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Surrogate estimators for complex bi-level energy management

Bendali Fatiha, Mailfert Jean, Gonzales Alejandro Olivas, Quilliot Alain, Toussaint Hélène, Kamga Eloise Mole

Annals of Computer Science and Information Systems

2022

Vol. 32

85--92

We deal here with the routing of vehicles in charge of performing internal logistics tasks inside some protected area. Those vehicles are provided in energy by a local solar hydrogen production facility, with limited storage and time-dependent production capacities. In order to avoid importing energy from outside, one wants to synchronize energy production and consumption in order ot minimize both production and routing costs. Because of the complexity of resulting bi-level model, we deal with it by short-cutting the production scheduling level with the help of surrogate estimators, whose values are estimated through fast dynamic programming algorithms or through machine learning.