Testing the reliability of deterministic multi-criteria decision-making methods using building performance simulation. (September 2019)

• Record Type: Journal Article
• Authors: Kokaraki, Nikoleta; Hopfe, Christina J.; Robinson, Elaine; Nikolaidou, Elli
• Is Part Of: Renewable & sustainable energy reviews. Volume 112(2019)
• Page Start: 991
• Page End: 1007
• Publication Date: 2019-09
• Subjects: Multi-criteria decision making -- AHP -- TOPSIS -- PROMETHEE II -- ELECTRE III -- Sensitivity analysis; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2019.06.018
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: The design of a building is a decision problem with multiple stakeholders and several often conflicting criteria. Multi-Criteria Decision-Making (MCDM) methods are capable of handling decision problems with the abovementioned specific features. This paper focuses on the application and comparison of some of the most well-known and widely applied MCDM methods; namely AHP, TOPSIS, ELECTRE III, and PROMETHEE II. For this purpose, a framework that incorporates the decision-makers' preferences and the use of dynamic simulation of the potential building forms is developed and applied to a real-life case study. This procedure provides a holistic approach through which the design team arrives at a more appropriate decision. The examined methods yield similar results, showing broad agreement on the top ten alternatives, amongst which the decision-makers can decide. In particular, the majority of the examined methods (all except TOPSIS) concluded with the same optimal solution, a situation that provides confidence in the final decision. Furthermore, an investigation of four "what-if" scenarios indicates that TOPSIS is the most sensitive method to the examined changes in the subjective preferences. Highlights: We compare four deterministic decision protocols using building performance simulation. Three of four methods yield similar results, showing agreement on the top design alternatives. Apart from TOPSIS, the methods provide confidence in the final decision. We further tested the sensitivity based on changes in subjective preferences. Four "what-if" scenarios indicate that TOPSIS is most sensitive to these changes.