Using GA and MCFDM to Find the Optimal Maintenance Planning - Tahvili, Sahar
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The main goal of this project is to explore the use of stochastic simulation, genetic algorithms, fuzzy decision making and other tools for solving complex maintenance planning optimization problems. We use two different maintenance activities, corrective maintenance and preventive maintenance. Since the evaluation of specific candidate maintenance policies can take a long time to execute and the problem of finding the optimal policy is both non-linear and non-convex, we propose the use of genetic algorithms (GA) for the optimization.

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Produktbeschreibung
The main goal of this project is to explore the use of stochastic simulation, genetic algorithms, fuzzy decision making and other tools for solving complex maintenance planning optimization problems. We use two different maintenance activities, corrective maintenance and preventive maintenance. Since the evaluation of specific candidate maintenance policies can take a long time to execute and the problem of finding the optimal policy is both non-linear and non-convex, we propose the use of genetic algorithms (GA) for the optimization.
Autorenporträt
Sahar Tahvili is a master student in Applied Mathematics at Mälardalen University, Sweden. Her main research interests include Non-linear optimization. She received her B.Sc. in applied mathematics with focus on operation research at IAUZAH, Iran 2006 and also B.Eng. in aeronautical engineering at Mälardalen University.