Evaluation of Multicriteria Decision Analysis Algorithms in Food Safety: A Case Study on Emerging Zoonoses Prioritization. Issue 2 (30th August 2019)

• Record Type: Journal Article
• Authors: Garre, Alberto; Boué, Geraldine; Fernández, Pablo S.; Membré, Jeanne‐Marie; Egea, Jose A.
• Is Part Of: Risk analysis. Volume 40:Issue 2(2020)
• Page Start: 336
• Page End: 351
• Publication Date: 2019-08-30
• Subjects: Food safety management -- MCDA -- prioritization -- risk ranking -- uncertainty; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.13391
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract: Decision making in food safety is a complex process that involves several criteria of different nature like the expected reduction in the number of illnesses, the potential economic or health‐related cost, or even the environmental impact of a given policy or intervention. Several multicriteria decision analysis (MCDA) algorithms are currently used, mostly individually, in food safety to rank different options in a multifactorial environment. However, the selection of the MCDA algorithm is a decision problem on its own because different methods calculate different rankings. The aim of this study was to compare the impact of different uncertainty sources on the rankings of MCDA problems in the context of food safety. For that purpose, a previously published data set on emerging zoonoses in the Netherlands was used to compare different MCDA algorithms: MMOORA, TOPSIS, VIKOR, WASPAS, and ELECTRE III. The rankings were calculated with and without considering uncertainty (using fuzzy sets), to assess the importance of this factor. The rankings obtained differed between algorithms, emphasizing that the selection of the MCDA method had a relevant impact in the rankings. Furthermore, considering uncertainty in the ranking had a high influence on the results. Both factors were more relevant than the weights associated with each criterion in this case study. A hierarchical clustering method was suggested to aggregate results obtained by the different algorithms. This complementary step seems to be a promising way to decrease extreme difference among algorithms and could provide a strong added value in the decision‐making process.