ABSTRACT The Genetic Algorithm (GA) is among the most widely applied metaheuristics in the scheduling literature, yet a comprehensive review exploring its developments is lacking. This study employs Main Path Analysis (MPA) to explore the development trajectories of GA in the scheduling literature. The study also delves into the development patterns of GA across scheduling subfields, offering a thorough understanding of the evolution of applications and their influencing factors based on Cluster Analysis (CA). Further investigations based on keyword analysis are provided to explore the major development areas: Flexible Job Shop Scheduling, Flexible Flow Shop Scheduling, Parallel Machine Scheduling, Job Shop Scheduling, and Permutation Flow Shop Scheduling Problems. The review indicates that GA has made relatively greater progress in Job Shop and Flexible Job Shop Scheduling Problems. Applications of GA in Assembly Shop Scheduling and Open Shop Scheduling Problems remain underexplored. Moreover, the review found that the GA literature may benefit from computational developments based on clustering techniques for solving intractable scheduling problems with interacting features.
Ying et al. (Mon,) studied this question.