An Online Policy for Energy-Efficient State Control of Manufacturing Equipment

Machine state control is one of the most promising energy-efficient measures for machining processes. A proper control reduces the energy consumed during idle periods by switching off/on the machines. A critical barrier for practical implementation is related to the knowledge of part arrival process that is affected by uncertainty. The stochastic processes involved in the system are usually assumed to be known. However, real production environments are subject to several sources of randomness that are difficult to model a priori. This work provides an online time-based algorithm that is able to control the machine state. Through a method for the estimation of the stochastic process, the algorithm provides the optimal control parameters based on a