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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Main Authors:	Muchamad , Oktaviandri, Adnan, Hassan, Awaluddin, Mohd. Shaharoun
Format:	Article
Language:	English
Published:	IOP Publishing Ltd 2016
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/12505/ http://umpir.ump.edu.my/id/eprint/12505/ http://umpir.ump.edu.my/id/eprint/12505/1/Generation%20of%20Look-Up%20Tables%20for%20Dynamic%20Job%20Shop%20Scheduling%20Decision%20Support%20Tool.pdf

id	ump-12505
recordtype	eprints
spelling	ump-125052018-02-27T07:09:50Z 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. http://iopscience.iop.org/article/10.1088/1757-899X/114/1/012067/meta
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TS Manufactures
spellingShingle	TS Manufactures Muchamad , Oktaviandri Adnan, Hassan Awaluddin, Mohd. Shaharoun Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool
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.
format	Article
author	Muchamad , Oktaviandri Adnan, Hassan Awaluddin, Mohd. Shaharoun
author_facet	Muchamad , Oktaviandri Adnan, Hassan Awaluddin, Mohd. Shaharoun
author_sort	Muchamad , Oktaviandri
title	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_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_sort	generation of look-up tables for dynamic job shop scheduling decision support tool
publisher	IOP Publishing Ltd
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/12505/ http://umpir.ump.edu.my/id/eprint/12505/ http://umpir.ump.edu.my/id/eprint/12505/1/Generation%20of%20Look-Up%20Tables%20for%20Dynamic%20Job%20Shop%20Scheduling%20Decision%20Support%20Tool.pdf
first_indexed	2023-09-18T22:14:13Z
last_indexed	2023-09-18T22:14:13Z
_version_	1777415229192273920

Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool

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