Flexible job-shop scheduling optimization method

A technology of workshop scheduling and flexible operation, applied in control/regulation systems, instruments, comprehensive factory control, etc., can solve the problems of no substantial improvement, easy to fall into local optimum, low accuracy of firefly algorithm, etc. Stability, the effect of improving the global search ability

Active Publication Date: 2018-03-20
SOUTHWEST JIAOTONG UNIV
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  • Abstract
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Problems solved by technology

[0003] However, in the process of application, it is found that the single firefly algorithm has shortcomings such as low precision and easy to fall into local optimum, so some methods are used to improve the search performance of the firefly algorithm and applied to discrete optimization problems. Although it has produced certain effects, but not substantially improved

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0069] A JSP problem of 5×5 scale is adopted, that is, 5 workpieces are processed on 5 machines. Each workpiece includes four processes, and the processing sequence is o i1 , o i2 , o i3 , o i4 . where o i1 and o i3 For processing operations, you can only choose to operate on the processing machine; o i2 and o i4 For detection operation, you can only choose to operate on the detection machine. The distribution of machines is that machines 1, 2, and 4 are processing machines, and machines 3 and 5 are testing machines. The constraints of machines and processes are shown in Table 1.

[0070] Table 1 Machine process constraints of 5×5 scale JSP

[0071]

[0072]

[0073] For a more intuitive expression, the number of processes that can be processed on each machine is as follows figure 2 shown.

example 2

[0075] Example 2 adopts a JSP problem with a scale of 10×10, that is, 10 workpieces are processed on 10 machines. The process constraints of each workpiece are the same as those in Example 1, and in Example 2. The first 6 machines are processing machines, and the last 4 machines are testing machines. The number of processes that can be processed on each machine is as follows: image 3 shown.

Embodiment 3

[0077] Example 2 adopts a JSP problem with a scale of 20×16, that is, 20 workpieces are processed on 16 machines. The process constraints of each workpiece are the same as those in Example 1, and in Example 3. The first 10 machines are processing machines, and the last 6 machines are testing machines. The number of processes that can be processed on each machine is as follows: Figure 4 shown.

[0078] specific operation

[0079] Establish the optimized mathematical model of example 1 according to formula (1)-(4):

[0080] Objective function:

[0081]

[0082] Constraint equation:

[0083] S k ≥0,P k ≥0 k=O 1 ,O 2 ,O 3 ,O 4 ;

[0084] S k -S k-1 ≥P k-1 k=O 1 ,O 2 ,O 3 ,O 4 ;

[0085] S k -S j ≥P k or S j -S k ≥P j (k,j)∈E h ,h∈(1,2,...,5)

[0086] The operation methods of the other two examples are the same. After obtaining the mathematical model, according to the principle and basis of initial parameter selection, the firefly population size N...

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Abstract

The invention relates to a flexible job-shop scheduling optimization method, which applies the Metropolis criterion and the sinusoidal adaptive step length to a firefly algorithm so as to optimize andsolve a discrete problem. On the basis of building a mathematical model, an initial solution population of a discrete combination problem is randomly generated, then local search in an individual domain is performed according to the Metropolis criterion in simulated annealing to generate a new individual, the internal energy difference between the new individual and the original individual is calculated, the new individual is accepted according to a certain probability, and global search is performed on each generation by using the discrete firefly algorithm with the sinusoidal adaptive steplength until an optimal solution is searched. The method can better search an optimal solution of the FJSP (Flexible Job-Shop Scheduling Problem) in the global space and has better search precision, search efficiency and stability, thereby having important significance and significant engineering practical application values for solving discrete problems such as job-shop scheduling.

Description

technical field [0001] The invention relates to the technical field of intelligent optimization of discrete combination problems, especially the technical field of flexible job shop scheduling optimization. Background technique [0002] Typical combinatorial optimization problems include Traveling Salesman Problem (TSP), Job-Shop Scheduling Problem-JSP, Flow-shop Scheduling Problem, 0-1 Knapsack Problem (Knapsack Problem) , Bin Packing Problem (Bin Packing Problem) and so on. Among them, the traditional JSP mainly determines the processing sequence of the job shop, while the flexible job shop scheduling problem (Flexible Job-Shop Scheduling Problem-FJSP) is an important content of the development of advanced manufacturing system operation research technology, management technology and optimization technology. At the same time, determining which machine tool each process is assigned to makes the problem more complicated. The typical combinatorial optimization problems of th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418
CPCG05B19/41865G05B2219/32252
Inventor 张剑陈浩杰邹益胜付建林沈梦超王爽徐修立
Owner SOUTHWEST JIAOTONG UNIV
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