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Index policies for the maintenance of a collection of machines by a set of repairmen
Affiliation:1. School of Management, University of Edinburgh, William Robertson Building, 55 George Square, Edinburgh EH8 9JY, UK;2. School of Mathematics and Statistics, Merz Court, University of Newcastle upon Tyne, Newcastle upon Tyne NE1 7RU, UK;1. School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China;2. School of Mechanical Engineering, Guangxi University, Nanning 530004, China;3. Nexteer Automotive systems (Liu Zhou) Co, ltd, Liuzhou 545005, China;1. School of Business, East China University of Science and Technology, Shanghai, 200237, China;2. International Elite Engineering School of Physics, East China University of Science and Technology, Shanghai, 200237, China;3. Research Center for Econophysics, East China University of Science and Technology, Shanghai, 200237, China;4. School of Mathematics, East China University of Science and Technology, Shanghai, 200237, China;1. Department of Statistics, College of Commerce, National Chengchi University, Taipei, Taiwan;2. Department of Applied Statistics and Operations Research, Schmidthorst College of Business, Bowling Green State University, Bowling Green, OH, USA;1. School of Economics and Finance, South China University of Technology, China;2. Department of Economics, University of Texas at San Antonio, USA
Abstract:A collection of M machines which deteriorate under usage is maintained by a set of R repairmen who may have other tasks to perform. Maintenance interventions will improve a machine's condition and may preempt costly breakdowns. The problem of scheduling such interventions to minimise the total expected discounted cost incurred in operating the machines over an infinite horizon is formulated as a Markov Decision Process which has the form of a restless bandit problem. We outline an approach to the development of maintenance policies based on simple machine indices in the form of a fair charge for a maintenance intervention in the machine's current state. Closed form indices are derived for two particular models. Numerical investigations demonstrate the strong performance of the derived index heuristics.
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