A surrogate model for optimal maintenance workforce cost determination in a process industry

In manufacturing systems, stiff competition in the international markets coupled with the global economic crisis has exerted immense pressure to search for new maintenance approaches. This study focuses on determining the optimal maintenance time and workforce size, as well as a low overall life-cycle cost. Current literature has completely ignored the influence of these measures and the significance of prediction has been downplayed. Consequently, the necessity to develop an approach based on scientific foundations is affirmed. Keeping in view the status of the literature concerning this issue, a surrogate model (an evolutionary algorithm technique and fuzzy inference system (FIS)) for maintenance workforce cost prediction is presented. The key influencing parameters include production volume, routine maintenance time, workforce size and spare parts costs. The surrogate model was tested using process industry data. The result (root mean square error) obtained from the surrogate model was better than those of an artificial neural network (ANN) and FIS. The implication of these results is that the surrogate model could serve as a basis for determining maintenance workforce costs during decision-making processes. The novelty of this study is that it uses a differential evolution algorithm to improve the performance of FIS when dealing with maintenance workforce cost prediction