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A Self-Adjusting Search Domain Method-Based Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem.

Bin Li^1,2,3, Xuewen Xia^3,4

¹College of Computer, Minnan Normal University, Zhangzhou 363000, China.

Computational Intelligence and Neuroscience

|October 20, 2022

Summary

This summary is machine-generated.

This study introduces a novel hybrid algorithm, the global and local search with reinitialization (GLRe)-based genetic algorithm (GA), to efficiently solve the complex flexible job shop scheduling problem (FJSP) and minimize makespan.

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Area of Science:

Operations Research
Computer Science
Artificial Intelligence

Background:

The flexible job shop scheduling problem (FJSP) is a computationally challenging nondeterministic polynomial (NP) problem.
Genetic algorithms (GAs) have shown significant promise in addressing complex scheduling challenges.
Minimizing makespan is a critical objective in FJSP for improving production efficiency.

Purpose of the Study:

To propose a novel hybrid algorithm, GLRe-based GA, for solving the FJSP.
To effectively minimize the makespan in flexible job shop environments.
To enhance the performance of genetic algorithms in solving NP-hard scheduling problems.

Main Methods:

A hybrid algorithm combining global and local search with reinitialization (GLRe) is developed.
A double-layer chromosome representation is employed for convenient genetic operations.
Two initialization strategies, CRO and 6D-VSP, are utilized to generate high-quality initial populations and reduce search space.
A reinitialization strategy is incorporated to maintain population diversity during evolution.

Main Results:

The proposed GLRe-based GA demonstrates effective performance on benchmark FJSP datasets.
The algorithm successfully minimizes makespan for the flexible job shop scheduling problem.
Experimental results validate the accuracy and effectiveness of the GLRe approach.

Conclusions:

The GLRe-based GA offers an accurate and effective solution for the FJSP.
The hybrid approach enhances the capabilities of genetic algorithms for complex scheduling tasks.
The proposed method provides a valuable tool for optimizing flexible job shop operations.