Knowledge and agent-based system for decentralised scheduling in manufacturing

The aim of the research paper is to develop algorithms for manufacturers’ agents that would allow them to sequence their own operation plans and to develop a multi-agent infrastructure to allow operation pair agents to cooperatively adjust the timing of manufacturing operations. The scheduling probl...

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Autores principales:	Salman Saeidlou, Mozafar Saadat, Guiovanni D. Jules
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	semantic web ontology graph database multi-agent system disturbance conflict resolution Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/bcf8251859ce437198c7ff21b6f80253
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id	oai:doaj.org-article:bcf8251859ce437198c7ff21b6f80253
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spelling	oai:doaj.org-article:bcf8251859ce437198c7ff21b6f802532021-11-04T15:51:55ZKnowledge and agent-based system for decentralised scheduling in manufacturing2331-191610.1080/23311916.2019.1582309https://doaj.org/article/bcf8251859ce437198c7ff21b6f802532019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311916.2019.1582309https://doaj.org/toc/2331-1916The aim of the research paper is to develop algorithms for manufacturers’ agents that would allow them to sequence their own operation plans and to develop a multi-agent infrastructure to allow operation pair agents to cooperatively adjust the timing of manufacturing operations. The scheduling problem consisted of jobs with fixed process plans and of manufacturers collectively offering the necessary operations for the jobs. Manufacturer agents sequenced and pair agents timed each operation as and when required. Timing an operation triggered a cascade of conflicts along the job process plan that other pair agents would pick up on and would take action accordingly. The conventional approach performs conflict resolution in series and manufacturer agents as well as pair agents wait until they are allowed to sequence and time the next operation. The limiting assumption behind that approach was systematically removed, and the proposed approach allowed manufacturers to perform operation scheduling in parallel, cutting down tenfold on the computation time. The multi-agent infrastructure consists of the Protégé knowledge base, the Pellet semantic reasoner and the Workflows and Agent Development Environment (WADE). The case studies used were the MT6, MT10 and LA19 job shop scheduling problems; and an industrial use case was provided to give context to the manufacturing environment investigated. Although there were benefits from the decentralised manufacturing system, we noted an optimality loss of 34% on the makespans. However, for scalability, our approach showed good promise.Salman SaeidlouMozafar SaadatGuiovanni D. JulesTaylor & Francis Grouparticlesemantic webontologygraph databasemulti-agent systemdisturbanceconflict resolutionEngineering (General). Civil engineering (General)TA1-2040ENCogent Engineering, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	semantic web ontology graph database multi-agent system disturbance conflict resolution Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	semantic web ontology graph database multi-agent system disturbance conflict resolution Engineering (General). Civil engineering (General) TA1-2040 Salman Saeidlou Mozafar Saadat Guiovanni D. Jules Knowledge and agent-based system for decentralised scheduling in manufacturing
description	The aim of the research paper is to develop algorithms for manufacturers’ agents that would allow them to sequence their own operation plans and to develop a multi-agent infrastructure to allow operation pair agents to cooperatively adjust the timing of manufacturing operations. The scheduling problem consisted of jobs with fixed process plans and of manufacturers collectively offering the necessary operations for the jobs. Manufacturer agents sequenced and pair agents timed each operation as and when required. Timing an operation triggered a cascade of conflicts along the job process plan that other pair agents would pick up on and would take action accordingly. The conventional approach performs conflict resolution in series and manufacturer agents as well as pair agents wait until they are allowed to sequence and time the next operation. The limiting assumption behind that approach was systematically removed, and the proposed approach allowed manufacturers to perform operation scheduling in parallel, cutting down tenfold on the computation time. The multi-agent infrastructure consists of the Protégé knowledge base, the Pellet semantic reasoner and the Workflows and Agent Development Environment (WADE). The case studies used were the MT6, MT10 and LA19 job shop scheduling problems; and an industrial use case was provided to give context to the manufacturing environment investigated. Although there were benefits from the decentralised manufacturing system, we noted an optimality loss of 34% on the makespans. However, for scalability, our approach showed good promise.
format	article
author	Salman Saeidlou Mozafar Saadat Guiovanni D. Jules
author_facet	Salman Saeidlou Mozafar Saadat Guiovanni D. Jules
author_sort	Salman Saeidlou
title	Knowledge and agent-based system for decentralised scheduling in manufacturing
title_short	Knowledge and agent-based system for decentralised scheduling in manufacturing
title_full	Knowledge and agent-based system for decentralised scheduling in manufacturing
title_fullStr	Knowledge and agent-based system for decentralised scheduling in manufacturing
title_full_unstemmed	Knowledge and agent-based system for decentralised scheduling in manufacturing
title_sort	knowledge and agent-based system for decentralised scheduling in manufacturing
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/bcf8251859ce437198c7ff21b6f80253
work_keys_str_mv	AT salmansaeidlou knowledgeandagentbasedsystemfordecentralisedschedulinginmanufacturing AT mozafarsaadat knowledgeandagentbasedsystemfordecentralisedschedulinginmanufacturing AT guiovannidjules knowledgeandagentbasedsystemfordecentralisedschedulinginmanufacturing
_version_	1718444737465155584

Knowledge and agent-based system for decentralised scheduling in manufacturing

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