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Abstract
Today’s maintenance workforce operates in a complex business environment and relies on metrics that indirectly link equipment breakdown, fluctuating production rate, demand uncertainties and fluctuating raw material requirements. This has triggered a change in the scope as well as the substance of maintenance workforce theory and practice, and the necessary requirement to promote a full understanding of maintenance workforce optimization of some seemingly non-polynomial hard problems. Theorizing is essential on the near optimal solution techniques for the maintenance workforce problem. In this paper, a fuzzy goal programming model is proposed and used in formulating a single objective function for maintenance workforce optimization with stochastic constraint consideration. The performance of the proposed model was verified using data obtained from a production system and simulated annealing (SA) as a solution method. The results obtained using SA and differential evolution (DE) were compared on the basis of computational time and quality of solution. We observed that the SA results outperform those of the DE algorithm. Based on the results obtained, the proposed model has the capacity to generate reliable information for preventive and breakdown workforce maintenance planning.