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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2006
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| Online Access: | http://eprints.utm.my/3382/1/Methodology_Of_Rolling_Horizon_Scheduling_Under_Demand_Uncertainty.pdf |
| _version_ | 1796853558805528576 |
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| author | Romli, Awanis Ameendeen, Mohamed Ariff Ahmad, Norasnita |
| author_facet | Romli, Awanis Ameendeen, Mohamed Ariff Ahmad, Norasnita |
| author_sort | Romli, Awanis |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T18:01:19Z |
| format | Conference or Workshop Item |
| id | utm.eprints-3382 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:01:19Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | utm.eprints-33822017-08-29T06:22:43Z http://eprints.utm.my/3382/ Methodology of rolling horizon scheduling under demand uncertainty Romli, Awanis Ameendeen, Mohamed Ariff Ahmad, Norasnita H Social Sciences (General) QA75 Electronic computers. Computer science 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. 2006-12 Conference or Workshop Item NonPeerReviewed application/pdf en http://eprints.utm.my/3382/1/Methodology_Of_Rolling_Horizon_Scheduling_Under_Demand_Uncertainty.pdf Romli, Awanis and Ameendeen, Mohamed Ariff and Ahmad, Norasnita (2006) Methodology of rolling horizon scheduling under demand uncertainty. In: International Conference on Technology Management 2006, 4 - 5 December 2006, Putrajaya. |
| spellingShingle | H Social Sciences (General) QA75 Electronic computers. Computer science Romli, Awanis Ameendeen, Mohamed Ariff Ahmad, Norasnita Methodology of rolling horizon scheduling under demand uncertainty |
| title | Methodology of rolling horizon scheduling under demand uncertainty |
| title_full | Methodology of rolling horizon scheduling under demand uncertainty |
| title_fullStr | Methodology of rolling horizon scheduling under demand uncertainty |
| title_full_unstemmed | Methodology of rolling horizon scheduling under demand uncertainty |
| title_short | Methodology of rolling horizon scheduling under demand uncertainty |
| title_sort | methodology of rolling horizon scheduling under demand uncertainty |
| topic | H Social Sciences (General) QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/3382/1/Methodology_Of_Rolling_Horizon_Scheduling_Under_Demand_Uncertainty.pdf |
| work_keys_str_mv | AT romliawanis methodologyofrollinghorizonschedulingunderdemanduncertainty AT ameendeenmohamedariff methodologyofrollinghorizonschedulingunderdemanduncertainty AT ahmadnorasnita methodologyofrollinghorizonschedulingunderdemanduncertainty |