MCDM Problems Research Articles

Performance evaluation model of transportation infrastructure: Perspective of COVID-19.

The transportation systems are facing major challenges due to changes social environment caused by the COVID-19 pandemic. How to construct a suitable evaluation criterion system and suitable assessment method to evaluate the status of the urban transportation resilience has become a predicament nowadays. Firstly, the criteria for evaluating the current state of transportation resilience involve many aspects. New features of transportation resilience under epidemic normalization are exposed, and previous summaries focusing on resilience characteristics under natural disasters can hardly reflect the current state of urban transportation resilience comprehensively. Based on this, this paper attempts to incorporate the new criteria (Dynamicity, Synergy, Policy) into the evaluation system. Secondly, the assessment of urban transportation resilience involves numerous indicators, which make it difficult to obtain quantitative figures for the criteria. With this background, a comprehensive multi-criteria assessment model based on q-rung orthopair 2-tuple linguistic sets is constructed to evaluate the status of transportation infrastructure from perspective on the COVID-19. Then, an example of urban transportation resilience is given to demonstrate the feasibility of the proposed approach. Subsequently, sensitivity analysis about parameters and global robust sensitivity analysis are conducted, and comparative analysis of existing method is given. The results reveal that the proposed method is sensitive to global criteria weights, so it is suggested that more attention should be paid to the rationality of the weight of criteria to avoid the influence on the results when solving MCDM problems. Finally, the policy implications regarding transport infrastructure resilience and appropriate model development are given.

Symmetric Matrices on Inverse Soft Expert Sets and Their Applications

The role of experts in decision-making is highly significant. The soft expert set (SES) is yet another milestone, as it plays a vital role in generalizing soft set theory. Soft expert set is creative and original because it involves more than one expert. This served as a driving force behind the creation of the current work. The present paper generalizes the framework of soft experts set to the inverse soft setting, namely the inverse soft expert set (ISES). Some captivating characteristic features of the soft expert set (SES) are established. On the inverse soft expert sets, numerous striking characteristics of symmetric matrices are presented. The ground-breaking idea of symmetric matrices on inverse soft expert sets (SMISES) is introduced. A MCDM problem for SMISES is described with an algorithm and interesting comparative analysis.

Confidence Levels-Based Cubic Fermatean Fuzzy Aggregation Operators and Their Application to MCDM Problems

Assessment specialists (experts) are sometimes expected to provide two types of information: knowledge of rating domains and the performance of rating objects (called confidence levels). Unfortunately, the results of previous information aggregation studies cannot be properly used to combine the two categories of data covered above. Additionally, a significant range of symmetric/asymmetric events and structures are frequently included in the implementation process or practical use of fuzzy systems. The primary goal of the current study was to use cubic Fermatean fuzzy set features to address such situations. To deal with the ambiguous information of the aggregated arguments, we defined information aggregation operators with confidence degrees. Two of the aggregation operators we initially proposed were the confidence cubic Fermatean fuzzy weighted averaging (CCFFWA) operator and the confidence cubic Fermatean fuzzy weighted geometric (CCFFWG) operator. They were used as a framework to create an MCDM process, which was supported by an example to show how effective and applicable it is. The comparison of computed results was carried out with the help of existing approaches.

Application of evidence reasoning algorithm and QUALIFLEX with single-valued neutrosophic set for MCDM

Multi-criteria decision-making methods often include attributes with uncertain nature in practical applications, single-valued neutrosophic set is an important approach to solve above problem. QUALIFLEX method is a traditional decision method that makes decision by comparing different permutations of alternatives. In this paper, QUALIFLEX method is developed to solve the MCDM problem with the element of decision matrix is the single-valued neutrosophic number. Besides, since the defects of the original QUALIFLEX method about fusing information of different attributes, this paper uses Dempster-Shafer theory of evidence to integrate the information about weight and alternatives. Finally, by comparing the result with other MCDM methods, we find that the new method can not only obtain reasonable results, but also explain the decision results by probability theory. This paper not only develops the traditional MCDM method, but also a meaningful attempt to apply AI algorithm in MCDM method.

The energy of interval valued neutrosophic matrix in decision-making to select the manager for the company project

The concept of energy in graphs and matrices is used effectively in all application areas. The energy of the matrix is an extension of graph energy. The usage of the energy idea in neutrosophic matrices makes it more flexible and applicable in multi-criteria decision-making environments. In this paper, we propose the energy approach in neutrosophic matrices with interval values. We determined the given energy’s upper and lower bounds. The energy is used of the interval-valued neutrosophic matrix to address the MCDM problem. A new strategy has been introduced called the interval-valued neutrosophic energy method to solve this problem. We look at the problem of choosing a qualified manager for a business project. A team of professionals in the company evaluates the options using neutrosophic numbers with interval values, and the energy method is then used to calculate the result. The result has been compared with the TOPSIS method results to show that the outcomes are similar.

Aggregation Operators for Interval-Valued Pythagorean Fuzzy Hypersoft Set with Their Application to Solve MCDM Problem

Experts use Pythagorean fuzzy hypersoft sets (PFHSS) in their investigations to resolve the indeterminate and imprecise information in the decision-making process. Aggregation operators (AOs) perform a leading role in perceptivity among two circulations of prospect and pull out concerns from that perception. In this paper, we extend the concept of PFHSS to interval-valued PFHSS (IVPFHSS), which is the generalized form of interval-valued intuitionistic fuzzy soft set. The IVPFHSS competently deals with uncertain and ambagious information compared to the existing interval-valued Pythagorean fuzzy soft set. It is the most potent method for amplifying fuzzy data in the decision-making (DM) practice. Some operational laws for IVPFHSS have been proposed. Based on offered operational laws, two inventive AOs have been established: interval-valued Pythagorean fuzzy hypersoft weighted average (IVPFHSWA) and interval-valued Pythagorean fuzzy hypersoft weighted geometric (IVPFHSWG) operators with their essential properties. Multi-criteria group decision-making (MCGDM) shows an active part in contracts with the difficulties in industrial enterprise for material selection. But, the prevalent MCGDM approaches consistently carry irreconcilable consequences. Based on the anticipated AOs, a robust MCGDM technique is deliberate for material selection in industrial enterprises to accommodate this shortcoming. A real-world application of the projected MCGDM method for material selection (MS) of cryogenic storing vessels is presented. The impacts show that the intended model is more effective and reliable in handling imprecise data based on IVPFHSS.

A Novel Z-Fuzzy AHP&EDAS Methodology and Its Application to Wind Turbine Selection

Modelling the reliability information in decision making process is an important issue to inclusively reflect the thoughts of decision makers. The Evaluation Based on Distance from Average Solution (EDAS) and Analytic Hierarchy Process (AHP) are frequently used MCDM methods, yet their fuzzy extensions in the literature are incapable of representing the reliability of experts’ fuzzy preferences, which may have important effects on the results. The first goal of this study is to extend the EDAS method by using Z-fuzzy numbers to reinforce its representation ability of fuzzy linguistic expressions. The second goal is to propose a decision making methodology for the solution of fuzzy MCDM problems by using Z-fuzzy AHP method for determining the criteria weights and Z-fuzzy EDAS method for the selection of the best alternative. The contribution of the study is to present an MCDM based decision support tool for the managers under vague and imprecise data, which also considers the reliability of these data. The applicability of the proposed model is presented with an application to wind energy investment problem aiming at the selection of the best wind turbine. Finally, the effectiveness and competitiveness of the proposed methodology is demonstrated by making a comparative analysis with the Z-fuzzy TOPSIS method. The results show that the proposed methodology can not only represent experts’ evaluation information extensively, but also reveal a logical and consistent sequence related to wind turbine alternatives using reliability information.

Novel Fermatean Fuzzy Bonferroni Mean aggregation operators for selecting optimal health care waste treatment technology

Fermatean fuzzy set (FFS) is an expedient tool in today’s world for coping with uncertainty, ambiguity, and incompleteness that emerge in many decision-making processes. In light of the relevance of FFS, this work offers a Multi-Criteria Group Decision Making (MCGDM) method together with Bonferroni mean and weighted Bonferroni mean operators in FF environment. These operators consider the interdependencies between the parameters into account during aggregation process in group decision making. On the other hand, health care waste (HCW) is an unavoidable byproduct of healthcare institutions, and so HCW management is vital owing to its negative influence on public health and environment. In this study, the proposed FFMCGDM has been applied to determine the optimal HCW treatment technologies (TT) for assuring sustainable environmental development, considering six TTs as alternatives assessing against nine criteria. Moreover, the proposed approach has been illustrated through an empirical case study of many district hospitals in Tripura, India. The obtained results are compared to other two well-known MCDM techniques as well as existing Fermatean Fuzzy Aggregation Operators (FFAOs). A sensitivity analysis has also been carried out to examine the stability of the model. From the analysis, it has been found that the proposed technique is competitive to obtain the optimal HCW TT. For further validation of the proposed method, a well-known MCDM problem, namely Green Supplier Selection (GSS) has been modeled and solved.

On spherical fuzzy distance measure and TAOV method for decision-making problems with incomplete weight information

A spherical fuzzy set is one of the reliable tools to handle the uncertainties in the data with the help of the three membership degrees: Positive, neutral, and negative. Under this environment, the paper aims to present a novel decision-making algorithm named as MCDM-TAOV with some novel distance measures using matrix norms. In the literature, several kinds of distance measures occur, but they fail to meet certain axioms of the measure. To overwhelm this, the proposed measure satisfies all axiomatic requirements and overcomes the hindrances in the existing distance measures. Several counter-intuitive examples are provided to justify the superiority of the proposed measures. Furthermore, we addressed the total area based on orthogonal vector (TAOV) methodology to solve the MCDM (“multi-criteria decision-making”) problems. A distance-based criteria weight determination method is presented to compute the unknown criteria weight. The approach has been demonstrated by assessing the third-party reverse logistics provider (3PRLP) problem. The practical applicability, comparative analysis and advantages of the study with other decision-making methods are furnished to depict the efficiency of the moulded process.

Soft Expert Symmetric Group and Its Application in MCDM Problem

Researchers are always inspired to broaden their explorations towards uncertainty theories, owing to their great interest and importance. Soft set theory plays a primary role among all recent uncertainty tools. Though this theory sounds good in all aspects, it has its own limitations due to a lack of experts. The novel idea of a soft expert set was brought up recently to address this issue. This strategy is innovative and inventive in the sense that it utilizes the expertise of numerous specialists. This novel idea inspired us a lot for the development of the present study. This paper introduces the notion of a soft expert symmetric group as a natural generalization of the symmetric group and soft expert set. Several interesting properties of soft expert symmetric groups are studied. Internal and external products of two soft expert symmetric groups and the homomorphism of soft expert symmetric groups are also presented. The application of a soft expert symmetric group in multi-criteria decision-making situations is also given in a lucid manner.

Novel Hybrid MPSI–MARA Decision-Making Model for Support System Selection in an Underground Mine

An underground mine is a very complex production system within the mining industry. Building up the underground mine development system is closely related to the installation of support needed to provide the stability of mine openings. The selection of the type of support system is recognized as a very hard problem and multi-criteria decision making can be a very useful tool to solve it. In this paper we developed a methodology that helps mining engineers to select the appropriate support system with respect to geological conditions and technological requirements. Accordingly, we present a novel hybrid model that integrates the two following decision-making components. First, this study suggests a new approach for calculating the weights of criteria in an objective way named the Modified Preference Selection Index (MPSI) method. Second, the Magnitude of the Area for the Ranking of Alternatives (MARA) method is proposed as a novel multi-criteria decision-making technique for establishing the final rank of alternatives. The model is tested on a hypothetical example. Comparative analysis confirms that the new proposed MPSI–MARA model is a very useful and effective tool for solving different MCDM problems.

Evaluation of the smart reverse logistics development scenarios using a novel MCDM model

The purpose of this study was to select the most promising smart reverse logistics system development scenario which would serve as a guideline for the decision-making in the process of building sustainable systems of circular economy and closed supply chains. Four development scenarios are defined in the study and evaluated by the representatives of the main stakeholders in relation to a broad set of sub-criteria classified within the six main criteria. To solve the defined problem, a novel multi-criteria decision-making model, combining the Delphi, Analytical Network Process (ANP) and COmprehensive distance Based RAnking (COBRA) methods in the fuzzy environment, was developed. The application of the developed model resulted in selecting the scenario which most effectively balances the wide application of Industry 4.0 technologies and the necessary resources. The scenarios imply the integration of the most effective Industry 4.0 technologies, such as the Internet of Things, Automated guided vehicles, Autonomous Vehicles, Artificial Intelligence, Big Data and Data Mining, Blockchain, Cloud Computing and Electronic/Mobile marketplaces, and their most realistic applications. The widest possible application of Industry 4.0 technologies does not necessarily guarantee the most acceptable development scenario and the solution should be sought in the area of common interest of all stakeholders. • Smart reverse logistics is a prerequisite for sustainable supply chains. • The application of Industry 4.0 technologies makes reverse logistics smart. • Various scenarios of smart reverse logistics development can be defined. • The selection of the most probable scenarios is a MCDM problem. • Novel fuzzy DANP-COBRA method is developed in the paper for solving this problem.

Analysis of Enterprise Resource Planning system using VIKOR Method

Enterprise Resource Planning (ERP) When implementing settings, most of the companies faced some problems, one of them is their requirements and how to decide the best ERP software that meets expectations Choosing Best Custom ERP Software has been around for a long time an important issue, Because the wrongly selected ERP software requires time and A company’s expenses and Impact on market share. On the other hand, ERP software selection is Several criteria are used to make the decision MCDM Problems in literature, to assess this type of problem Several methods have been intro-duced, one of them is Analytical Hierarchy Process (AHP), It is widely used in MCDM selection problems. COPRAS (Complex Proportionality Assessment) and Based on the concepts of AHP (Analytic Hierarchy Process). A new fuzzy MCDM method is proposed to evaluate the potential maintenance strategy. To estimate ratings and weights Linguistic terms are used. Fuzzy AHP estimation used to calculate the weights of criteria; then, fuzzy set theory and A ranking of al-ternatives is calculated based on COPRAS. The COPRAS method using for Enterprise Resource Planning (ERP) soft-ware selection. It includes General System Specifications (GSS), Manufacturing Module (PM), Financial Management Module (FMM), Quality Management Module (QMM), Sales and Distribution Module (SDM), Maintenance Manage-ment Module (MMM), We are taken erp, risk, quality, effectiveness, efficiency, user satisfaction, best, worst is alterna-tives. We are taken corporate vision, technology and system architecture, product functionality, service and support are evaluation parameter. Alternative: ERP, Risk, Quality, effectiveness, Efficiency, User satisfaction. Evaluation Preference: Corporate vision, Technology and system architecture, Product functionality, Service and support. In this from analysis VIKOR method is the most ideal solution Short-distance and negative-best The solution with the longest distance from the solution Determines, but the comparison of these distances Does not consider importance. From the result it is seen that ERP is got the first rank where as is the Effectiveness is having the lowest rank.

Multi-Criteria Group Decision-Making Models in a Multi-Choice Environment

The best–worst method (BWM) has recently demonstrated its applicability in addressing various decision-making problems in a practical setting. The traditional BWM method is based on deterministic information gathered from experts as pairwise comparisons of several criteria. The advantage of BWM is that it uses fewer calculations and analyses while maintaining good, acceptable consistency ratio values. A multi-choice best–worst method (MCBWM), which considers several options for pairwise comparison of preferences between the criteria, has recently been developed. The experts are given the option to select values from several comparison scales. The MCBWM technique has been shown to be better. Presenting the options for which an optimal solution has been found simplifies the calculation and establishes the ideal weight values. This study proposes two different mathematical programming models for solving multi-criteria decision-making problems having multiple decision-makers. The two methods are proposed considering the multi-choice uncertainty assumption in pairwise criteria comparisons. Additionally, it considers the best–worst method as the base model. The multi-choice uncertainty is applied to determine the best choice out of multiple choices. It gives a real-life scenario to the decision-making problems. Although there are many other forms of uncertainty, such as fuzzy, intuitionistic fuzzy, neutrosophic, probabilistic, etc., it focuses on choices instead of ambiguity in terms of the probabilistic or fuzzy nature of parameters. The parameter considered as multi-choice is the pairwise comparison. These parameters are handled by applying the Lagrange interpolating polynomial method. The proposed models are novel in terms of their mathematical structure and group decision-making approach. The models are formulated and further validated by solving numerical examples. It provides a framework for solving mcdm problems where the weightage to the decision-makers is also incorporated. The CR values for all the models of example 1 and 2, and the case study has been found acceptable.

Application of multi-criteria decision making technique in wire-cut EDM tool steel

The findings of a study on the application of the MCDM technique to select the best input parameters in wire-cut electrical discharge machining (wire-cut EDM) 90CrSi tool steel are presented in this paper. The TOPSIS method was used in the study to solve the MCDM problem, and the Entropy method was used to compute the weights of the criteria. In this work, six input parameters including the cutting voltage VM, the pulse on time ton, the pulse off time toff, the servo voltage SV, the wire feed WF, the feed speed SPD, and the workpiece cutting radius R were investigated. Also, a 27-2 design experiment was performed and a total of 32 experimental runs were conducted. The MCDM problem was solved. According to the findings of this study, the best experimental setup is experiment No. 7 with the following input parameters: VM=9 (V), Ton=12 (s), Toff=13 (s), SV=25 (V), WF=8 (mm/min), SPD=4.5 (mm/min), and R=9 (mm).

Understanding MCDM Preference Relations Index Method and Its Application

Custom table (PSI) method. Priority selection coding was developed by Mania & Butt (2010) to solve MCDM problems. As proposed, it is not necessary to assign comparative importance between attributes. Choosing a desirable machine is an important concern for the manufacturing company. The selection process has some important selection properties and the task of this process is to select the preferred machine from among the multiple candidate machines. The problem of machine selection is a problem that is determined by many characters. This paper solves the problem of machine selection, which generates an optional selection code in a Flexible production cell. A case study is used to demonstrate that the proposed method is effective and feasible. This article explores the use of the MCDM System of Choice (PSI) method to address various decision-making issues commonly encountered during the design phase of a production system life cycle. To demonstrate the feasibility, applicability, and accuracy of the PSI method in solving the decision-making problem at the design stage of the product life cycle, five examples are cited from the literature and compared with the results obtained by past researchers. Research has concluded that the BSI method is logical and highly relevant to material selection issues. The choice of marketing area is an important factor in sales activities. Many factors are required in choosing a marketing area, including rental prices, area, audience, and distance. For a decision to select this marketing area to be effective, the hotel needs a decision support system that can provide the best possible service to guests and deliver the right decision in implementing alternatives, as there are responsibilities and responsibilities of many departments

The Energy of rough neutrosophic matrix and its application to MCDM problem for selecting the best building construction site

An approach to data processing for relational databases is called a rough set theory. It is an interesting area of uncertainty mathematics that is mainly related to fuzzy theory. Rough set theory and neutrosophic set theory can be joined to create a powerful tool for dealing with indeterminacy. Neutrosophic matrices help decision-makers deal with multi-criteria decision-making by providing them with more useful and practical when we apply the concept of matrix energy. In this paper, we defined a Rough neutrosophic matrix and its energy. Some propositions, lower and upper limits of the rough neutrosophic matrix's energy were derived. The proposed energy of the rough neutrosophic matrix was applied in multi-criteria decision-making problems. The problem is to select the best place for constructing the school building. Applying the energy method to the MCDM problem became more relatable and produced good results.

Some fundamental Operations for multi-Polar Interval-Valued Neutrosophic Soft Set and a Decision-Making Approach to Solve MCDM Problem

Some fundamental Operations for multi-Polar Interval-Valued Neutrosophic Soft Set and a Decision-Making Approach to Solve MCDM Problem

A Comparative Study on Multi-Criteria Decision-Making in Dressing Process for Internal Grinding

The MCDM problem is very important and often encountered in life and in engineering as it is used to determine the best solution among various possible alternatives. In this paper, the results of the MCDM problem in the dressing process for internal grinding are presented. To perform this work, an experiment with six input parameters, including the depth and the time of fine dressing, the depth and the time of coarse dressing, non-feeding dressing, and dressing feed rate, was conducted. The experiment was designed according to the Taguchi method with the use of L16 orthogonal arrays. In addition, TOPSIS, MARCOS, EAMR and MAIRCA methods were selected for the MCDM to obtain the minimum SR and the maximum MRR simultaneously. In addition, the weight determination for criteria was implemented by MEREC and entropy methods. From the results, the best solution to the multi-criteria problem for the dressing process in internal grinding has been proposed.

Modeling of factors affecting investment behavior during the pandemic: a grey-DEMATEL approach

The study intends to examine the cause-and-effect relationship between Covid-19 and the factors affecting investment behavior in a South Asian economy. The investment behavior is considered as an MCDM problem. To address this problem, the study employs MCDM approach i.e., a blend of both DEMATEL and Grey theory due to its potential to deal with subjective judgments of investors. The results indicate that Covid-19 is the leading cause behind financial stress, psychophysiological health outcomes, investors' perception about the market, and investors' strategy. Among sub-factors, portfolio allocation is the most influenced sub-factor. Alteration in portfolio is a major challenge for emerging countries which have become attractive destinations for global investors. Overall, the significant contribution of the paper is to establish the interlinkages among the factors affecting investment behavior, given the uncertainty triggered by the pandemic. Although the literature provides evidence on this problem during normal situations, analysis of investment behavior during severe crisis is still lacking. The research will be immensely useful to different stakeholders such as government, policymakers, financial advisors, and investors in making their strategic or operational decisions.