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Method for solving flexible job shop scheduling based on hybrid genetic algorithm

A hybrid genetic algorithm and shop scheduling technology is applied in the field of flexible job shop scheduling based on hybrid genetic algorithm, which can solve the problems of unstable solution results and low solution accuracy.

Pending Publication Date: 2020-11-20
XI'AN POLYTECHNIC UNIVERSITY
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  • 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 a hybrid genetic algorithm, which solves the problems of unstable solution results and low solution accuracy in the flexible job shop scheduling problem existing in the prior art

Method used

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  • Method for solving flexible job shop scheduling based on hybrid genetic algorithm
  • Method for solving flexible job shop scheduling based on hybrid genetic algorithm
  • Method for solving flexible job shop scheduling based on hybrid genetic algorithm

Examples

Experimental program
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Effect test

Embodiment 1

[0112] The 4×5 in the Kacem benchmark example of flexible job scheduling is solved. The processing information is shown in Table 1. The algorithm parameters are set as follows: the population size is 200, the maximum number of iterations is 100, the mutation probability is 0.1, and the crossover probability is 0.7:

[0113] Table 1 Example 1 processing task information table

[0114]

[0115] It is solved by the method of the present invention to obtain its WSA-GA evolutionary convergence curve such as figure 2 shown by figure 2 It can be seen that the method of the present invention can converge to 11 in 25 generations, indicating that the method of the present invention can achieve rapid convergence when solving small-scale problems, and 11 is the known optimal result of the Kacem4×5 calculation example, which corresponds to the scheduling result Gantt chart such as image 3 As shown, the Gantt chart is a specific description of the above results. The Gantt chart of ...

Embodiment 2

[0117] The 8×8 in the Kacem benchmark example of flexible job scheduling is solved. The processing information is shown in Table 2. The algorithm parameters are set as follows: the population size is 200, the maximum number of iterations is 200, the mutation probability is 0.1, and the crossover probability is 0.7:

[0118] Table 2 Example 2 processing task information table

[0119]

[0120]

[0121] It is solved by the method of the present invention to obtain its WSA-GA evolutionary convergence curve such as Figure 4 shown by Figure 4 It can be seen that the algorithm can converge to 14 in the 71st generation, and 14 is the known optimal result of the Kacem8×8 calculation example, which shows that the method of the present invention can achieve convergence quickly and the solution result is relatively stable when solving medium-scale problems, and can be used in In the actual enterprise production scheduling problem, the Gantt chart of the corresponding scheduling...

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Abstract

The invention discloses a method for solving flexible job-shop scheduling based on a hybrid genetic algorithm. The method is specifically implemented according to the following steps of 1, defining aflexible job-shop scheduling coding mode as a double-layer coding mode; 2, defining a fitness function, and taking the shortest time for solving and finishing machining as an optimization target; 3, initializing parameters and populations in a flexible workshop scheduling problem by adopting a genetic algorithm and a whale swarm algorithm, and randomly generating an initial population; 4, solvingflexible job shop scheduling through a genetic algorithm; 5, solving flexible job shop scheduling through a whale swarm algorithm; and 6, realizing iterative optimization of the whale swarm algorithmon the genetic algorithm through coding recombination, and outputting an optimal scheduling scheme. According to the method, the search depth of the algorithm is increased, and solving precision and solving stability are enhanced.

Description

technical field [0001] The invention belongs to the technical field of flexible job shop scheduling, and relates to a method for solving flexible job shop scheduling based on a hybrid genetic algorithm. Background technique [0002] In the traditional Job-shop Scheduling Problem (JSP), there is only one processing machine in a process, but parallel machines often appear in the actual production process of the enterprise workshop, which leads to the traditional job-shop scheduling problem. It cannot meet the actual needs of enterprises well, so the Flexible Job-shop Scheduling Problem (FJSP) emerges as the times require. FJSP is an extension of JSP. It is more flexible than JSP. While specifying the process, it also needs to allocate processing machines for each process. It is a complex NP (Non-deterministic Polynomial Complete, that is, polynomial complexity. non-deterministic) problem. It is precisely because FJSP has higher complexity and flexibility of solution, so it i...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06N3/00G06Q50/04
CPCG06Q10/04G06Q10/06312G06N3/006G06Q50/04Y02P90/30
Inventor 毋涛贾培豪齐琦李科崔璐任永亮潘华峰陈莉媛刘青青
Owner XI'AN POLYTECHNIC UNIVERSITY
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