Renewable Energy-Aware Machine Scheduling Under Intermittent Energy Supply

The integration of renewable energy sources into the manufacturing sector is a recent development, which is prompting companies to explore innovative approaches to enhance their production systems through efficient renewable energy utilization. Within this context, renewable energy-aware machine scheduling has emerged as a pivotal area requiring novel strategies for sustainability. This study aims to evaluate the effect of intermittent renewable energy supply on sustainable machine scheduling by incorporating the option of machine speed for the first time in the literature. A two-stage stochastic program is developed to model the problem and examine the value of the stochastic solution. Additionally, an efficient genetic algorithm is proposed for approximate problem solving. Extensive test study is conducted to explore and generalize the sustainable operational policies derived from the proposed method. The results reveal key scheduling strategies that promote sustainability in production by reducing non-renewable dependence. Notably, the study underlines the importance of considering uncertainties in renewable supply in scheduling, especially under conditions of substantial job and machine scales, high-capacity but variable renewable supply, and flexible job deadlines. As an illustration, in the context of test cases involving a significant number of jobs and machines, the stochastic solutions demonstrate a substantial impact on reducing energy expenditure. Failing to account for the stochastic nature of renewable energy supply would lead to considerable additional energy consumption from the grid. These findings offer valuable insights for corporate management and scheduling operations, particularly as they navigate the transition towards green manufacturing practices.