Flexible job shop scheduling with stochastic machine breakdowns by an improved tuna swarm optimization algorithm

In job-shop production environments, machine breakdowns are a significant factor in reducing productivity. Existing approaches seldom consider algorithm improvement and rescheduling scheme design in an integrated manner, and lack stability considerations. This paper addresses the flexible job shop scheduling problem with random machine breakdowns, aiming to produce a stable rescheduling scheme that minimizes a combined index of maximum completion time and stability. The paper innovatively applies the tuna swarm optimization algorithm to the flexible job shop scheduling problem, proposing an efficient and superior improved version called the genetic chaos levy nonlinear tuna swarm optimization (GCLNTSO) algorithm. Three stability metrics are designed to guide the generation of efficient and stable rescheduling schemes. A rescheduling scheme is proposed that combines right-shift rescheduling with complete rescheduling. The proposed scheme is benchmarked against Brandymalter and Kacem's problems, and compared with other algorithms from the literature. The results demonstrate that the GCLNTSO algorithm outperforms other algorithms in terms of both performance and stability.