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

摘要

In maintenance systems, the current approach to workforce analysis entails theudutilisation of metrics that focus exclusively on workforce cost and productivity. Thisudmethod omits the “green” concept, which principally hinges on energy-efficientudmanufacturing and also ignores the production-maintenance integration. The approach isudnot accurate and could not be heavily relied upon for sound maintenance decisions.udConsequently, a comprehensive, scientifically-motivated, cost-effective and anudenvironmentally-conscious approach are needed. With this in view, a deviation from theudtraditional approach through employing a combined fuzzy, quality function deploymentudinteracting with three meta-heuristics (colliding bodies optimisation, big-bang big-crunchudand particle swarm optimisation) for optimisation is made in the current study. Theudworkforce size parameters are determined by maximising workforce size’s earned-valuedudas well as electric power efficiency maximisation subject to various real-life constraints.udThe efficacy and robustness of the model is tested with data from an aluminium productsudmanufacturing system operating in a developing country. The results obtained indicateudthat the proposed colliding bodies’ optimisation framework is effective in comparisonudwith other techniques. This implies that the proposed methodology potentially displaysudtremendous benefit of conserving energy, thus aiding environmental preservation and costudof energy savings. The principal novelty of the paper is the uniquely new method ofudquantifying the energy savings contributions of the maintenance workforce