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dc.contributor.authorStockton, Daviden
dc.contributor.authorKhalil, R. A. (Riham A.)en
dc.contributor.authorMukhongo, Lawrenceen
dc.date.accessioned2012-12-19T09:43:22Z
dc.date.available2012-12-19T09:43:22Z
dc.date.issued2012
dc.identifier.citationStockton, D.J., Khalil, R. and Mukhongo, L.M. (2012) Autonomous Planning using the basic principles of Gene Transcription Regulatory Control. American Journal of Operational Research, 2 (5), pp. 66-80en
dc.identifier.issn2160-8830
dc.identifier.urihttp://hdl.handle.net/2086/7966
dc.description.abstractThe vast majority of the research efforts in finite capacity scheduling over the past several years has focused on the generation of precise and almost exact measures for the working schedule presupposing complete information and a deterministic environment. During execution, however, production may be the subject of considerable variability, which may lead to frequent schedule interruptions. Adopting biological control principles refers to the process where a schedule is developed prior to the start of the processing after considering all the parameters requirements at a machine and updated accordingly as the process executes. This research reviews the best practices in gene transcription and translation control methods and adopts these principles in the development of an autonomous finite capacity scheduling control logic aimed at reducing excessive use of manual input in planning tasks. With autonomous decision-making functionality, finite capacity scheduling will as much as practicably possible be able to respond autonomously to process variability by deployment of proactive scheduling procedures that may be used to revise or re-optimize the schedule when variability process requirements is noted. The novelty of this work is the ability for processing machine to autonomous take decisions just as decisions are taken by autonomous entities in the process of gene transcription and translation. The idea has been implemented by the integration of simulation modelling techniques with Taguchi analysis to investigate the contributions of finite capacity scheduling factors, and determination of the ‘what if’ scenarios encountered due to the existence of variability in production processes. The control logic adopts the induction rules as used in gene expression control mechanisms, studied in biological systems. Scheduling factors are identified to that effect and are investigated to find their effects on selected performance metrics for each machine used. How they are used to deal with variability in process is one major objective for this research as it is because of the variability that autonomous decision making becomes of interest. Although different scheduling techniques have been applied and are successful in production planning and control, the results obtained from the inclusion of the autonomous finite capacity scheduling control logic has proved that significant improvement can still be achieved. This research demonstrated that the correct choice of values for finite capacity scheduling factors has a great impact on the performance measurements, such that, high throughput rate can be reached with a bigger batch size, lower percentage of rework, and shorter stoppages. Quicker movement of goods through the facility means better utilisation of assets. Better utilisation of assets creates additional capacity resulting from faster throughput thereby improving customer satisfaction through quicker delivery. It has also been shown that %waiting, %blocking and %stoppage makes it possible to examine different manufacturing constraints as well as the relationship between non-utilisation and different performance measurements.en
dc.language.isoenen
dc.publisherScientific & Academic Publishingen
dc.subjectVariabilityen
dc.subjectControl Factorsen
dc.subjectLogic Controlen
dc.subjectInduction Rulesen
dc.titleAutonomous Planning using the basic principles of Gene Transcription Regulatory Controlen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.5923/j.ajor.20120205.04
dc.researchgroupAdvanced Manufacturing Processes and Mechatronics Centre (AMPMC)en
dc.peerreviewedYesen
dc.ref2014.selected1366964034_8110680006428_15_4


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