In this work the uncertainties related to the optimal planning/allocation of government subsidies for residential building stocks retrofits are considered and the uncertainty based planning problem is formulated and solved as a multi-objective, constrained problem. Different multi-objective algorithms are considered with the idea to determine the most effective and efficient approach that can be customized as planning tool to be used by the public administration personnel. The preliminary comparison between 2 multi-objective evolutionary algorithms and a deterministic one is presented and optimal/pareto results are analysed.
UNCERTAINTY BASED OPTIMAL PLANNING OF RESIDENTIAL BUILDING STOCKS RETROFITS / Ricciu, R.; Besalduch, L. A.; Minisci, Edmondo; Manuello Bertetto, A.. - In: INTERNATIONAL JOURNAL OF MECHANICS AND CONTROL. - ISSN 1590-8844. - STAMPA. - 20:1(2019), pp. 127-132.
UNCERTAINTY BASED OPTIMAL PLANNING OF RESIDENTIAL BUILDING STOCKS RETROFITS
MINISCI, Edmondo;A. Manuello Bertetto
2019
Abstract
In this work the uncertainties related to the optimal planning/allocation of government subsidies for residential building stocks retrofits are considered and the uncertainty based planning problem is formulated and solved as a multi-objective, constrained problem. Different multi-objective algorithms are considered with the idea to determine the most effective and efficient approach that can be customized as planning tool to be used by the public administration personnel. The preliminary comparison between 2 multi-objective evolutionary algorithms and a deterministic one is presented and optimal/pareto results are analysed.File | Dimensione | Formato | |
---|---|---|---|
JoMaC.pdf
accesso aperto
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Pubblico - Tutti i diritti riservati
Dimensione
600.47 kB
Formato
Adobe PDF
|
600.47 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2783976