Surrogate estimators for complex bi-level energy management

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

Identyfikatory

DOI

10.15439/2022F19

• Konferencja: 17th Conference on Computer Science and Intelligence Systems
• Języki publikacji: EN

Abstrakty

EN

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.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 32
• Strony: 85--92
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Bendali Fatiha

• LIMOS Lab. CNRS/UCA Clermont-Ferrand, France

autor

Mailfert Jean

• LIMOS Lab. CNRS/UCA Clermont-Ferrand, France

autor

Gonzales Alejandro Olivas

• Labex IMOBS3, LIMOS Lab, UCA/CNRS Clermont-Ferrand, France

autor

Quilliot Alain

• Labex IMOBS3, LIMOS Lab, UCA/CNRS Clermont-Ferrand, France

autor

Toussaint Hélène

autor

Kamga Eloise Mole

• Labex IMOBS3, LIMOS Lab CNRS/UCA, Clermont-Ferrand, France

Bibliografia

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• 2. A.Albrecht, P. Pudney: Pickup and delivery with a solar-recharged vehicle. Ph.D. thesis Australian Society for O.R (2013).
• 3. C.Artigues, E.Hébrard, A.Quilliot, H.Toussaint: "Models and algorithms for natural disaster evacuation problems". Proceedings of the 2019 FEDCSIS WCO Conference, p 143-146, (2019). DOI: http://dx.doi.org/10.15439/978-83-952357-8-8.
• 4. F.Bendali, J.Mailfert, E.Mole-Kamga, A.Quilliot, H.Toussaint : Pipelining dynamic programming procsses in order to synchronize Energy production and consumption, 2020 FEDCSIS WCO Conf., p 303-306, (2020). DOI: http://dx.doi.org/10.15439/978-83-955416-7-4.
• 5. A.Caprara, M.Carvalho, A.Lodi, G.J.Woeinger: A study on the computational complexity of the bilevel knapsack problem; SIAM Journal on Optimization 24 (2), p 823-838, (2014).
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• 8. S.Fidanova, O.Roeva, M.Ganzha: " Ant colony optimization algorithm for fuzzy transport modelling". Proceedings of the 2020 FEDCSIS WCO Conference, p 237-240, (2020). DOI: http://dx.doi.org/10.15439/978-83-955416-7-4
• 9. C.Grimes, O.Varghese, S.Ranjan. Light, water, hydrogen: Solar generation by photoelectrolysis. Springer-Verlag US, (2008).
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• 12. M.Krzyszton: "Adapative supervison: method of reinforcement learning fault elimination by application of supervised learning". Proceedings of the 2018 FEDCSIS AI Conference, p 139-149, (2018). DOI: http://dx.doi.org/10.15439/978-83-949419-5-6
• 13. G. Macrina, L.D. Pugliese, F. Guerriero: (2020). The green-vehicle routing problem: A survey. In Modeling and Optimization in Green Logistics, Cham: Springer International Publishing. p. 1-26, (2020). doi:10.1007/978-3-030-45308-4_1
• 14. K.Stoilova, T.Stoilov: "Bi-level optimization application for urban traffic management". 2020 FEDCSIS WCO Conference, p 327-336, (2020). DOI: http://dx.doi.org/10.15439/978-83-949419-5-6
• 15. J. Wojtuziak, T. Warden, O. Herzog: "Machine learning in agent based stochastic simulation: Evaluation in transportation logistics"; Computer and Mathematics with Applications 64, p 3658-3665, (2012). https://doi.org/10.1016/j.camwa.2012.01.079.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).