., to achieve the effect of taking into account breadth, improving diversity, improving solution accuracy and convergence efficiency

Active Publication Date: 2019-07-19
HENAN INST OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Yang et al. (Optimization of three-dimensional space path optimization for compound operations in stacker-type dense storage system, computer integrated manufacturing system, 2017) proposed an improved ant colony algorithm to solve the problem of three-dimensional space path optimization for compound operations in dense storage systems

Method used

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  • Multi-species coevolution method for solving warehousing operation optimization problem with aisles
  • Multi-species coevolution method for solving warehousing operation optimization problem with aisles
  • Multi-species coevolution method for solving warehousing operation optimization problem with aisles

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Embodiment 1

[0033] see figure 1 , the multi-species co-evolution method for solving the optimization problem of storage operations with aisles proposed by the present invention is characterized in that the following specific steps:

[0034] (1) Analyze the constraints existing on the storage site and the goals to be optimized, and abstract them into a mathematical model with constraints;

[0035] (2) Initialization parameters: overall maximum evolution algebra G_max , the maximum evolutionary algebra of each species G_pmax , evolutionary algebraic counter t , population size M, crossover probability p c , mutation probability p m , inertia weight , learning factor and , step length stepsize , the field of view of the artificial fish visual , number of attempts Try_number , crowding factor , to initialize the individual population of each species;

[0036] (3) global search, t = t +1;

[0037] (4) Local search, genetic algorithm, particle swarm algorithm and artific...

Embodiment 2

[0049] This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0050] The optimization model established in the step (1) is established based on the following considerations: optimizing the picking path of the stacker for the purpose of saving energy and reducing consumption. The goal to be optimized is to complete the given order picking operation in the shortest time, and its mathematical model is expressed as follows:

[0051]

[0052] in For the target to be optimized, O A collection of picking tasks for an order, R A collection of picking paths for the stacker, It is the time required for the stacker to pass through two storage positions to be picked continuously; Indicates whether the stacker passes through the storage space continuously in a certain path and location mark of Indicates the location Is it a subpath .

Embodiment 3

[0054] see figure 1 , the multi-species co-evolution method for solving the optimization problem of storage operations with aisles proposed by the present invention, its specific steps are as follows:

[0055] 1. Establish goals and establish optimization models

[0056] The multi-aisle storage operation optimization problem in this example storage operation has the following characteristics:

[0057] There are m relevant positions corresponding to a certain batch of orders, and their positions in the warehouse are denoted as p 1 , p 1 ,...,p m . , the storage layout of this type is as follows figure 2 shown.

[0058] Let the average speed of horizontal movement and vertical movement of the stacker be respectively , , and the horizontal and vertical movements are independent of each other, the maximum loading capacity of the stacker is C , and the length, width and height of each storage bin are respectively recorded as L , W and H . The roadway width is W 1...

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Abstract

The invention discloses a multi-species coevolution method for solving the storage operation optimization problem with aisles, and belongs to the field of intelligent logistics and storage equipment.According to the method, the transverse aisles are added on the basis of traditional warehousing, the warehousing operation optimization model with the aisles is established, and the model is solved.In view of the defects that the existing solving technology is easy to premature and low in convergence speed, the invention provides a multi-species co-evolution optimization method based on the joint participation of a genetic algorithm, a particle swarm algorithm and an artificial fish swarm algorithm, i.e., through a multi-species competition symbiotic predation strategy based on a learning mechanism, the environment adaptability of each species can be enhanced; by introducing a variation mechanism, the population diversity of all species is synergistically improved, so that the evolutioncapability of a single species is improved, and meanwhile, the global optimization capability and the solving efficiency of the algorithm are also improved. According to the method, the operation efficiency of overall storage is improved, and the logistics storage can be promoted to be transformed and upgraded to be intelligent and green.

Description

technical field [0001] The invention belongs to the field of intelligent logistics, and further relates to a multi-species co-evolution method for solving the optimization problem of storage operations with aisles. Background technique [0002] As the carrier of commodity circulation and the blood of e-commerce, warehousing logistics has become an indispensable part of enterprise development. Warehousing operation efficiency is the main index to measure the logistics storage system. Therefore, to further improve the efficiency of warehousing operations and meet the requirements of customers for delivery time has become a thorny problem for traditional manufacturing enterprises to solve. In this context, the research on the optimization problem of multi-aisle storage operations, which is different from traditional automated three-dimensional storage, has a good reference for improving the efficiency of storage operations. Therefore, a multi-species solution to the optimizatio...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/08G06N3/00
CPCG06Q10/04G06Q10/087G06N3/006
Inventor 杨文强孔晓红苏建修杜家熙付广春徐君鹏张素君郭昊
Owner HENAN INST OF SCI & TECH
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