A manufacturing system energy-efficient optimisation model for maintenance production workforce size determination using integrated fuzzy logic and quality function deployment approach

In maintenance systems, the current approach to workforce analysis entails the utilisation of metrics that focus exclusively on workforce cost and productivity. This method omits the “green” concept, which principally hinges on energy-efficient manufacturing and also ignores the production-maintenance integration. The approach is not accurate and could not be heavily relied upon for sound maintenance decisions. Consequently, a comprehensive, scientifically-motivated, cost-effective and an environmentally-conscious approach are needed. With this in view, a deviation from the traditional approach through employing a combined fuzzy, quality function deployment interacting with three meta-heuristics (colliding bodies optimisation, big-bang big-crunch and particle swarm optimisation) for optimisation is made in the current study. The workforce size parameters are determined by maximising workforce size’s earned-valued as well as electric power efficiency maximisation subject to various real-life constraints. The efficacy and robustness of the model is tested with data from an aluminium products manufacturing system operating in a developing country. The results obtained indicate that the proposed colliding bodies’ optimisation framework is effective in comparison with other techniques. This implies that the proposed methodology potentially displays tremendous benefit of conserving energy, thus aiding environmental preservation and cost of energy savings. The principal novelty of the paper is the uniquely new method of quantifying the energy savings contributions of the maintenance workforc