Requalifying public buildings and utilities using a group decision support system. (15th October 2017)

• Record Type: Journal Article
• Authors: Lolli, Francesco; Ishizaka, Alessio; Gamberini, Rita; Rimini, Bianca; Balugani, Elia; Prandini, Laura
• Is Part Of: Journal of cleaner production. Volume 164(2017)
• Page Start: 1081
• Page End: 1092
• Publication Date: 2017-10-15
• Subjects: Energy requalification -- Decision support system -- AHP -- Clustering -- Knapsack; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2017.07.031
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Public buildings and utilities are responsible for a significant share of energy consumption and other related CO2 emissions. There is therefore an acute need for energy requalification interventions. Unfortunately, municipalities are under tight budget constraints – but decisions have to be made. A new hybrid group decision support system has been proposed in a bid to provide them with firm, transparent support. The system is based on a combination of the analytic hierarchy process, the K-means algorithm, and the 0–1 knapsack model. The first two methods aim at sorting alternatives into ordered classes of importance. To help in this task, the Bezier curve-fitting approach is used to construct the preference functions of decision-makers based on reference points. Then, the knapsack model selects the alternatives from the generated classes while complying with the budget constraints. A case study carried out in an Italian municipality allowed us to verify the validity of our new method in a real setting, and to highlight the advantages of an automatic sorting procedure in practice. Highlights: Selecting the energy requalification interventions is a relevant multi-criteria issue. We developed a new hybrid group decision support system. Bezier curve-fitting approach, AHP, K-means algorithm, and 0–1 knapsack have been combined. A case study carried out in an Italian municipality allowed us to verify the validity of our proposal in a real setting.