Robustify a job-shop schedule according to the payload of the machine

Robust scheduling is an effective way to solve job-shop scheduling problem (JSP) under a dynamic and stochastic environment with uncertainties, which may lead to numerous schedule disruptions. Maintaining the optimization obtained, the robust schedule is more stable in execution. The vast majority of the researches on robust scheduling concentrate on the development of the robustness and stabilization with an optimization target that minimizes makespan. However, other optimization targets are needed in reality, and in this paper, we introduce another optimization target that maximizes the payload of the machine, which can reflect the capacity of machine. This work applies a two-step way to achieve a robust schedule. A baseline schedule in the deterministic environment is generated first, which pursues standard optimization criterions, and then the temporal buffers are inserted into the baseline schedule which make the schedule absorb some level of unexpected disruption without rescheduling. According to the payload of each machine which should be close to the maximum at any time in execution, we can get each size of the temporal buffers inserted into the different operations. An extensive experiment is used to show the relative performance of the proposed robust schedule. It is shown that the schedule is robust and stable under the stochastic disturbance coupling with minimal makespan tardiness and symmetry payload.