Multi-objective job shop scheduling method based on hybrid hyena algorithm

Through a hybrid hyena algorithm combining SHO and VDO algorithms, the problem of local optimal solutions in multi-objective operation workshop scheduling is solved, and a better global optimal solution is achieved, which is suitable for the scheduling needs of flexible open operation workshops.

CN114565141BActive Publication Date: 2025-03-28WUHAN UNIV OF TECH
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Patent Information

Application Number
CN202210148044.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-02-17
Publication Date
2025-03-28
Estimated Expiration
2042-02-17

AI Technical Summary

Technical Problem

When solving the scheduling problems in flexible open operation workshops, the existing multi-objective operation workshop scheduling methods are difficult to effectively avoid local optimal solutions, and lack the ability to global optimal solutions when processing factors such as sequence dependence, transportation time and uncertain processing time.

Method used

A multi-objective work workshop scheduling method based on hybrid hyena algorithm is adopted, and the SHO algorithm is combined with the VDO algorithm to improve the local optimal solution, enhance the local search and global search capabilities of the algorithm. Through initialization processing, fitness calculation, local search and global search, a better global optimal solution is obtained.

Benefits of technology

In the flexible open operation model, sequential dependence, transportation time, preventive maintenance and uncertain processing time are effectively considered, which significantly improves the global optimal solution quality of scheduling and avoids the trap of local optimal solution.

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Abstract

The present application provides a multi-objective job shop scheduling method based on a hybrid hyena algorithm, including: encoding workpieces based on processing stages and processing procedures; decoding workpieces to obtain the processing order of each parallel machine for the workpieces; inputting workpiece processing parameters and spotted hyena algorithm parameters to initialize the spotted hyena population; calculating the fitness value of spotted hyena individuals; defining the optimal solution, selecting some spotted hyenas to approach prey, and selecting some spotted hyenas to explore; when the positions of spotted hyenas are updated, performing fast non-dominated sorting, and redefining the optimal solution; combining the VDO algorithm to perform sufficient local search to avoid the spotted hyena algorithm from falling into the local optimum; when the spotted hyena algorithm reaches the maximum number of iterations, the optimal solution after optimization is obtained, and multi-objective scheduling is performed. Improvements have been made in the model and algorithm, combining the SHO algorithm with the VDO algorithm to enhance the local search and global search capabilities of the algorithm.
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