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Large-scale flexible work workshop scheduling optimization method

A flexible operation and workshop scheduling technology, applied in genetic models, genetic laws, instruments, etc., can solve problems such as complex solution space, insufficient solution quality and solution time to meet requirements, etc.

Active Publication Date: 2018-03-30
SOUTHWEST JIAOTONG UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

With the increase of the number of workpieces and machines, the solution space of the JSP problem becomes complex, and the traditional solution algorithm cannot meet the requirements in terms of solution quality and solution time

Method used

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  • Large-scale flexible work workshop scheduling optimization method
  • Large-scale flexible work workshop scheduling optimization method
  • Large-scale flexible work workshop scheduling optimization method

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0066] Example 1 adopts a JSP problem with a scale of 230×18, that is, 230 workpieces are processed on 18 machines. Each workpiece includes four processes, and the processing sequence is Qi1, Qi2, Qi3, Qi4. Among them, Qi1, Qi2 and Qi3 are processing operations, which can only be operated on the processing machine; Qi4 is the detection operation, which can only be selected to be operated on the detection machine. The distribution of machines is that machines 1-15 are processing machines, and machines 16-18 are testing machines.

example 2

[0068] Example 2 adopts a JSP problem with a scale of 460×18, that is, 460 workpieces are processing machines in 18, and the distribution of processing machines and testing machines is the same as that of Example 1.

[0069] specific operation

[0070] First, group batches based on similar workpiece characteristics

[0071] This method first clusters and batches parts with similar processing technology, workpiece sizes within the same range, and the same blank material, and randomly forms different batches. Each workpiece has its own delivery date. The earliest delivery date is scheduled. For example, there are 10 conduit workpieces, and their main characteristics are shown in Table 1. The pipe diameter has two size ranges of 6mm-12mm and 14mm-38mm. The main process of the workpiece is replaced by A, B, C, D, E, F, and G. Among them, E and F can be processed by the same machine, and the process sequence is the same Under certain conditions, the process can be considered as ...

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Abstract

The invention provides a large-scale flexible work workshop scheduling optimization method. A large-scale production task is reorganized to be downscaled and then solving and optimization are performed by using the adaptive improved genetic algorithm. The method comprises the following concrete steps that (1) the workpieces of which the processing technology is similar, the workpiece dimension iswithin the same range and the blank material is the same are clustered and grouped for batching so as to reduce the problem solving scale; and (2) the initial parameter of the algorithm is set, the three-layer gene coding technology, the OBX crossover mode and the certain variation strategy are adopted, the crossover length is selected through combination of the simulation experiment and optimization and solving are performed by using the adaptive improved genetic algorithm. According to the method, the problem solving scale can be reduced and the solving speed can be improved; and the workpiece completion time and delay time can be reduced.

Description

technical field [0001] The invention relates to the technical field of intelligent optimization algorithms for discrete combination problems. Especially large-scale flexible job shop scheduling optimization methods. Background technique [0002] The workshop scheduling problem that does not exceed 20×50 (machine tool×workpiece) is a small and medium-scale scheduling problem, and the large-scale workshop scheduling problem has the following situations [LIANG Xu, WA NG Jia, H UANG Ming. New coding method formative production scheduling problem [J]. Computer Integrated Manufacturing Systems,2008,10(14):1974-1982]: ①When the number of workpieces is J>50, and the number of machines is M>20; ②When J≤50, M>20, J×M>1000 ③ When J>50, M≤20, J×M>1000. In the past 60 years, the production scheduling and scheduling problems of job shops are NP-hard problems. Many scholars have proposed many optimization methods with better solution effects, but the solution scale is...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06N3/12
CPCG06N3/126G06Q10/04G06Q10/06316
Inventor 邹益胜尹慢王爽石朝王若鑫张剑付建林
Owner SOUTHWEST JIAOTONG UNIV
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