Two-stage teaching-learning-based optimization method for flexible job-shop scheduling under machine breakdown

In the real-world situations, uncertain events commonly occur and cause disruption of normal scheduled activities. Consideration of uncertain events during the scheduling process helps the organizations to make strategies for handling the uncertainties in an effective manner. Therefore, in the present paper, unexpected machine breakdowns have been considered during scheduling of jobs in a flexible job-shop environment. The objective is to obtain lowest possible makespan such that robust and stable schedules are produced even if an unexpected machine breakdown occurs. The robust and stable schedules may help to decrease the costs associated with unexpected machine failures. The present work uses a two-stage teaching-learning-based optimization (2S-TLBO) method to solve flexible job-shop scheduling problem (FJSP) under machine breakdown. In the first stage, the primary objective of makespan is optimized without considering any machine breakdown. In the second stage, a bi-objective function considering robustness and stability of the schedule is optimized under uncertainty of machine breakdowns. In order to incorporate the machine breakdown data to basic FJSP, a non-idle time insertion technique is used. In order to generate effective robust and stable predictive FJSP schedules, a rescheduling technique called modified affected operations rescheduling (mAOR) is used. The Kacem's and Brandimarte's benchmark problems have been solved and compared with other algorithms available in the literature. Results indicate that TLBO outperforms other algorithms by generating superior robust and stable predictive schedules. Statistical analysis is carried out to test the significance difference of the results obtained by TLBO with other algorithms.