The field of energy systems and transportation is witnessing a significant shift towards optimizing energy dispatch and scheduling. Researchers are exploring innovative approaches to balance energy supply and demand, reduce costs, and minimize environmental impact. One notable trend is the integration of evolutionary algorithms and metaheuristics to solve complex scheduling problems. These methods are being applied to various domains, including hybrid renewable energy systems, energy-aware production planning, and railway timetabling. The use of data-driven approaches and real-world data is also becoming increasingly prevalent, enabling more accurate modeling and adaptive control solutions. Noteworthy papers in this area include:

• A study on a data-driven Evolutionary Game-Based Model Predictive Control framework for hybrid renewable energy systems, which achieves cost-effective and reliable energy dispatch.
• A comparative analysis of evolutionary algorithms for energy-aware production scheduling, which presents a memetic metaheuristic approach for minimizing makespan, energy costs, and emissions.
• A methodology based on fairness-oriented timetabling for equitable rail service allocation in liberalized markets, which uses equity metrics to ensure efficient and sustainable allocation of infrastructure.