Optimizing Makespan and Minimizing Risk in Job Shop Scheduling: A Review

The job shop deals with customized product deliverables because of which the product mix is high, but the volume is low. For smooth functioning of the Job shop facility, it is important to plan for multiple factors. The review deals with the challenges that come with job shop scheduling (JSP) and provides predictive schedules to optimize some of the performance measures like makespan, makespan risk, stability risk and tardiness. Due to the high variety of products and unpredictable demand the scheduling complexity is substantial. This complexity further increases when we consider unpredictable machine breakdowns, increased setup times and absence of manpower. In order to come up with a robust and reliable scheduling plan the study will mainly focus on using buffered strategies (additional idle time), use of an Artificial Neural Network to correctly estimate the variable solutions for makespan and tardiness and using genetic algorithms to reduce and mitigate risk.