A Clustering Multi-Criteria Decision-Making Method for Large-Scale Discrete and Continuous Uncertain Evaluation

Since light fidelity (LiFi) and wireless fidelity (WiFi) do not interfere with one another, a LiFi/WiFi hybrid network may provide superior performance to existing wireless options. With many users and constant changes, a network can easily become overloaded, leading to slowdowns and fluctuations in data transfer speeds. Access point assignment (APA) is required with the increase of users, which can negatively impact the system performance and quality-of-service (QoS) due to mobility and line-of-sight (LOS) blockage. Many variables could influence the APA process; these variables are considered as criteria, such as the network capacity, the degree of blockage, the speed of the connected user, etc. Unlike conditional APA methods, recent studies have considered treating these variables as “evaluation criteria”. Considering these criteria can offer better and more accurate results, eventually enhancing the APA process and QoS. However, the variety of these criteria, the conflict amongst them, their weights (importance), and priority have not been addressed so far. Moreover, treating the criteria equally might result in inaccurate outcomes. Therefore, to solve this issue, it is essential to investigate the impact of each criterion on the APA process. In this work, a multicriteria decision-making (MCDM) problem is formulated to determine a network-level selection for each user over a period of time The decision problem is modeled as a hierarchy that fragments a problem into a hierarchy of simple and small subproblems, and the selection of the AP network among various alternatives is a considered as an MCDM problem. Based on the previous works, we are not aware of any previous research attempts using MCDM methods in the LiFi research area for network selection. Therefore, this work proposes an access point assignment framework using an MCDM approach for users in a hybrid LiFi/WiFi network. The experiment was conducted based on four phases: Five criteria were identified and evaluated with eleven APs (alternatives). The outcome of this phase was used to build the decision matrix and an MCDM was developed and built based on user mobility and blockages with various scenarios using all the criteria; The analytic hierarchy process (AHP) was employed to identify the criterion of the subjective weights of each criterion and to determine the degree of importance supported by experts’ judgement. Determining the weights in the AHP process considered various investigations, including the consistency ratio (CR) and the AHP consensus indicator, which is calculated using the rank-based maximum likelihood method (RGMM) and Shannon entropy techniques. The VIekriteri-Jumsko KOmpromisno Rangiranje (VIKOR) method is adopted in the selection of the optimal AP for the proper selection of whether a LiFi or WiFi AP must serve the users. The integrated AHP–VIKOR was effective for solving the APA and was the best solution based on using weighted criteria simultaneously. Moreover, the ranking outcomes of the developed integrated AHP–VIKOR approach were evaluated using sensitivity analysis. The result of this work takes the APA for hybrid LiFi networks to a new perspective.

Decision-making is a highly researched topic and various methods have been developed to facilitate a decision-maker (DM) in choosing the best alternative. Saaty's analytic hierarchy process (AHP) has been very popular since 1977 and has been adapted all over the world. However, AHP is a highly-debated topic. Technique for order of preference by similarity to ideal solution (TOPSIS) is another multi-criteria decision-making (MCDM) method developed by Hwang and Yoon in 1981 as a ranking method. This research is focused on identifying which is the MCDM method between AHP and TOPSIS. Since TOPSIS is a ranking method, the authors propose to combine AHP and TOPSIS methods and determine which method's ranking (AHP, AHP-TOPSIS combination, and TOPSIS with equal weights) aligns more closely with the DM's initial preference. Moreover, this research states the efficiency of the method by comparing the time it takes to make a decision and its reliability.

Fuzzy multicriteria decision making (MCDM) has been widely used in ranking a finite number of decision alternatives characterised by fuzzy assessments with respect to multiple evaluation criteria. The MCDM methods suitable for solving a given decision problem usually differ in their normalisation process and aggregation process for handling the performance ratings of the decision alternatives and the weights of the evaluation criteria. The overall preference of a decision alternative is obtained by aggregating the criteria weights and the performance ratings of the alternatives, on which the ranking is based. Due to their structural differences, these methods often produce inconsistent ranking results for the same fuzzy MCDM problem. To address this issue, this study develops a novel approach for the development and validation of fuzzy MCDM models. The approach incorporates three normalisation methods, three aggregation methods, and a α-cut based defuzzification method to develop fuzzy MCDM models. The α-cut based defuzzification method allows the decision maker’s attitude on fuzzy assessments to be incorporated into the decision making process. To examine the validity of the fuzzy MCDM models available for a given decision problem, a new validation process is developed based on the fuzzy clustering technique to assist in selecting a valid outcome from the inconsistent ranking results produced by these models. To demonstrate the effectiveness of the fuzzy MCDM model development and validation approach, three practical applications under various decision contexts are conducted. The first application is about the airport performance evaluation problem. This study selects 12 Asia-Pacific major international airports as the decision alternatives of the evaluation problem and identifies 19 quantitative and qualitative evaluation criteria under the airport operator, passenger, and airline dimensions. Based on three normalisation methods and two aggregation methods, six fuzzy MCDM models are developed which produce inconsistent ranking results for the evaluation problem. The ranking validity of the six models is examined by the validation process using fuzzy clustering and the most valid model is selected. The second application is concerned with the scrap metal buyer selection problem. This study considers five recycling companies in southern China as the decision alternatives of the buyer selection problem and identifies four qualitative selection criteria under the economic and environmental dimensions. Based on three normalisation methods and three aggregation methods, seven fuzzy MCDM models are developed which produce inconsistent ranking results for the selection problem. The ranking validity of the seven models is examined by the validation process using fuzzy clustering and the most valid model is selected. The third application deals with the non-ferrous scrap metal supplier selection problem. This study considers 15 scrap metal suppliers as the decision alternatives of the supplier selection problem and identifies five quantitative and qualitative selection criteria for a non-ferrous scrap metal buyer. Based on three normalisation methods and three aggregation methods, seven fuzzy MCDM models are developed which produce inconsistent ranking results for the selection problem. The ranking validity of the seven models is examined by the validation process using fuzzy clustering and the most valid model is selected. With the development of the approach and the three empirical applications, this study makes significant methodological and practical contributions. The approach addresses the validity issue of the cardinal rankings generated by different fuzzy MCDM models. In practical applications, the subjective attitude of the decision maker is effectively incorporated into the decision making process. With its simplicity in both concept and computation, the approach has a general applicability for solving general MCDM problems, and is particularly suited to decision situations where the ranking results produced by different fuzzy MCDM models differ significantly.

The α-Discounting Method was developed to be an alternative to and extension of the Analytical Hierarchy Process (AHP) to solve multi-criteria decision-making (MCDM) problems with non-commensurable and conflicting criteria. In contrast to the AHP, this method works not only for pairwise comparisons but also for n-wise comparisons if relative importance of criteria can be expressed in a system of linear homogenous equations. This method also has a comparative advantage as it can transform those MCDM problems, classified as inconsistent by the AHP, into a consistent form. This study briefly compares the two methods and then develops the Fuzzy α-Discounting Method for Multi-Criteria Decision Making (Fα-DM MCDM). Two illustrative fuzzy MCDM problems from the literature have been solved to show how the Fα-DM MCDM works.

Analytic Hierarchy Process (AHP) is a tool of decision making technique which complies with complex decision making in any kind of situations. AHP deals with structuring the hierarchical layer to perform the preference judgement of each criterion and alternatives in multi-criteria decision making (MCDM) problems. The sequence of AHP structure unfortunately lack of certainty as the evaluation consists of vagueness. Thus, the theory of intuitionistic fuzzy sets (IFS) is integrated with AHP method to deal with these uncertainty and vagueness of the AHP preference judgement. The aim of this paper is to propose a new intuitionistic fuzzy analytic hierarchy process (IF-AHP) method characterised by new preference scale of pair-wise comparison matrix measurement. The new preference scale considers the degree of hesitation of IFS in expressing the conversion of consistency to a triangular intuitionistic fuzzy numbers (TIFNs). The values of hesitation degree are averaged to test consistency of matrix judgment. The intuitionistic fuzzy weighted averaging (IFWA) is utilized to aggregate the matrix assessment of the decision makers (DMs) into a group opinion. Modified intuitionistic fuzzy entropy is used to obtain the entropy weights of each criterion and alternatives. Three MCDM problems were used to illustrate the proposed method. It is found the ranking of MCDM problems using the proposed method were slightly inconsistent with the original ranking.

Due to the diversity of information, many decision-making problems cannot be solved based on a single criterion. The complexity of assessment objects and the limitations of individual cognition cause the opinions given by experts uncertain, which further aggravates decision-making difficulties. Although fuzzy sets and intuitionistic fuzzy sets are proposed to express vague information, they still have the problem of losing information. As a tool to express uncertain information, interval techniques can effectively prevent the loss of information and improve the accuracy of decision-making. In this regard, many scholars applied interval analysis techniques and their fuzzy extensions to solve multi-criteria decision-making (MCDM) problems. This study reviews 195-related articles published from 2007 to 2022, and analyzes the research progress of interval analysis techniques and their fuzzy extensions in MCDM problems. Through bibliometrics analyses, publication and citation trends as well as productive countries can be intuitively and quantitatively obtained. Then, we review theories and methods regarding the combination of interval techniques and MCDM methods, and find that interval-valued fuzzy sets are extensively discussed and combined with TOPSIS. Next, real-world applications of these publications are reviewed, and we obtain that interval techniques are mainly used in supply chain selection. Finally, we propose future directions regarding interval techniques in MCDM problems. It is hoped that this study would be helpful for scholars and practitioners to carry out further research.

n modern aircraft design, increased attention is being paid to the conceptual and preliminary design phases so as to increase the odds of creating a design that will ultimately be successful at the completion of the design process. Since aerospace systems are complex systems with interacting disciplines and technologies, the decision makers dealing with such design problems are involved in balancing multiple, potentially conflicting attributes/criteria, transforming a large amount of customer supplied guidelines into a solidly defined set of requirement definitions. As a result, the criteria have to be all simultaneously taken into account and a compromise essentially becomes part of the decision making process. Various methods and techniques are available to deal with such sort of multi-criteria decision making (MCDM) problems. In the 1970’s, Saaty proposed the Analytic Hierarchy Process (AHP), which facilitates the MCDM problems that have a hierarchical structure of attributes by reducing complex decisions to a series of pair-wise comparisons. In this method, the preference information is elicited as the pair-wise comparisons between attributes or alternatives and treated using the eigenvector method. The other straightforward method to handle the MCDM problem is the Overall Evaluation Criterion (OEC) technique, presented in Ref 3. The OEC is a single metric and is obtained by summing multiple non-dimensional attribute metrics normalized by the metric values of a relevant baseline. Another commonly used MCDM technique is the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). The “best” solution chosen by TOPSIS is the alternative that is the closest to the positive ideal solution and the furthest from the negative ideal solution. The separation between each alternative solution and the ideal solution, which is determined by the weighted criteria, is rather sensitive to criterion weights, so typically several weighting scenarios are investigated to determine the final solution. Among these developed MCDM methods, different methods have different underlying assumptions, information requirements, analysis models, and decision rules that are designed for solving a certain class of decision making problems. This implies that it is critical to use the most appropriate method to solve the problem under consideration since the use of unsuitable method always leads to misleading design decisions. Consequently, bad design decisions will result in big loss to the society, such as property damage or personal injury. Thus, it is necessary to review the existing MCDM methods, discuss in depth their advantages, disadvantages, applicability, computational complexity, etc. in order to make right decision when choosing the right method for the given problem. In this paper a hybrid MCDM method is developed to deal with the problem under consideration. Relative weights of the evaluation criteria are elicited by using the eigenvector method to describe the decision maker’s preference information. The TOPSIS method is used to analyze the qualitative and quantitative data of input parameters and find the solution to the given problem. An aircraft technology selection problem is conducted as a proof of implementation to demonstrate the functionality and effectiveness of the proposed methodology.

The requirements, conditions and values encompassing a decision-making situation determine the decision maker's feasible alternative solutions and circumscribe the best solution. Various approaches have been suggested in the literature to identify the best solution for a class of problems called multicriteria decision making (MCDM). MCDM problems may be deterministic or non-deterministic. These approaches employ different methodologies and usually produce dissimilar solutions. Therefore, it is worthwhile to examine and compare the available MCDM methods. This paper focuses mainly on deterministic MCDM situations and methods and only a brief reference is made to their non-deterministic counterparts. We introduce a new performance measurement method called operational competitiveness rating (OCRA) and discuss its use as a deterministic MCDM tool. We demonstrate OCRA's application to a process selection problem that is adapted from an actual situation that involves qualitative data. W e compare the results obtained by OCRA with the results of analytic hierarchy process and data envelopment analysis to understand their similarities and differences. The comparison of these three non-parametric performance measurement tools provides some useful insights into their behaviour in actual MCDM situations.

Selecting a discrete multiple criteria decision making method for Boeing to rank four global market regions

Purpose – The purpose of this paper is to report a study in which analytical network process (ANP) was used for selecting the best concept from sustainability view point. Design/methodology/approach – The concept selection in the sustainability viewpoint is a typical multi-criteria decision-making (MCDM) problem involving complex interrelationship among the decision criteria. The formulated MCDM problem of sustainable concept selection was solved using ANP. The sensitivity analysis was also being conducted to validate the results. Findings – The interrelationship among the decision criteria was analyzed using ANP, and the best alternative was selected based on the computation of Product Sustainability Weighted Index (PSWI). The selected best alternative was subjected to implementation in the case organization. Research limitations/implications – The study deals with the formulation of sustainable concept selection as a typical MCDM problem and providing solutions using ANP. The best alternative “weight reduction” was subjected to implementation. The developed MCDM problem also could be solved using hybrid MCDM methods. Practical implications – The study focuses on selecting the best sustainability concept for an Indian automotive component manufacturing organization. Hence, the inferences being derived from the study are practically feasible and contribute toward the improvement of product sustainability. Originality/value – The formulation of a hierarchical model for sustainable concept selection as MCDM problem and generating solution using ANP is the contribution of the authors.

The analytic hierarchy process (AHP) has been a widely used method for handling multi-criteria decision-making (MCDM) problems since the 1980s. However, it postulates that criteria are independent and static, which may not always hold true in realistic situations. Although several methods have been proposed to relax the assumption of independence between criteria in the AHP, such as the analytic network process (ANP), these methods do not account for time-dependent criteria in the AHP. Consequently, this paper presents an innovative method that integrates dynamic Bayesian networks (DBNs) with the AHP to model dynamic interdependencies between criteria in MCDM problems. We illustrate the proposed method through a comprehensive numerical example and compare the result with the conventional AHP. The findings suggest that the proposed method extends the AHP to accommodate time-dependent issues and, when ignoring specific information, reduces to the conventional AHP, thereby demonstrating that our approach serves as a more general AHP model.

Optimality of flood influencing factors for flood hazard mapping: An evaluation of two multi-criteria decision-making methods

Assessment of Occupational Safety and Health (OSH) of process plants is an important aspect in safety management. Quantitative assessment is necessary to compare the OSH status of different plants. For this purpose, usually, an evaluation team is constituted which evaluates the OSH performance based on the available OSH related information. The evaluation team arrives at a decision through discussions and mutual agreement. However, while working in a group, there always remains a possibility of ‘groupthink’ behavior which may lead to an inappropriate decision without critically analyzing alternative hypotheses. Multi Criteria Decision Making (MCDM) methods are useful in avoiding the groupthink behavior while decision making. Here, we used two MCDM methods, Delphi method and Analytic Hierarchy Process (AHP) Method, to develop an OSH index from a set of safety indicators. Delphi method was used to prioritize the safety indicators. Relative weights of the safety indicators to the final OSH index were estimated by AHP method. AHP analysis resulted that leading safety indicators have greater weightage compared to lagging indicators while assessment. We believe that this method will be useful for safety professionals and regulators in evaluation of OSH performance by using easily available information from plant authorities.

Multi-criteria decision making (MCDM) deals with the process of making decisions among a number of experts who evaluate several alternatives or solutions over different criteria. Real-world MCDM problems often present changing contexts in which uncertainty has not a probabilistic nature, in such cases, linguistic information has been successfully applied to model such uncertainty. The use of linguistic information in decision making implies making computations with it to solve linguistic MCDM problems, under these conditions, computing with words methodology allows to obtain linguistic outputs from linguistic premises. There is a large amount of computational models to carry out these processes in the specialized literature, being often difficult to make comparison among different models for a specific MCDM problem. Both the large number of existing models and the increasing difficulty of MCDM problems make difficult the resolution of such problems for the decision makers without any additional support. Therefore, the need of decision support tools that consider all relevant information seems evident. For this reason, FLINTSTONES was proposed as a software suite for dealing with MCDM problems within linguistic contexts based on the 2-tuple linguistic model. This contribution presents an updated version, FLINTSTONES 2.0, that includes new useful features for supporting MCDM resolution processes.

Fuzzy set based models and methods of multicriteria group decision making