The Re?Pli project is represented with a contribution at the biennial European Conference on Operations Research (OR).The 33rd European Conference on Operations Research will take place from June 30 to July 03 2024 at the Technical University of Denmark in Copenhagen and is organized by the Association of European Societies for OR.
In the stream "Energy for OR", Sascha Burmeister will present a new approach on how to make production planning decisions while ensuring low-cost and low-emission energy procurement.
The dynamic energy market with its current emissions is taken into account and any existing energy storage and renewable energy generation systems are also included.
Abstract:
Dynamic energy tariffs and on-site energy generation offer manufacturers new opportunities to optimize their energy consumption. In addition to conventional goals such as minimizing makespan, manufacturers can focus on minimizing energy costs or emissions. However, scheduling processes in the face of uncertain future energy prices and emissions is a major challenge. Energy storage systems (ESS) can compensate for differences between predicted and actual energy costs and emissions. To make an investment decision for ESS, decision makers need to assess their impact on energy costs and emissions, as well as their return on investment. In the literature, the Green Flexible Job Shop Scheduling Problem (FJSP) is concerned with resource and environmental aspects in addition to economic objectives. However, existing approaches neglect the combination of a multi-criteria objective with an uncertain dynamic energy mix and the use of ESS. We propose a two-phase approach based on a memetic NSGA-III and mathematical programming with the goal of minimizing a schedule’s makespan, energy costs, and emissions, incorporating dynamic energy prices and emissions, on-site generation, and ESS. We evaluate the approach using FJSP benchmark instances from literature as part of a rolling horizon approach with real energy market data. We investigate the impact of ESS by presenting estimated Pareto fronts, showing potential savings in energy cost and carbon emissions.
