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Seminar by Dr. Nabil Nahas (SE Dept.), 25th Nov 2014




Metaheuristics for Redundancy and Imperfect Preventive Maintenance Optimization Problem



Tuesday, 25 November, 2014

From 2:10 PM - 3:10 PM

Location    22-134



Dr. Nabil Nahas

Assistant Professor, Systems Engineering Department





To improve the performance of a multi-state degraded system, preventive maintenance (PM) plays a key role. Perfect PM is aimed at making the MSS ‘as good as new’, while imperfect PM may bring the MSS back to an intermediate state between the current state and the perfect functioning state. In practice, both redundancy and maintenance are used to provide a required level of system reliability. In this talk a joint redundancy and imperfect preventive maintenance planning optimization model for series-parallel multi-state degraded systems is presented. Non-identical multi-state components can be used in parallel to improve the system availability by providing redundancy in subsystems. The status of each component is considered to degrade with use. The objective is to determine jointly the maximal-availability series-parallel system structure and the appropriate preventive maintenance actions, subject to a budget constraint. A procedure is used, based on Markov processes and universal moment generating function, to evaluate the multi-state system availability and the cost function. Two metaheuristics approaches are proposed to solve the formulated problem.




Nabil Nahas is an Assistant Professor at King Fahd University of Petroleum & Minerals (Dhahran, Saudi Arabia) in the Systems Engineering Department. He received his DESS degree in Industrial Engineering, Master degree in Aerospace Engineering and PhD in Industrial Engineering from Laval University (Canada) in 1995, 2000 and 2007 respectively. He was a Postdoctoral Research-Fellow at CIRELT (Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, Canada) from 2010 to 2012. His main research interests are in the optimal design of production and manufacturing systems, reliability optimization, meta-heuristics and layout design.