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.

One approach to supply chain coordination is early order commitment, whereby a retailer commits to purchase a fixed-order quantity at a fixed delivery time before demand uncertainty is resolved. In this paper, we develop an analytical model to quantify the cost savings of an early order commitment in a two-level supply chain where demand is serially correlated. A decision rule is derived to determine whether early order commitment will benefit the supply chain, and accordingly to determine the optimal timing for early commitment. Our results indicate that the supply chain would experience greater savings from early order commitment when-(a) the inventory item receives less value-added activities at the retailer site; (b) the manufacturing lead time is short; (c) demand correlation over time is positive but weak; or (d) the delivery lead time is long (if a condition exists). We also propose a rebate scheme for the supply chain partners to share the gains of practicing early order commitment.

This paper deals with the single machine scheduling problem with multiple financial resource constraints. It: is shown that the problem can be reduced to the two machine flow shop scheduling problem if the financial resources arrive uniformly over time, and it is also shown that the LPT (Largest Processing Time) rule generates an optimal solution to the problem if the financial resources are consumed uniformly by all the jobs. Hence there exist polynomial algorithms for these two special cases of the problem.

This paper compares the performance of a new improved heuristic (STIL) proposed by Coleman and McKnew (1991) against those of the Wagner-Whitin (WW), cost-modified Wagner-Whitin (MWW) and some simple lot-sizing rules found to perform well by Zhao et al. (1995). It also investigates the impact of the lot-sizing rules on the selection of the parameters for freezing the master production schedule (MPS). The experimental settings include forecasting, master production scheduling and material requirements planning under a rolling time horizon. The result of the study shows that the STIL heuristic, the WW rule and the MWW rule do not really outperform the simple cost-modified Silver-Meal (MSM) and the Silver-Meal/Lot-for-Lot (SM/LFL) even though they require substantially higher computation time. The study also shows that the selection of the parameters for freezing the MPS is not significantly influenced by the selection of the lot-sizing rules.

A computer model is built to simulate master production scheduling activities in a capacitated multi-item production system under demand uncertainty and a rolling time horizon. The output from the simulation is analyzed through statistical software. The results of the study show that forecasting errors have significant impacts on total cost, schedule instability and system service level, and the performance of forecasting errors is significantly influenced by some operational factors, such as capacity tightness and cost structure. Furthermore, the selection of the master production schedule freezing parameters is also significantly influenced by forecasting errors. The findings from this study can help managers optimize their production plans by selecting more reasonable forecasting methods and scheduling parameters, thus improving the performance of roduction systems.