Two stage approach to address the flexible job shop scheduling problem using an evolutionary algorithm considering random machine breakdowns

Abstract

In practical situations, uncertainties are an integral part of a production process. These uncertainties cause the planned schedule to be disrupted and need to be accounted for during the process of scheduling. Hence, this work addresses the flexible job shop scheduling problem considering random machine breakdown. The objective is to generate a robust and stable predictive schedule employing a Pareto based backtracking search algorithm minimizing makespan and stability. A two stage approach is employed to address the problem assuming a single machine breakdown. The first stage minimizes make span which is the primary objective followed by the second stage which considers machine breakdowns while minimizing the bi-objective function, generating robust and stable schedules. Kacem and Brandimarte benchmark instances are employed to compare the performance of the proposed approach under various breakdown conditions with other approaches from literature. Experimental results indicate that the proposed approach is superior compared to other approaches in generating robust and stable predictive schedules.