Minimizing Mean Completion Time in Flowshops with Random Processing Times

RACT : We consider the two-machine flowshop scheduling problem where jobs have random processing times are bounded within certain intervals. The objective is to minimize total completion time of all jobs. The decision of a solution for the problem has to be made based on the lower and upper bounds on job processing times since this only information available. The problem is NP-hard since the special case when the lower and upper bounds are i.e., the deterministic case, is known to be NP-hard. Therefore, a reasonable approach is to come up with well ing heuristics. We propose eleven heuristics which utilize the lower and upper bounds on job processing times on the Shortest Processing Time (SPT) rule. The proposed heuristics are compared through randomly generated he computational analysis has shown that the heuristics using the information on the bounds of job processing times machines perform much better than those using the information on one of the two machines. It has also shown that the proposed heuristics performs as the best for different distributions with an overall average percentage error of n one.