A multi-attribute decision making procedure using fuzzy numbers and hybrid aggregators

The classical Analytical Hierarchy Process (AHP) has two limitations. Firstly, it disregards the aspect of uncertainty that usually embedded in the data or information expressed by human. Secondly, it ignores the aspect of interdependencies among attributes during aggregation. The application of fuzzy numbers aids in confronting the former issue whereas, the usage of Choquet Integral operator helps in dealing with the later issue. However, the application of fuzzy numbers into multi-attribute decision making (MADM) demands some additional steps and inputs from decision maker(s). Similarly, identification of monotone measure weights prior to employing Choquet Integral requires huge number of computational steps and amount of inputs from decision makers, especially with the increasing number of attributes. Therefore, this research proposed a MADM procedure which able to reduce the number of computational steps and amount of information required from the decision makers when dealing with these two aspects simultaneously. To attain primary goal of this research, five phases were executed. First, the concept of fuzzy set theory and its application in AHP were investigated. Second, an analysis on the aggregation operators was conducted. Third, the investigation was narrowed on Choquet Integral and its associate monotone measure. Subsequently, the proposed procedure was developed with the convergence of five major components namely Factor Analysis, Fuzzy-Linguistic Estimator, Choquet Integral, Mikhailov‘s Fuzzy AHP, and Simple Weighted Average. Finally, the feasibility of the proposed procedure was verified by solving a real MADM problem where the image of three stores located in Sabak Bernam, Selangor, Malaysia was analysed from the homemakers‘ perspective. This research has a potential in motivating more decision makers to simultaneously include uncertainties in human‘s data and interdependencies among attributes when solving any MADM problems

Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey

The use of a multi-criteria decision-making (MCDM) technique mostly begins with normalizing the incommensurable data values in the decision matrix. Numerous normalization methods are available in the literature and applying different normalization methods to an MCDM technique is proven to deliver varying results. As such, selecting suitable normalization methods for an MCDM technique has emerged as an intriguing research topic, especially with the advent of big data. Several efforts have been made to compare the suitability of various normalization methods, but regrettably, no paper provides an updated review of these crucial efforts. This study, therefore, aimed to trace articles reporting such efforts and review them based on the following three perspectives: (1) the normalization methods considered, (2) the MCDM methods considered, and (3) the comparison metrics used to determine the suitable normalization methods. The relevant articles were extracted with the aid of Google Scholar using the keywords of “normalization” and “MCDM,” and Tableau software was used to analyze further the data gathered through the articles. A total of five limitations were uncovered based on the current state of literature, and potential future works to address those limitations were offered. This paper is the first to compile and review the previous investigations that compared and determined the ideal normalization methods for an MCDM technique

An unsupervised technique to estimate λ0-fuzzy measure values and its application to multi-criteria decision making

The use of Choquet integral as an aggregation operator in multi-criteria decision-making problems requires the prior estimation of fuzzy measure values. λ0 -measure is one form of fuzzy measure which was introduced to reduce the usual computational complexity associated with the estimation of fuzzy measure values. However, the existing techniques to estimate λ0 -measure require some amount of initial data from the decision-makers. This paper, therefore, aimed at proposing a completely unsupervised estimation technique, where the λ0- measure values are directly derived based on the available decision matrix, without the need for any initial data from the decision-makers. The technique was developed by incorporating the CRITIC method into the original λ0 - measure estimation technique. The usage of the proposed technique was illustrated based on a university course evaluation problem. The same problem was also solved with a conventional additive operator for the comparison purpose

Identifying the Image Attributes of Fast-food Restaurants Using Delphi Survey

People’s hectic routines have now led to mushrooming of fast-food restaurants (FFRs), especially in urban areas. Therefore, FFR owners need to find ways to control the demand of the market. Image is one of the drivers that influences customer satisfaction, behavior intention, trust, and loyalty. FFR owners should therefore take into account all possible attributes that may affect their image, before executing any improvement strategies. Unfortunately, the image evaluation attributes proposed in past scholarly works appear to be either incomprehensive or highly redundant to each other. This study thus aims at introducing a set of attributes, which are all-inclusive yet distinctive from one another other, which could be utilised by any future studies to evaluate the image of FFRs without too much revision. The study begins by extracting an initial list of image attributes by reviewing pertinent past literature. This tentative list was then verified via a two-round Delphi survey that was participated by 10 well-experienced fast-food restaurateurs. The contribution and limitation of the study are summarised in the conclusion section

A short survey on the usage of choquet integral and its associated fuzzy measure in multiple attribute analysis

Choquet integral operator is currently making inroads into many real multiple attribute analysis due to its ability on modeling the usual interactions held by the attributes during the aggregation process.Unfortunately, the process of identifying 2n values of fuzzy measure prior to employing Choquet integral normally turns into a very complex one with the increasing number of attributes, n.On that note, this paper mainly reviews on some of the methods that have been proposed in reducing the complexity of identifying fuzzy measure values together with their pros and cons. The paper begins with a discussion on the aggregation process in multiple attribute analysis which then focuses on the usage of Choquet integral and its associated fuzzy measure before investigating some of the fuzzy measure identification methods A simple numerical example to demonstrate the merit of using Choquet integral and the indications for future research are provided as well.The paper to some extent would be helpful in stimulating new ideas for developing simpler or enhanced versions of fuzzy measure identification methods

A hybrid multiattribute decision making model for evaluating students’ satisfaction towards hostels

This paper proposes a new hybrid multiattribute decision making (MADM) model which deals with the interactions that usually exist between hostel attributes in the process of measuring the students’ satisfaction towards a set of hostels and identifying the optimal strategies for enhancing their satisfaction. The model uses systematic random stratified sampling approach for data collection purpose as students dwelling in hostels are “naturally” clustered by block and gender, factor analysis for extracting large set of hostel attributes into fewer independent factors, λ-measure for characterizing the interactions shared by the attributes within each factor, Choquet integral for aggregating the interactive performance scores within each factor, Mikhailov’s fuzzy analytical hierarchy process (MFAHP) for determining the weights of independent factors, and simple weighted average (SWA) operator to measure the overall satisfaction score of each hostel. A real evaluation involving fourteen Universiti Utara Malaysia (UUM) hostels was carried out in order to demonstrate the model’s feasibility. The same evaluation was performed using an additive aggregation model in order to illustrate the effects of ignoring the interactions shared by attributes in hostel satisfaction analysis

A Hybrid Multiple Attribute Decision Making Model for Measuring Image Scores of a Set of Stores

Evaluating store image is a challenging task as it incorporates with multiple attributes. Earlier quantitative studies paid minimal attention on assessing the stores based on their image scores and overlooked the interaction aspects between attributes in the process of identifying the optimal strategies for image enhancement. This paper proposes a hybrid multiple attribute decision making model for quantitatively performing image evaluation involving a set of stores. The model uses factor analysis to extract the large set of interacted attributes into fewer independent factors, Sugeno measure to characterize the interactions between attributes, Choquet integral to aggregate the interactive performance scores within each extracted factor, Mikhailo

An analysis on the Service Dimensions of National Youth Training Institutes via an Integrated Multi-Attribute Decision-Making Procedure

It is indeed a challenging undertaking for the key players of Malaysian National Youth Skills Training Institutes (IKBNs, in Malay) to decide the finest possible strategies that could significantly enhance their students’ satisfaction over their services. It involves the consideration of various service attributes that naturally carry diverse priorities. Therefore, this study aimed at recommending some efficient strategies to improve student satisfaction in IKBNs by systematically uncovering the relative priorities of service dimensions. In this study, we carried out a Delphi survey involving a group of experts to validate the list of service attributes elicited from past literature. A questionnaire, which was designed based on the finalised 41 attributes, was then used to gather the necessary data from a sample of 636 IKBN students. With the help of factor analysis, these 41 attributes were then grouped into nine independent dimensions. Further analysis using the group-based compromised analytical hierarchy process (C-AHP) has identified training tools, training delivery, tangible amenities, student-centred management, and training instructors as the five most salient dimensions of student satisfaction. trastudy could enable the IKBNs to manage their resource better when improving their services. From the management science perspective, this study has contributed a new hybrid multiattribute decision-making procedure combining Delphi survey, factor analysis, and group CAHP. The procedure is appropriate for dealing with any complex decision problems that entail a large set of evaluation attributes

A Modified CRITIC Method to Estimate the Objective Weights of Decision Criteria

In this study, we developed a modified version of the CRiteria Importance Through Intercriteria Correlation (CRITIC) method, namely the Distance Correlation-based CRITIC (D-CRITIC) method. The usage of the method was illustrated by evaluating the weights of five smartphone criteria. The same evaluation was repeated using four other objective weighting methods, including the original CRITIC method. The results from all the methods were further analyzed based on three different tests (i.e., the distance correlation test, the Spearman rank-order correlation test, and the symmetric mean absolute percentage error test) to validate D-CRITIC. The tests revealed that D-CRITIC could produce more valid criteria weights and ranks than the original CRITIC method since D-CRITIC yielded a higher average distance correlation, a higher average Spearman rank-order correlation, and a lower symmetric mean absolute percentage error. Besides, additional sensitivity analysis indicated that D-CRITIC has the tendency to deliver more stable criteria weights and ranks with a larger decision matrix. The research has contributed an alternative objective weighting method to the area of multi-criteria decision-making through a unique extension of distance correlation. This study is also the first to propose the idea of a distance correlation test to compare the performance of different criteria weighting methods

Making informed decisions to improve restaurant image using a hybrid MADM approach: A case of fast-food restaurants in an island of East Malaysia

Restaurant image refers to an immediate perception that pops up in a customer’s mind when the name of a restaurant is mentioned. Therefore, it is crucial for restaurants, including fast-food restaurants (FFRs), to evaluate and sustain a positive restaurant image. However, evaluating and improving a restaurant’s image is challenging, since it counts in multiple service attributes associated with various degrees of unknown priority. Even so, the existing literature is yet to outspread the usage of an appropriate multi-attribute decision-making (MADM)-based approach to specifically evaluate the image of FFRs. Therefore, this research aimed at employing such an approach to evaluate the image of four FFRs on an island in East Malaysia, using various people, processes, and physical evidence attributes. Firstly, an initial list of FFR image attributes was elicited from the available literature. This initial list was then further validated through a two-round Delphi survey involving a panel of ten experts. A questionnaire was then designed based on the finalized attributes, and data collected from a sample of 251 respondents were analyzed using the compromised-analytical hierarchy process (C-AHP) method. The C-AHP results suggest that the strategies to improve an FFR’s image should primarily incorporate the following six attributes: hospitality, employees’ problem-solving skills, employees’ knowledge, food taste, physical cleanliness, and service response time. The FFR at the top of the ranking has the highest performance scores over these same six attributes. Surprisingly, employees’ appearance and restaurant exterior were reported as the two least important image attributes. This research is the first to demonstrate the application of a hybrid MADM-based approach to uncover the weights of FFR image attributes and rank those FFRs by computing their aggregated image scores