Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool
Majority of existing scheduling techniques are based on static demand and deterministic processing time, while most job shop scheduling problem are concerned with dynamic demand and stochastic processing time. As a consequence, the solutions obtained from the traditional scheduling technique are ine...
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| Format: | Article |
| Language: | English |
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IOP Publishing Ltd
2016
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| Online Access: | http://umpir.ump.edu.my/id/eprint/12505/1/Generation%20of%20Look-Up%20Tables%20for%20Dynamic%20Job%20Shop%20Scheduling%20Decision%20Support%20Tool.pdf |
| _version_ | 1825822821176049664 |
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| author | Muchamad , Oktaviandri Adnan, Hassan Awaluddin, Mohd. Shaharoun |
| author_facet | Muchamad , Oktaviandri Adnan, Hassan Awaluddin, Mohd. Shaharoun |
| author_sort | Muchamad , Oktaviandri |
| collection | UMP |
| description | Majority of existing scheduling techniques are based on static demand and deterministic processing time, while most job shop scheduling problem are concerned with dynamic demand and stochastic processing time. As a consequence, the solutions obtained from the traditional scheduling technique are ineffective wherever changes occur to the system. Therefore, this research intends to develop a decision support tool (DST) based on promising artificial intelligent that is able to accommodate the dynamics that regularly occur in job shop scheduling problem. The DST was designed through three phases, i.e. (i) the look-up table generation, (ii) inverse model development and (iii) integration of DST components. This paper reports the generation of look-up tables for various scenarios as a part in development of the DST. A discrete event simulation model was used to compare the performance among SPT, EDD, FCFS, S/OPN and Slack rules; the best performances measures (mean flow time, mean tardiness and mean lateness) and the job order requirement (inter-arrival time, due dates tightness and setup time ratio) which were compiled into look-up tables. The well-known 6/6/J/Cmax Problem from Muth and Thompson (1963) was used as a case study. In the future, the performance measure of various scheduling scenarios and the job order requirement will be mapped using ANN inverse model. |
| first_indexed | 2024-03-06T12:02:16Z |
| format | Article |
| id | UMPir12505 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:02:16Z |
| publishDate | 2016 |
| publisher | IOP Publishing Ltd |
| record_format | dspace |
| spelling | UMPir125052018-02-27T07:09:50Z http://umpir.ump.edu.my/id/eprint/12505/ Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool Muchamad , Oktaviandri Adnan, Hassan Awaluddin, Mohd. Shaharoun TS Manufactures Majority of existing scheduling techniques are based on static demand and deterministic processing time, while most job shop scheduling problem are concerned with dynamic demand and stochastic processing time. As a consequence, the solutions obtained from the traditional scheduling technique are ineffective wherever changes occur to the system. Therefore, this research intends to develop a decision support tool (DST) based on promising artificial intelligent that is able to accommodate the dynamics that regularly occur in job shop scheduling problem. The DST was designed through three phases, i.e. (i) the look-up table generation, (ii) inverse model development and (iii) integration of DST components. This paper reports the generation of look-up tables for various scenarios as a part in development of the DST. A discrete event simulation model was used to compare the performance among SPT, EDD, FCFS, S/OPN and Slack rules; the best performances measures (mean flow time, mean tardiness and mean lateness) and the job order requirement (inter-arrival time, due dates tightness and setup time ratio) which were compiled into look-up tables. The well-known 6/6/J/Cmax Problem from Muth and Thompson (1963) was used as a case study. In the future, the performance measure of various scheduling scenarios and the job order requirement will be mapped using ANN inverse model. IOP Publishing Ltd 2016 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/12505/1/Generation%20of%20Look-Up%20Tables%20for%20Dynamic%20Job%20Shop%20Scheduling%20Decision%20Support%20Tool.pdf Muchamad , Oktaviandri and Adnan, Hassan and Awaluddin, Mohd. Shaharoun (2016) Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool. Materials Science and Engineering, 114. pp. 1-9. (Published) http://iopscience.iop.org/article/10.1088/1757-899X/114/1/012067/meta |
| spellingShingle | TS Manufactures Muchamad , Oktaviandri Adnan, Hassan Awaluddin, Mohd. Shaharoun Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool |
| title | Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool |
| title_full | Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool |
| title_fullStr | Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool |
| title_full_unstemmed | Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool |
| title_short | Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool |
| title_sort | generation of look up tables for dynamic job shop scheduling decision support tool |
| topic | TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/12505/1/Generation%20of%20Look-Up%20Tables%20for%20Dynamic%20Job%20Shop%20Scheduling%20Decision%20Support%20Tool.pdf |
| work_keys_str_mv | AT muchamadoktaviandri generationoflookuptablesfordynamicjobshopschedulingdecisionsupporttool AT adnanhassan generationoflookuptablesfordynamicjobshopschedulingdecisionsupporttool AT awaluddinmohdshaharoun generationoflookuptablesfordynamicjobshopschedulingdecisionsupporttool |