Classifying the degree of exposure of customers to COVID-19 in the restaurant industry: A novel intuitionistic fuzzy set extension of the TOPSIS-Sort

Abstract

Despite the rigid public safety protocols of the restaurant sector amid the COVID-19 pandemic in an effort to restart economic activities, customers do not feel secure eating at a sit-in restaurant, which is associated with prolonged restrictions on movement. As a mitigating initiative, holistically evaluating customers’ perceived degree of exposure to COVID-19 in restaurants is deemed relevant in the design of mitigation measures. Such an agenda is associated with multiple attributes under decision-making uncertainty within the framework of multiple criteria sorting (MCS). Thus, this work addresses this problem domain by proposing an intuitionistic fuzzy set extension of the previously developed TOPSIS-Sort (i.e., IF TOPSIS-Sort). As a case demonstration, 40 restaurants are evaluated under six attributes that define exposure to COVID-19. With 250 survey participants, the IF TOPSIS-Sort assigns 10, 13, and 17 restaurants to low, moderate, and high exposure classes, respectively. With this classification, crucial insights are offered to the restaurant industry for planning and policy formulation. To determine its effectiveness, a comparative analysis was carried with other distance-based MCS methods. Findings reveal that the proposed method is pessimistic and that other methods tend to underestimate the assignments, which may be counterintuitive, especially in applications related to public health. These sorting differences may be associated with addressing the vagueness and uncertainty in decision-making within the IF TOPSIS-Sort platform. The proposed novel IF TOPSIS-Sort is sufficiently generic for other domain sorting applications and contributes to the MCS literature.

Emergency preparedness during the COVID-19 pandemic: Modelling the roles of social media with fuzzy DEMATEL and analytic network process

“…Following the declaration of national emergency, the entire island of Luzon was placed under Enhanced Community Quarantine, wherein travel bans were implemented [ 101 ]. Strict physical distancing measures were also enforced, which led to the suspension of classes, holding work-from-home activities for employees, and the prohibition of social gatherings [ 102 ]. Additionally, the strategic installation of handwashing facilities in public areas was imposed to facilitate hand hygiene and sanitation.…”

Intuitionistic fuzzy fairly operators and additive ratio assessment-based integrated model for selecting the optimal sustainable industrial building options

“…In order to assess the rooftop photovoltaic project sites, Gao et al 26 recommended an MCDM methodology by combining intuitionistic fuzzy score function, prospect theory, analytical network process and linear weighting technique. Ocampo et al 29 suggested the TOPSIS-Sort method with IFSs and demonstrated in arranging the restaurants for apparent experience of clients to COVID-19.…”

ARASsort: A new sorting based multiple attribute decision‐making algorithm

“…Yamagishi and Ocampo (2022) used their TOPSISsort algorithms in sorting tourist sites for perceived COVID‐19 exposure. Ocampo et al (2021) proposed the intuitionistic fuzzy extension of TOPSISsort and showed this extension's applicability in classifying the degree of exposure of customers to COVID‐19 in the restaurant industry. Interested readers can find the steps of TOPSISsort algorithms and their details in mentioned papers.…”

“…All methods have their own assumption, characteristics, paradigm, and perspective. Outranking methods such as PROMSORT (Araz & Ozkarahan, 2005) and Flowsort (Nemery & Lamboray, 2008) which are based on PROMETHEE, require that the decision‐maker defines preference functions for each attribute and also indifference and preference thresholds. Distance‐based approaches which are extensions of compensatory methods such as TOPSISsort (de Lima Silva et al, 2020; De Lima Silva & de Almeida Filho, 2020; Ocampo et al, 2021; Sabokbar et al, 2016; Yamagishi & Ocampo, 2022) and VIKORsort (Demir et al, 2018; Ocampo & Yamagishi, 2021; Polat et al, 2021; Sabbagh et al, 2021) need to determine positive and negative ideal solutions and measure the distance between each alternative and these ideals. Pairwise comparison‐based approaches relying on subjective judgments of the decision‐makers, need to build a hierarchy showing the relations between attributes and/or alternatives and too many survey questions should be answered by the experts, such as AHPsort (Ishizaka et al, 2012; Toledo et al, 2019), AHPsort II (Labella et al, 2020; Miccoli & Ishizaka, 2017; Xie et al, 2019), some fuzzy AHP sorting methods (Du et al, 2021; Ishizaka et al, 2020; Krejci & Ishizaka, 2018; Xu et al, 2019), and group AHPsort (Assumma et al, 2021; Labella et al, 2021; Lopez & Ishizaka, 2017). …”