Joint optimization of preventive maintenance and spare parts inventory using genetic algorithms and particle swarm optimization algorithm

The problem of joint optimization of preventive maintenance and spare parts inventory was solved in this paper. The novelty of this study lies with the fact that the developed method could tackle not only the artificial test case but also a real-world industrial problem. Various investigators developed several methods and semi-analytical tools for obtaining optimum solutions for this problem. In this study, non-traditional optimization tools, namely genetic algorithms (GA) and particle swarm optimization (PSO) algorithm were utilized to obtain the joint optimum preventive maintenance and spare parts inventory ordering interval. The optimum values of time interval yielded by both the GA and PSO algorithm were compared and found to be in agreement with the published results for the similar models obtained through semi-numerical methods. It proves the applicability of these non-traditional optimization tools to solve these problems. This investigation ended with the analysis of preventive maintenance data taken from an industry, for an electric overhead traveling crane. The optimum time schedules so suggested by the GA and PSO algorithm were found to be cost effective, in comparison with the current practice being followed by the industry. A sensitivity analysis was also conducted at the end for this industrial problem.