Rescheduling job shops under random disruptions

In this work, an algorithm for rescheduling the affected operations in a job shop is presented and its performance, with respect to measures of efficiency and stability, is compared with that of Total Rescheduling and Right-Shift Rescheduling. Algorithms for all three methods are implemented in a rescheduling program which, based on keyed-in disruption information, produces three new schedules and outputs their related performance measures. A designed factorial experiment is performed on benchmark scheduling problems to study the effect of different rescheduling methods as well as various problem characteristics and disruption scenarios on the performance of the new schedules. The results demonstrate that the Affected Operations Algorithm overcomes the disadvantages associated with other rescheduling methods.