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 multi-component 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.