Energy-carbon footprint optimization in sequence-dependent production scheduling

Under the panorama of increasing energy costs and environmental policies driven by global warming concerns, this research is aimed at creating an energy optimal production schedule model to minimize energy costs and reduce carbon footprint emissions. The proposed model is built under the context of a sequence-dependent single machine scheduling problem. Moreover, the scheduling decisions are assumed to be affected by independent time-of-use electricity prices and carbon footprint environmental policies forced by either corporate or governmental institutions. To this end, a Low Energy-Carbon Cost Sequence Dependent Job Scheduling optimization model (LEC-SDJS) is developed under a Mixed Integer Linear Programming formulation. Preliminary results show the value of the model when compared to classical scheduling optimization approaches and job scheduling heuristics. Research directions and proposed validation are finally discussed. • Planned idle time is of strategic advantage in production scheduling, not a waste. • Optimal production scheduling decisions are sensitive to carbon tax values. • Production makespan and energy and emissions efficiency are conflicting in nature. • Scheduling energy-intensive jobs during off-peak hours increases emissions costs.