The scheduling problems studied in this paper concern the two-stage no-wait flowshops with parallel machines under the objective function of the minimization of the maximum completion time. A new heuristic algorithm, i.e. the minimum deviation algorithm, is developed to solve the problems. In order to evaluate the average case performance of the algorithm, we design numerical experiments to compare the effectiveness of the algorithm with that of the other approximation algorithms. Extensive simulations are conducted under different shop conditions, and the results statistically show that the minimum deviation algorithm performs well under most of the situations.

This paper investigates the impact of freezing the master production schedule (MPS) in multi-item single-level systems with a single resource constraint under demand uncertainty. It also examines the impact of environmental factors on the selection of MPS freezing parameters. A computer model is built to simulate master production scheduling activities in a multi-item system under a rolling time horizon. The result of the study shows that the parameters for freezing the MPS have a significant impact on total cost, schedule instability and the service level of the system. Furthermore, the selection of freezing parameters is also significantly influenced by some environmental factors such as capacity tightness and cost structure. While some findings concerning the performance of MPS freezing parameters without capacity constraints can be generalised to the case of limited capacity, other conclusions under capacity constraints are different from those without capacity constraints.