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
