Das Projekt Re?Pli ist mit einem Beitrag auf der zweij?hrlich stattfindenden Europ?ischen Konferenz für Operations Research (OR) vertreten. Die 33. Europ?ischen Konferenz für Operations Research findet vom 30.06.2024 bis 03.07.2024 an der Technischen Universit?t D?nemark in Kopenhagen statt und wird von der Vereinigung der europ?ischen Gesellschaften für OR ausgerichtet.
Im Stream "Energy for OR" pr?sentiert Sascha Burmeister einen neuen Ansatz, wie Produktionsplanungsentscheidungen getroffen werden und zeitgleich eine günstige und emissionsarme Energiebeschaffung gew?hrleistet wird.
Dabei wird der dynamische Energiemarkt mit seinen aktuellen Emissionen berücksichtigt und auch evtl. vorhandene Energiespeicher und Anlagen zur Gewinnung erneuerbarer Energie inkludiert.
Abstract (Englisch):
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.
