Production scheduling using a hybrid approach based on genetic algorithm and convergent random search

Abstract

The flexible job shop scheduling problem (FJSSP) is a highly complex combinatorial optimization problem widely encountered in modern manufacturing systems. Its complexity arises from the simultaneous determination of operation sequencing and machine assignment, making it significantly more challenging than the classical job shop scheduling problem (JSSP). Recent advances in hybrid metaheuristics and intelligent optimization methods have improved solution quality; however, achieving an effective balance between global exploration and local exploitation remains a critical challenge. In this paper, a novel hybrid metaheuristic approach combining a genetic algorithm (GA) and convergent random search (CRS) is proposed to address the FJSSP. The proposed method exploits the global search capability of GA to explore the solution space, while CRS is employed as an adaptive local refinement mechanism applied to elite individuals. This hybridization strategy enhances convergence speed and avoids premature stagnation. Extensive computational experiments are conducted on well-known benchmark instances, including Brandimarte and Kacem datasets. The results indicate that the proposed GA–CRS approach significantly improves the makespan compared to classical GA and PSO based methods. In addition, the algorithm exhibits faster convergence behavior, reaching high-quality solutions in fewer iterations. Statistical analysis using non-parametric tests confirms the superiority of the proposed method. These findings demonstrate that the proposed hybrid GA–CRS algorithm provides a robust and efficient optimization framework for solving large-scale and complex FJSSP instances, outperforming several state-of the-art approaches.