Abstract
Energy saving has always been a major concern in the design and operation of sustainable manufacturing systems. Optimal maintenance strategies have also been explored intensively to achieve manufacturing sustainability. However, in most of the related studies, the two aspects are considered separately. As a manufacturing system is characterized by its complex and stochastic dynamics, there has been a lack of a general methodology to integrate the two important sectors for a sustainable manufacturing system. In this paper, utilizing the data acquired by the distributed sensors, a data-driven model of multi-stage manufacturing system is established. Based on the proposed model, the losses or benefits of conducting maintenance and energy saving are properly evaluated. Based on the proposed real-time maintenance cost rate, the maintenance control can determine the optimal maintenance level, i.e. perfect, imperfect, or minimal, upon random failures. The energy saving is achieved by switching on or off the machines according to system states. The knowledge-guided genetic algorithm is applied to find the optimal energy saving decisions. The proposed joint control scheme is implemented in a real-time manner to achieve resource-effective and energy-efficient production.
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