Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/7275
Title: Memetic algorithm to optimize preventive maintenance schedule for a multi-component machine
Authors: Bakshi, Miroojin
Lad, Bhupesh Kumar
Keywords: Customer satisfaction;Genetic algorithms;Machine components;Maintenance;Optimization;Scheduling algorithms;Stochastic models;Stochastic systems;Brute force search;Computational time;Machine downtime;Maintenance scheduling;Manufacturing industries;Memetic algorithms;Multicomponents;Stochastic simulation model;Preventive maintenance
Issue Date: 2016
Publisher: Totem Publishers Ltd
Citation: Upasani, K., Bakshi, M., Pandhare, V., & Lad, B. K. (2016). Memetic algorithm to optimize preventive maintenance schedule for a multi-component machine. International Journal of Performability Engineering, 12(2), 183-195.
Abstract: Failure of a machine in a manufacturing industry leads to disruption of operations that severely impacts the enterprise in terms of revenue and customer satisfaction. The most prevalent strategy to mitigate the chance of machine failure is to perform preventive maintenance. Optimizing the preventive maintenance schedule is important to minimize operations cost and machine downtime. Further, for a multicomponent machine, the knowledge of which components to perform preventive maintenance on can be crucial for such optimization. A stochastic simulation model can be used to evaluate all possible candidates and find the optimum preventive maintenance schedule. However, this involves infeasible computational time owing to the combinatorial nature of the problem. A Memetic Algorithm is proposed as a heuristic in the present work to address this challenge. Accuracy of obtained solutions and run-time is compared with brute-force search method and genetic algorithm for the same system. The Memetic Algorithm is found to yield better results as it can explore the search space more efficiently. © Totem Publisher, Inc.
URI: https://dspace.iiti.ac.in/handle/123456789/7275
ISSN: 0973-1318
Type of Material: Journal Article
Appears in Collections:Department of Mechanical Engineering

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