A two-level evolutionary algorithm for dynamic scheduling in flexible job shop environment

In many industrial real-world environments, scheduling necessitates continual reactive adjustments due to unpredictable perturbations, leading to the dynamic transformation of predefined static schedules. In this paper, we introduce a new framework named a two-level evolutionary algorithm (2LEA) as a comprehensive approach for addressing the dynamic flexible job shop scheduling problem. The 2LEA is based on a bi-level optimization design, where the upper level is dedicated to solving the general flexible job shop scheduling problem, and the lower level is used as a new evolutionary operator guided by a probability rate in the upper level, focusing on the optimization of operation sequences. This framework is capable of handling four dynamic events job insertion, job cancellation, machine breakdown, and job replacement using a predictive-reactive rescheduling strategy. By addressing the previously unexplored dynamic event of job replacement, this paper fills a significant gap in the literature and opens avenues for further research. Extensive computational experiments conducted on well-known benchmark instances from the Brandimarte and Hurink datasets demonstrate the effectiveness and efficiency of our proposed scheduling algorithm. Our results showcase the superior performance of 2LEA over state-of-the-art approaches in terms of solution quality, affirming its potential as a leading solution for both static and dynamic scheduling challenges.