The energy transition is driving rapid growth in renewable energy generation,
creating the need to balance energy supply and demand with energy price
awareness. One such approach for manufacturers to balance their energy demand
with available energy is energyaware production planning. Through energy-aware
production planning, manufacturers can align their energy demand with dynamic
grid conditions, supporting renewable energy integration while benefiting from
lower prices and reduced emissions. Energy-aware production planning 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 market dynamics and the NP-hard
multi-objective nature of the problem, evolutionary algorithms are widely used
for energy-aware scheduling. Cependant, existing research focuses on the design
and analysis of single algorithms, with limited comparisons between different
approaches. In this study, we adapt NSGA-III, HypE, and $\theta$-DEA as memetic
metaheuristics for energy-aware scheduling to minimize makespan, energy costs,
emissions, and the number of workers, within a real-time energy market context.
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.

Cet article explore les excursions dans le temps et leurs implications.

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