Flexible job shop batch dynamic scheduling optimization method

A technology of flexible operation and optimization method, applied in the direction of electrical program control, comprehensive factory control, etc., can solve problems such as insufficient flexibility/intelligence, insufficient response time, and reduced efficiency

Inactive Publication Date: 2019-01-25
CRRC QINGDAO SIFANG CO LTD +1
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

At the same time, through our research, the existing literature to solve the dynamic scheduling problem of the job shop all adopts reactive priority rules or meta-heuristic algorithms, which may have insufficient flexibility/intelligence (

Method used

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  • Flexible job shop batch dynamic scheduling optimization method
  • Flexible job shop batch dynamic scheduling optimization method
  • Flexible job shop batch dynamic scheduling optimization method

Examples

Experimental program
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[0071] The present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0072] Attached figure 1 Learn the schematic diagram for Q; figure 2 Learning flowchart for Q;

[0073] In the example used in this patent, the parameters in the initialization Q learning are learning rate α=0.5, discount factor γ=0.9, control factor a=0.01, and greedy search threshold ε=0.95.

[0074] 1. Single process BDFJSP experiment

[0075] The input required for this experiment is shown in Table 2, where each parameter satisfies the uniform distribution of the range in the table.

[0076] Table 2 Single process BDFJSP input table

[0077]

[0078] Where NOM represents the number of workpieces processed on machine m

[0079] According to the input, this experiment uses Q learning and three priority rules to calculate the results 10 times respectively, as shown in Table 2. The learning process diagram and the solution distribution line diagram are as fol...

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Abstract

The invention discloses a flexible job shop batch dynamic scheduling optimization method. According to the method, a production mode of batch scheduling and the dynamic factor of machine malfunction are introduced into a flexible job shop scheduling problem; relevant constraints are extended as needed with delivery period constraints considered, a BDFJSP problem model adopting minimizing delay time as an objective is established; on the basis of the problem model, a Q learning mode is adopted to train an agent, so that a solution can be obtained, and a reasonable state set, an action set and areward function are established according to the problem; and in order to better balance exploration and utilization in the Q learning, epsilon greedy search is integrated into the Q learning, and areasonable epsilon attenuation function is set. The method is of great significance and engineering practical application value for solving the job scheduling of flexible shops and even discrete optimization problems similar to the job scheduling of the flexible shops.

Description

technical field [0001] The invention relates to the technical field of intelligent optimization of discrete combination problems under dynamic working conditions represented by the technical field of flexible job shop scheduling optimization. Background technique [0002] The flexible job shop scheduling problem is one of the most difficult problems in the manufacturing system. Its complex combination of processing machines and workpieces leads to high computational complexity and is a very strong NP-hard problem. In order to optimize within a reasonable time range, scholars are more inclined to use meta-heuristic intelligent algorithms to obtain approximate solutions (satisfactory solutions) to the optimal solution, thus developing and improving many intelligent algorithms, but most of these algorithms are For static FJSP issues. Many unpredictable dynamic factors often occur in the actual process, such as machine failure, arrival of new workpieces, etc., so the above algo...

Claims

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Application Information

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IPC IPC(8): G05B19/418
Inventor 贾广跃陈浩杰韩磊张剑杨龙付建林
Owner CRRC QINGDAO SIFANG CO LTD
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