My research focuses on energy-aware scheduling problems. The specific problem I am currently studying aims to minimize the energy cost of executing a set of jobs on a single machine within a fixed time horizon, where electricity prices follow a Time-of-Use (TOU) tariff. We additionally consider the presence of a battery that can be charged from the grid and discharged during high-price periods to reduce overall energy costs.
The core challenge is to jointly determine the job order, the precise start time of each job and the optimal battery usage. Even without a battery, this scheduling problem is known to be NP-hard; the introduction of energy storage adds additional interactions that make the decision space even more complex.
Starting from this fundamental setting allows us to develop algorithmic techniques designed to handle the interaction between scheduling and energy storage. In the long term, this work will be extended toward more complex scheduling environments, incorporating features such as multiple machines, setup times, and order selection decisions.
The final goal is to design efficient algorithms that exploit the structure of the problem, combining exact methods for key subproblems with metaheuristic strategies to obtain high-quality solutions for realistic instances.
