Production Scheduling under Disruption
The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but rather a part of a company business case. In this research, we (Hasan, Sarker and Essam) have first solved JSPs using an Improved Memetic Algorithm (IMA). We have studied JSPs under sudden machine breakdown scenarios which introduces a risk of not completing the jobs on time. We have extended IMA to deal with the changed situation, and developed a simulation model to analyze the risk using a job order-anddelivery scenario. So the paper has made three sequential contributions: job scheduling under ideal condition, rescheduling under machine breakdown, and risk analysis for a production business case. The extended algorithm provides better understanding and results than the existing algorithms, the rescheduling shows a good way of recovering disruptions, and the risk analysis shows an effective way of maximizing return under such situations. A part of this research has been reported in a paper published in the International Journal of Production Research in 2011.