The project Re?Pli was represented with a contribution at the annual International Conference on Operations Research, jointly organized by the German Operations Research Society GOR), the Austrian Society for Operations Research (?GOR), and the Swiss Operations Research Society (SVOR/ASRO). The conference took place in Hamburg from August 29th to September 1st 2023.
In the stream “Energy and Environment”, Sascha Burmeister presented a new approach to the Green Multi-Objective Flexible Job Shop Problem with consideration of energy storage systems.
Abstract:
Environmental sustainability has become increasingly important in recent years, as many countries set targets to reduce the carbon emissions. A major consumer of energy is the manufacturing industry. For manufacturers it is essential to find ways to align their production with the current energy mix and consume energy at times with high renewable energy production without jeopardizing production deadlines. In addition to schedules based on the energy mix, energy storage systems can be used to compensate for fluctuations in renewable energy sources. In the literature, the Green Flexible Job Shop Scheduling Problem (FJSP) is concerned with resource and environmental aspects in addition to the economic objective of the minimum makespan. However, existing approaches neglect the volatile dynamic energy mix, a multi-criteria objective, or the consideration of modern energy storage systems. We aim to close this research gap and propose an algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) with the goal of minimizing both, makespan and emissions. In order to design low-emission production, the algorithm constructs production schedules while considering the charging and discharging of an energy storage system. We evaluate the approach in computational experiments using prominent FJSP-benchmark instances from the literature with real emissions. We investigate the trade-off between a short makespan and low carbon emissions based on solutions on the approximated Pareto front and discuss the value of energy storage systems for sustainable production.
