The lot-sizing problems address the issue of determining the production lot-sizes of various items appearing in consecutive production stages over a given finite planning horizon. In general capacitated lot-sizing problems, the product structure can be a general acyclic network, the capacity constraints can be very sophisticated, and all the known parameters can be time-varying. This paper proposes heuristic genetic algorithms for these problems by designing a domain-specific encoding scheme for the lot-sizes and by providing a heuristic shifting procedure as the decoding schedule. The main contribution of these genetic algorithms is the presentation technique that encodes only the binary variables for the setup patterns but derives other decision variables by making use of the problem-specific knowledge. Some results from the computational experiments are also given.

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.

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.