Dynamic scheduling in a multi-product manufacturing system
To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize wait...
| Main Authors: | , , |
|---|---|
| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Teknologi Malaysia
2005
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/2647/ http://eprints.utm.my/2647/1/75062.pdf |
| _version_ | 1848890398991187968 |
|---|---|
| author | Hassan, Adnan Mohd. Shaharoun, Awaluddin Oktaviandri, Muchamad |
| author_facet | Hassan, Adnan Mohd. Shaharoun, Awaluddin Oktaviandri, Muchamad |
| author_sort | Hassan, Adnan |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation. |
| first_indexed | 2025-11-15T20:41:27Z |
| format | Monograph |
| id | utm-2647 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:41:27Z |
| publishDate | 2005 |
| publisher | Universiti Teknologi Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-26472012-05-22T03:52:21Z http://eprints.utm.my/2647/ Dynamic scheduling in a multi-product manufacturing system Hassan, Adnan Mohd. Shaharoun, Awaluddin Oktaviandri, Muchamad TJ Mechanical engineering and machinery TS Manufactures To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation. Universiti Teknologi Malaysia 2005-03-30 Monograph NonPeerReviewed application/pdf en http://eprints.utm.my/2647/1/75062.pdf Hassan, Adnan and Mohd. Shaharoun, Awaluddin and Oktaviandri, Muchamad (2005) Dynamic scheduling in a multi-product manufacturing system. Project Report. Universiti Teknologi Malaysia. (Unpublished) |
| spellingShingle | TJ Mechanical engineering and machinery TS Manufactures Hassan, Adnan Mohd. Shaharoun, Awaluddin Oktaviandri, Muchamad Dynamic scheduling in a multi-product manufacturing system |
| title | Dynamic scheduling in a multi-product manufacturing system |
| title_full | Dynamic scheduling in a multi-product manufacturing system |
| title_fullStr | Dynamic scheduling in a multi-product manufacturing system |
| title_full_unstemmed | Dynamic scheduling in a multi-product manufacturing system |
| title_short | Dynamic scheduling in a multi-product manufacturing system |
| title_sort | dynamic scheduling in a multi-product manufacturing system |
| topic | TJ Mechanical engineering and machinery TS Manufactures |
| url | http://eprints.utm.my/2647/ http://eprints.utm.my/2647/1/75062.pdf |