Abstract
Profit-making is one of the main aims of any manufacturing unit and minimization of the overall maintenance cost can help to reach this goal. However, this should be achieved without any compromise with various machinery reliability constraints. Often, due to the busy production schedule, maintenance job is postponed, which leads to the higher production cost for frequent breakdowns, and others. These can be mitigated by performing preventive maintenance (PM), but it has to be done in an optimal sense. An optimized PM interval should minimize the total maintenance cost, while satisfying the lower bound reliability constraints. Most of the PM models available in literature do not address this aspect, especially for the real industrial circumstances. Crankshaft balancing machine is an example of such a system, where PM interval is to be optimized for the better production rate and overall maintenance cost minimization. To solve this problem, meta-heuristic techniques, such as genetic algorithm (GA), particle swarm optimization (PSO) have been implemented. However, in this paper, a recently-developed optimization method, namely Bonobo Optimizer (BO), has been applied to determine the optimal PM interval with the minimized total maintenance cost. In this experiment, BO is able to yield better results compared to that of the GA and PSO, and a considerable amount of cost reduction has been achieved using this technique.
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Das, A.K., Pratihar, D.K. (2019). Optimal preventive maintenance interval for a Crankshaft balancing machine under reliability constraint using Bonobo Optimizer. In: Uhl, T. (eds) Advances in Mechanism and Machine Science. IFToMM WC 2019. Mechanisms and Machine Science, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-030-20131-9_164
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DOI: https://doi.org/10.1007/978-3-030-20131-9_164
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