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A method for solving flexible job shop scheduling based on a hybrid whale swarm algorithm

A technology of flexible operation and workshop scheduling, applied in computing, computing models, instruments, etc., can solve problems such as slow convergence speed and low solution accuracy

Pending Publication Date: 2019-05-21
CHANGAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method for solving flexible job shop scheduling based on the mixed whale swarm algorithm, which solves the problems of low solution accuracy and slow convergence speed in the flexible job shop scheduling problem existing in the prior art

Method used

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  • A method for solving flexible job shop scheduling based on a hybrid whale swarm algorithm
  • A method for solving flexible job shop scheduling based on a hybrid whale swarm algorithm
  • A method for solving flexible job shop scheduling based on a hybrid whale swarm algorithm

Examples

Experimental program
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example 1

[0117] Example 1 is a 3×6 FJSP, and its processing task information is shown in Table 1. The parameters of the whale swarm algorithm are set as follows: the population size is 50, the dimension of the individual whale position vector is 30, ρ 0 set to 2, d max =32.86, the attenuation coefficient η is set to 0, and the maximum number of iterations M is 400:

[0118] Table 1 Example 1 processing task information table

[0119]

[0120] Use two-stage encoding based on random keys to encode machine allocation and process sequencing, then the length of the individual position vector is 30, and each element can take any value in [-3,3], use a computer to generate a 30-bit random number, and stored in a certain order, as shown in Table 2, where OP ij Indicates the jth process of workpiece i:

[0121] Table 2 Individual position vector map

[0122]

[0123] For embodiment 1, its WSA evolution convergence curve obtained by solving it is as follows figure 2 shown by figure...

Embodiment 2

[0125] In order to further verify the feasibility and superiority of the algorithm, select literature [1] (Fu Weiping, Liu Dongmei, Lai Chunwei, Wang Wen. Improved genetic algorithm based on multi-color set to solve multi-variety flexible scheduling problem[J]. Computer Integrated Manufacturing System, 2011 ,17(05):1004-1010.) to carry out the simulation, the specific processing information can be found in literature [1]. The parameters of the whale swarm algorithm are set as follows: the population size is 100, and the dimension of the individual whale position vector in example 1 is 108, ρ 0 set to 2, d max =104.96, the attenuation coefficient η is set to 0, and the maximum number of iterations M is 200. The evolutionary convergence curve of WSA is obtained as Figure 4 As shown, the optimal solution is 121 minutes, by Figure 4 Comparing the evolutionary convergence curve of WSA with the evolutionary curve of Example 2, it can be seen that the whale swarm algorithm can q...

Embodiment 3

[0127] In order to compare and verify the effect of the algorithm more comprehensively, the 8×8 embodiment in the Kacem benchmark problem of flexible job scheduling is selected to solve, and the calculation is performed 20 times. The parameters of the whale swarm algorithm are set as follows: the population size is 100, the dimension of the individual whale position vector is 54, ρ 0 set to 2, d max =117.57, the attenuation coefficient η is 0, and the maximum number of iterations M is 200, the WSA evolution convergence curve of embodiment 3 is as follows Figure 5 shown. Table 3 compares the results obtained by the whale swarm algorithm with the results obtained by the evolutionary algorithm, master-slave genetic algorithm, multi-stage genetic algorithm, and ant colony algorithm:

[0128] Table 3 Comparison of solution results of various algorithms for the Kacem 8×8 benchmark problem

[0129] method name

[0130] Aiming at the shortcomings of the traditional algor...

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Abstract

The invention discloses a method for solving flexible job shop scheduling based on a hybrid whale swarm algorithm, and the method comprises the steps: firstly defining the coding mode of flexible jobshop scheduling as two-stage random key coding, and then carrying out the mapping conversion through employing a conversion mechanism; Defining the shortest total processing time for solving the fitness function as an optimization target; Secondly, initializing parameters and whale population in the flexible job shop scheduling problem by adopting a whale group algorithm, wherein initialization isdivided into a sorting scheme of a random generation process and a genetic variation mode of an improved genetic algorithm to generate a better machine distribution scheme corresponding to the sorting scheme of the process, and then a better initial population is generated; Calculating the fitness value of each scheduling scheme, and finding and reserving the best scheduling solution; And finally, outputting the optimal scheduling solution and the corresponding fitness function value to obtain a solved optimal scheduling scheme, and solving the problems of low solution precision and low convergence rate in the existing flexible job shop scheduling problem.

Description

technical field [0001] The invention belongs to the technical field of flexible job shop scheduling, and in particular relates to a method for solving flexible job shop scheduling based on a mixed whale swarm algorithm. Background technique [0002] Flexible job-shop scheduling problem (Flexible Job-shop Scheduling Problem, FJSP), as an extended form of the traditional job-shop scheduling problem, adds the problem of selecting the flexible machining path of the workpiece, which is more difficult to solve and closer to the actual production. It is proved to be a combinatorial optimization problem with NP-hard characteristics. For the solution of this problem, various intelligent algorithms are currently its main means, and it has also become a research hotspot in the field of production scheduling. [0003] In the prior art, FJSP, in which the processing sequence is associated with the start time, designed a tabu search algorithm with domain specificity and solution space di...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06N3/00
Inventor 蔡宗琰栾飞李富康
Owner CHANGAN UNIV
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