Robust Optimization on Unrelated Parallel Machine Scheduling With Setup Times

The parallel machine scheduling problem has been a popular topic for many years due to its theoretical and practical importance. This paper addresses the robust makespan optimization on unrelated with sequence-dependent setup times, where processing times are uncertain, only knowledge is time intervals they take values from. We propose model min-max regret criterion formulate this problem. To solve problem, we prove that worst-case scenario maximum given solution belongs finite set of extreme scenarios. Based analysis, procedure obtain proposed an enhanced evaluation method (ERE) designed accelerate process, which great significance improve efficiency algorithm. A multi-start decomposition-based heuristic algorithm (MDH) based analysis properties Computational experiments conducted justify performance robustness these methods. Note Practitioners—Various uncertainties may occur in production brings challenges operations management. schedule factories make full use capacity deal abnormalities. study motivated by R&D assembly task encountered high-end equipment manufacturing factory each job distribution also unknown limited information. In study, consideration uncertain job-processing time, view labor groups different skill levels as machines build (min-max regret) so reduce makespan. An developed solution, can be applied practice release schedulers from burdensome work provide high-quality schedules complicated environment.