A dispatching algorithm for parallel machines with rework processes

Abstract

This paper presents a parallel machine scheduling problem with rework probabilities, due-dates and sequencedependent setup times. It is assumed that rework probability for each job on a machine can be given through historical data acquisition. Since the problem is NP-hard in the strong sense, a heuristic algorithm is presented, which finds good solutions. The dispatching algorithm named MRPD (minimum rework probability with duedates) is proposed in this paper focusing on the rework processes. The performance of MRPD is measured by the six diagnostic indicators: total tardiness, maximum lateness, mean flow-time, mean lateness, the number of reworks and the number of tardy jobs. A large number of test problems are randomly generated to evaluate the performance of the proposed algorithm. Computational results show that the proposed algorithm is significantly superior to existing dispatching algorithms for the test problems.

On the n-job, m-machine permutation flow shop scheduling problems with makespan criterion and rework

“…Term (10) shows that the start time for kth job in sequence (for 2  k ) on a machine (for 2  i ) equals the bigger value between finish time for kth job on the previous machine and finish time for the preceding job on the current machine. Terms (11) and (12) are used to make unique assignments for jobs and their positions in sequence to calculate the processing times in Term (8). Term (13) determines the nature of decision variables of the model.…”

“…A linear programming model was suggested to handle this problem. Kang et al [8] studied a dispatching algorithm to solve a parallel machine scheduling problem with rework possibilities. The performance of the proposed algorithm was evaluated in terms of the six performance indicators: total tardiness, maximum lateness, mean flow time, mean lateness, the number of reworks and the number of tardy jobs, respectively.…”