Energy-efficient flow-shop scheduling with the strategy of switching the power statuses of machines

Abstract

Energy-saving for flow-shop scheduling problem has drawn growing attention under the environmental pressure in industry. As the major source of energy consumption for flow-shop, machine tools are unavoidable to stay in idle status in most cases, which result in greater waste of energy. To investigate the potential on energy-saving for flow-shop, this work proposed a novel ultra-low idle status for machine tools by turning off part of the power load in idle status. In light of this, an energy efficient model for flow-shop is first established by considering three-statuses of machine tools, i.e., processing, idle and ultra-low idle. To solve the problem, a novel hybrid genetic algorithm (HGA) with energy-saving strategy (ESS) and enhanced local search was presented, which realized the active control of machine tools in three power statuses. A case study was proposed to illustrate the availability of the HGA and its application in practice. The results showed that the proposed HGA is effective to reduce the total energy consumption by adjusting the power statuses in idle status. The proposed model can help enterprises to realize the smooth reduction of energy consumption and power demand in the production process when the three-statuses of machine tools scheduling optimization is used.