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Aiming at the joint optimisation problem of uncertain distributed manufacturing and preventive maintenance (PM) for semiconductor wafers considering multi-energy complementary (UDMPM-MEC), a bi-level integrated scheduling model is constructed with interval makespan, total interval carbon emissions and total interval cost as the optimisation objectives. The upper model is used to obtain the joint optimisation scheduling scheme of distributed manufacturing and PM, and the lower model determines the energy allocation scheme on this basis. Given the problem characteristics, we propose an improved multi-objective whale swarm algorithm (IMOWSA). By designing jobs-to-factories allocation strategy, synchronous scheduling maintenance strategy, multi-energy complementary energy allocation decoding strategy, individual distance calculation strategy, multi-neighbourhood search strategy and deduplication strategy, the local and global search are effectively balanced, and the convergence precision is improved. A large number of simulation experiments and comparative analyses show that the IMOWSA algorithm is feasible and effective to solve this problem. [ABSTRACT FROM AUTHOR]

This study mainly considers the scheduling problems with learning effects, where the learning rate is a random variable and obeys a uniform distribution. In the first part, we introduce a single machine model with location-based learning effects. We have given the theoretical proof of the optimal solution for the five objective functions. In the second part, we study the problem with group technology. Both intra-group and inter-group have location-based learning effects, and the learning rate of intra-group jobs follows a uniform distribution. We also give the optimal ranking method and proof for the two problems proposed. [ABSTRACT FROM AUTHOR]

From the perspective of the surgical scheduling problem in emergency, this paper considers the fatigue effect and the truncated learning effect caused by medical staffs' long-time work, regards the surgical scheduling problem as the hybrid flow-shop scheduling problem and proposes a three-stage surgical scheduling model which includes preoperative, intraoperative and postoperative. The improved grey wolf optimization is used to solve the emergency surgical scheduling model, and the effectiveness of the model and the algorithm are tested through simulation experiments. The results show that the improved grey wolf optimization algorithm can solve the emergency surgical scheduling model more effectively and get better scheduling results. [ABSTRACT FROM AUTHOR]