Life Cycle Assessment quantifies the multi-dimensional impact of goods and services and can be handled by Multi-Criteria Decision Analysis. In Multi-Criteria Decision Analysis, Robust Ordinal Regression manages all the compatible preference functions at once when assessing a set of alternatives and a group of preferences on reference alternatives. Robust Ordinal Regression is thus a versatile method of reducing the cognitive effort required by decision makers for eliciting their preference structures in Life Cycle Assessment, although it does not directly operate on noisy alternatives and requires Stochastic Multicriteria Acceptability Analysis to deal with such scenarios. We propose integrating a dimensionality reduction technique, Principal Component Analysis, and Robust Ordinal Regression methods, to reduce the problem dimensionality and ensure the actual problem features are considered. A generated dataset, a dataset from literature and a Life Cycle Assessment case study are used to test the effectiveness of the proposed methods.

Dimensionality reduced robust ordinal regression applied to life cycle assessment / Balugani, E.; Lolli, F.; Pini, M.; Ferrari, A. M.; Neri, P.; Gamberini, R.; Rimini, B.. - In: EXPERT SYSTEMS WITH APPLICATIONS. - ISSN 0957-4174. - 178:(2021), pp. 1-15. [10.1016/j.eswa.2021.115021]

Dimensionality reduced robust ordinal regression applied to life cycle assessment

Balugani E.
Investigation
;
Lolli F.
Methodology
;
Pini M.
Membro del Collaboration Group
;
Ferrari A. M.
Membro del Collaboration Group
;
Neri P.
Membro del Collaboration Group
;
Gamberini R.
Conceptualization
;
Rimini B.
Supervision
2021

Abstract

Life Cycle Assessment quantifies the multi-dimensional impact of goods and services and can be handled by Multi-Criteria Decision Analysis. In Multi-Criteria Decision Analysis, Robust Ordinal Regression manages all the compatible preference functions at once when assessing a set of alternatives and a group of preferences on reference alternatives. Robust Ordinal Regression is thus a versatile method of reducing the cognitive effort required by decision makers for eliciting their preference structures in Life Cycle Assessment, although it does not directly operate on noisy alternatives and requires Stochastic Multicriteria Acceptability Analysis to deal with such scenarios. We propose integrating a dimensionality reduction technique, Principal Component Analysis, and Robust Ordinal Regression methods, to reduce the problem dimensionality and ensure the actual problem features are considered. A generated dataset, a dataset from literature and a Life Cycle Assessment case study are used to test the effectiveness of the proposed methods.
178
1
15
Dimensionality reduced robust ordinal regression applied to life cycle assessment / Balugani, E.; Lolli, F.; Pini, M.; Ferrari, A. M.; Neri, P.; Gamberini, R.; Rimini, B.. - In: EXPERT SYSTEMS WITH APPLICATIONS. - ISSN 0957-4174. - 178:(2021), pp. 1-15. [10.1016/j.eswa.2021.115021]
Balugani, E.; Lolli, F.; Pini, M.; Ferrari, A. M.; Neri, P.; Gamberini, R.; Rimini, B.
File in questo prodotto:
File Dimensione Formato  
Balugani et al. 2021.pdf

non disponibili

Tipologia: Versione dell'editore (versione pubblicata)
Dimensione 1.72 MB
Formato Adobe PDF
1.72 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1246318
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 3
social impact