Many-objective flexible job-shop scheduling based on a loose non-dominated sorting genetic algorithm

ABSTRACT

To address the challenges of solving the many-objective flexible job-shop scheduling problem, this study proposes a loose non-dominated sorting genetic algorithm III (LNSGA-III), an enhancement of the non-dominated sorting genetic algorithm III (NSGA-III). First, a loose dominance principle is proposed to overcome the shortcomings of low selection pressure and slow convergence under the Pareto dominance principle. Next, a novel crossover operator without repair, named improved order crossover, is presented to fully preserve the characteristics of exchanged operations and enhance the exploration capability of the algorithm. Experimental studies involve testing algorithms on some typical scheduling instances with six simultaneously optimized objectives. The primary metric for algorithm comparison is the hypervolume, with additional investigation for statistical significance. Other metrics, including coverage, convergence and diversity, are also used for comparison. The experimental results demonstrate the effectiveness of the proposed enhancements, showcasing the significant superiority of the algorithm over some state-of-the-art alternatives.

KEYWORDS:

• Flexible job-shop scheduling
• many-objective
• loose dominance principle
• improved order crossover
• NSGA-III

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings are available from the corresponding author upon reasonable request.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers U22A6001, 51935009], Key Research and Development Program of Zhejiang Province [grant number 2021C01008] and High-Level Talent Special Support Plan of Zhejiang Province [grant number 2020R52004].