Methodology of rolling horizon scheduling under demand uncertainty
Production planning and scheduling play a prominent role in any kind of manufacturing activities that require resources input in terms of men, materials, machines and money (capital). It is a process of developing good relationship between market demands and production capacity in such a way that cu...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2006
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/3382/1/Methodology_Of_Rolling_Horizon_Scheduling_Under_Demand_Uncertainty.pdf http://eprints.utm.my/id/eprint/3382/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Production planning and scheduling play a prominent role in any kind of manufacturing activities that require resources input in terms of men, materials, machines and money (capital). It is a process of developing good relationship between market demands and production capacity in such a way that customers demand are satisfied and at the same time production activities are carried out in an economic manner. A reliable and efficient production planning and scheduling is essential in order to manage the production operations effectively. In a rolling horizon setting, the frequency with which a master production schedule (MPS) is updated or replanned can have a significant impact on MPS stability, productivity, production and inventory costs and customer service. Hence, one of the important decisions in the design of a rolling horizon MPS is the frequency of replanning. In this paper, we propose the possibility to establish a method for planning the MPS under demand uncertainty. A stochastic lot sizing algorithm is used to test the effectiveness of the rolling horizon MPS construction and extension. Therefore, a computer model was built to simulate the MPS activities under rolling horizon requirement. This model use a combination of an autoregressive fractionally integrated moving average (ARFIMA) forecasting model and fractional differencing method. The advantages of the ARFIMA time series model with fractional differencing method will benefits in planning the MPS under demand uncertainty. |
|---|