A two-level approach for multi-objective flexible job shop scheduling and energy procurement

Dynamic energy tariffs in combination with energy storage systems (ESS) and renewable energy sources (RES) offer manufacturers new opportunities to optimize their energy consumption. Flexible production planning empowers decision-makers not only to minimize makespan, but also to reduce energy costs and emissions. However, flexible production planning is a major challenge due to the fact that scheduling decisions affect energy demand, whose costs and emissions depend on energy procurement decisions. In Operations Research, the Green Flexible Job Shop Scheduling Problem (FJSP) addresses production planning decisions incorporating resource, environmental, and economic objectives. The Energy Procurement Problem (EPP) aims to efficiently acquire energy resources. In the literature, existing approaches for energy-aware scheduling neglect to procure energy from sources such as an uncertain dynamic energy market, RES, and ESS. We aim to close this research gap and propose a two-level approach based on a memetic Non-dominated Sorting Genetic Algorithm (NSGA-III) and linear programming with the goal of minimizing the makespan, energy costs, and emissions of a schedule, incorporating dynamic energy prices and emissions, RES, and ESS. We evaluate the approach in computational experiments using FJSP benchmark instances from the literature as part of a rolling horizon approach with real energy market data. We investigate the impact of RES and ESS by presenting estimated Pareto fronts, showing potential savings in energy cost and carbon emissions.