314. Comparative Analysis of Evolutionary Algorithms for Energy-Aware Production Scheduling
Invited abstract in session WD-12: Practical problems in scheduling , stream Scheduling and Project Management.
Wednesday, 14:30-16:00Room: Clarendon SR 1.02
Authors (first author is the speaker)
| 1. | Sascha C Burmeister
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| Department of Management Information Systems, Paderborn University | |
| 2. | Till Niklas Rogalski
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| Paderborn University | |
| 3. | Guido Schryen
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| Department of Information Systems, Paderborn University |
Abstract
The energy transition is driving rapid growth in renewables, requiring manufacturers to balance energy demand with price awareness. Energy-aware production planning aligns demand with dynamic grid conditions, supporting renewables while reducing costs and emissions. This can be modeled as a multi-criteria scheduling problem, where the objectives extend beyond traditional metrics like makespan or required workers to also include minimizing energy costs and emissions. Due to frequent recalculations and the NP-hard multi-objective nature of the problem, evolutionary algorithms are widely used. However, research often focuses on single algorithms with limited comparative studies. This study adapts NSGA-III, HypE, and theta-DEA as memetic metaheuristics for energy-aware production scheduling, minimizing makespan, energy costs, emissions, and workforce in a real-time energy market. These adapted metaheuristics present different approaches for environmental selection. In a comparative analysis, we explore differences in solution efficiency and quality across various scenarios which are based on benchmark instances from the literature and real-world energy market data. Additionally, we estimate upper bounds on the distance between objective values obtained with our memetic metaheuristics and reference sets obtained via an exact solver.
Keywords
- OR in Energy
- Scheduling
- Metaheuristics
Status: accepted
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