Narrow Results

In this paper, we consider the rescheduling problem for jobs on a single machine with release dates to minimize total sequence disruption under a limit on the makespan. We show that the considered problem can be solved in polynomial time. Consequently, the rescheduling problem for jobs on a single machine with release dates to minimize makespan under a limit on the total sequence disruption can also be solved in polynomial time.

We study the Pareto optimization scheduling on an unbounded parallel-batch machine with jobs having agreeable release dates and processing times for minimizing makespan and maximum cost simultaneously. The jobs considered in this paper are of two types: batch jobs and drop-line jobs. For batch jobs, the completion time of a job is given by the completion time of the batch containing this job. For drop-line jobs, the completion time of a job is given by the starting time of the batch containing this job plus the processing time of this job. For both of batch jobs and drop-line jobs, we present polynomial-time algorithms for finding all Pareto optimal points.

In this paper, the single machine total weighted completion time scheduling problem is studied. The jobs have nonzero release time and processing time increases during the production due to the effect of deterioration on the machine. An operator sets up the machine and manually loads the job in the machine and unloads it at the end of the working time. The setup time and the removal time are influenced by the ability of the worker due to his work experience and learning capacity. Heuristic algorithms are proposed to solve the scheduling problem, and their efficiency is evaluated on a wide benchmark.