Simulation based optimization of joint maintenance and inventory for multi-components manufacturing systems

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dc.contributor.authorAlrabghi, Abdullah
dc.contributor.authorTiwari, Ashutosh
dc.contributor.authorAlabdulkarim, Abdullah A.
dc.date.accessioned2019-04-12T08:59:00Z
dc.date.available2019-04-12T08:59:00Z
dc.date.issued2014-01-27
dc.description.abstractMaintenance and spare parts management are interrelated and the literature shows the significance of optimizing them jointly. Simulation is an efficient tool in modeling such a complex and stochastic problem. In this paper, we optimize preventive maintenance and spare provision policy under continuous review in a non-identical multi-component manufacturing system through a combined discrete event and continuous simulation model coupled with an optimization engine. The study shows that production dynamics and labor availability have a significant impact on maintenance performance. Optimization results of Simulated Annealing, Hill Climb and Random solutions are compared. The experiments show that Simulated annealing achieved the best results although the computation time was relatively high. Investigating multi-objective optimization might provide interesting results as well as more flexibility to the decision maker.en_UK
dc.identifier.citationAbdullah Alrabghi, Ashutosh Tiwari and Abdullah Alabdulkarim. Simulation based optimization of joint maintenance and inventory for multi-components manufacturing systems. 2013 Winter simulation conference (WSC 2013), 8-11 December 2013, Washington, DC. USAen_UK
dc.identifier.isbn9781479939503
dc.identifier.urihttps://Doi.org/10.1109/WSC.2013.6721500
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14070
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleSimulation based optimization of joint maintenance and inventory for multi-components manufacturing systemsen_UK
dc.typeConference paperen_UK

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