Robust and stable multi-task manufacturing scheduling with uncertainties using a two-stage extended genetic algorithm.

In cloud manufacturing (CMfg), unexpected uncertainties can occur in real-world manufacturing processes that could make the predetermined schedule infeasible. A new multi-objective proactive method is proposed in this situation to evaluate the proactive schedule. A novel two-stage extended genetic algorithm (2S-EGA) is proposed to generate proactive schedules that consider service interruptions. The experimental results confirmed that the obtained proactive schedule produces great performance when applied to multi-task scheduling problems with service interruptions. Furthermore, the results also showed that the proactive schedule obtained by the proposed approach is more robust and stable than other baseline algorithms taken from the literature.