Multiple Attribute Decision Making Research Articles

Multiple‐attribute decision‐making spherical vague normal operators and their applications for the selection of farmers

AbstractIn this article, we discuss a few fresh approaches to the spherical vague normal set (SVNS) approach to multiple attribute decision‐making (MADM) problems. A new generalization of the vague set (VS) and the spherical interval valued fuzzy set (SIVFS) is the spherical vague set (SVS). The spherical vague number (SVN) concepts consolidate normal fuzzy number (NFN) and we defined the spherical vague normal number (SVNN) and some of its intriguing fundamental operations. The purpose of this article is to discuss a novel idea of spherical vague normal weighted averaging (SVNWA), spherical vague normal weighted geometric (SVNWG), generalized spherical vague normal weighted averaging (GSVNWA) and generalized spherical vague normal weighted geometric (GSVNWG) operators. We talked about a flowchart with an algorithm that uses this operators and the MADM approach. With the help of a numerical example, we interact the extended euclidean and hamming distance measures. In this communication, it is also important to elaborate on some key SVN approach characteristics based on various algebraic operations, such as idempotency, boundedness, commutativity, and monotonicity. They are quicker to find the best option, more straightforward and practical. Think about five farmers. The four factors that are taken into account for each of the five farmers are climate, water, soil, disease, and flood, and their corresponding weights are displayed. We want to select the best option from a large number of choices by comparing expert assessments with the criteria. As a result, the conclusions of the defined models are more precise and closely related to . We contrast some of the current models with the ones that have been proposed in order to demonstrate the dependability and utility of the models under investigation. The study's findings are also intriguing and fascinating.

Selection of Appropriate Global Partner for Companies Using q-Rung Orthopair Fuzzy Aczel–Alsina Average Aggregation Operators

The term “q-Rung orthopair fuzzy sets (q^{r}OFSs)” refers to the collection of the most differentiated ideas for expressing fuzzy data in decision-making rules. The q^{r}OFSs can gradually adapt the region of information by adjusting the parameter q ge 1 in response to the fluctuation degree, so assisting in the generation of more numerous possibilities. Aczel–Alsina triangular norm and conorm are superior operations that may create general and adaptable working principles for aggregating arguments. To make use of q^{r}OFSs and Aczel–Alsina triangular norms in “multiple attribute decision-making (MADM)” issues, a few q-rung orthopair fuzzy (q^{r}OF) Aczel–Alsina aggregation operators: q^{r}OF Aczel–Alsina weighted average (q^{r}OFAAWA) operator, q^{r}OF Aczel–Alsina order weighted average (q^{r}OFAAOWA) operator, q^{r}OF Aczel–Alsina hybrid average (q^{r}OFAAHA) operator for aggregating q^{r}OFSs are exhibited in this article to fix the MADM issues based on q^{r}OFSs. Some significant properties are additionally demonstrated. A technique for taking care of the MADM issues is proposed based on these created operators. The adequacy of the suggested technique is shown by means of a numerical example on enterprise partner selection, a lot of investigations and correlations with other existing strategies. The sensitivity of the proposed aggregation operators to decision-making findings is investigated. The demonstration outcomes suggest that the suggested technique has fulfilling universality and adaptability in aggregating q^{r}OF data and acquiring the correlations of criteria and the attitudes of “decision-makers (DMs)” and is doable and compelling for taking care of the MADM issues dependent on q^{r}OFSs.

Multiple Attribute Decision-Making Based on Bonferroni Mean Operators under Square Root Fuzzy Set Environment

The intuitionistic fuzzy set (IFS), which has a membership and non-membership degree, is a controlling and effective device for dealingwith fuzziness and uncertainty. Recently,the square root fuzzy set which is one of the efficient generalizations of an IFS for dealing with uncertainty and haziness in information has beenintroduced. In this study, a novel method for multiple attribute decision-making (MADM) based on SR-fuzzy information isinvestigated. Since aggregation operators are significant in the decision-making (DM) process, to achievethis goal, the current paper suggests a variety of novel Bonferroni mean and weighted Bonferroni mean operators to aggregate the SR-fuzzy values for the various decision-maker preferences. To achieve this goal, the current paper suggests a variety of novel Bonferroni mean and weighted Bonferroni mean operators to aggregate the SR-fuzzy values for the various decision-maker preferences. SR-fuzzyBonferroni mean operator and weighted SR-fuzzy Bonferroni mean operator are established and their properties are described. Then, we constructed a MADM approach using the proposed operators for the SR-fuzzyinformation and proved the approach with a mathematical example. Inthe end, a comparative study ofthe developed and existing approaches has been discussed to evaluate the pertinency and practicality of the proposed DM technique. Received: 24 August 2022 | Revised: 21 October 2022 | Accepted: 1 November 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.

Comparison of Health Status Indicators in Iran with the Eastern Mediterranean Countries Using Multiple Attribute Decision-Making Methods.

Improving public health is the main goal of healthcare systems across the world. Healthcare policymakers often use comparisons between different healthcare systems to better position their country and use the outcome to develop novel strategies to improve their own public health. The present study aimed to compare the health status indicators in Iran with those of the Eastern Mediterranean (EM) countries using the multiple attribute decision-making (MADM) methods. A descriptive-analytical study was conducted in 2021 at Shiraz University of Medical Sciences, Shiraz, Iran. Data on the ranking of health status indicators in EM countries were obtained from the annual publications of the World Health Organization, World Health Statistics (2016-2020). As part of the MADM mathematical models, the "criteria importance through intercriteria correlation" (CRITIC) model was used to assign weights to health status indicators. In addition, the "multi-criteria optimization and compromise solution" (VIKOR) model was used to rank the EM countries. The results showed that Bahrain and Somalia ranked first and last on health status indicators, respectively. Iran was ranked fifth among the EM countries. However, while Iran has a better status on all indicators than the mean value of all EM countries, there is a significant gap between the health status in Iran compared to the top-ranked countries. Health care strategies adopted by top-ranked countries, such as Bahrain and Qatar, can be used by Iran and other EM countries as a model to improve their healthcare system.

Decision support system in predicting pneumonia transmission in toddlers based on life patterns

The increasingly widespread spread of pneumonia during the Covid-19 pandemic that attacks children, especially toddlers, has caused the under-five mortality rate (IMR) to increase. Pneumonia is the most worrying thing for parents because it is an acute respiratory disease that attacks the lungs due to the lack of special health information for toddlers that parents get about lifestyle during the Covid-19 pandemic. With the health information from the government for toddlers through the media, especially in improving a healthy lifestyle, it is expected to minimize the transmission of pneumonia. The problem that occurs in the transmission of pneumonia among the general public is air pollution by the presence of bacteria, viruses, and fungi or parasites and the most common types of bacteria attack Streptococcus Pneumonia and Haemophilus Influenzae Virus type b (Hib). The weakening of the condition of children with this disease due to lack of adequate oxygen intake so that children will feel pain in the esophagusarea when breathing. To be able to predict the transmission of pneumonia in toddlers based on lifestyle, the Fuzzy Multiple Attribute Decision Making (Fuzzy MADM) method through a Decision Support System (DSS) is used.

Exploring 2-rank strategic weight manipulation in multiple attribute decision making and its applications in project review and university ranking

In some real multiple attribute decision making (MADM) problems, sometimes, it is time-consuming and unnecessary to obtain a complete ranking of alternatives, thus, a decision maker would classify the alternatives into two ordered categories, forming a 2-rank MADM problem. Occasionally, a decision maker can manipulate the desired 2-rank results by strategically setting attribute weights. This process is called 2-rank strategic weight manipulation (2RSWM). First, this study defines the 2-rank range of alternatives. Subsequently, several mixed 0–1 linear programming models (MLPMs) are constructed to obtain the 2-rank range and the strategic attribute weight vector of the desired 2-rank result of the alternative(s) of the decision maker. Furthermore, we provide conditions for the existence of the strategic attribute weight vector based on the 2-rank range of the alternatives and the proposed MLPMs. Finally, two illustrative examples and two simulation experiments are conducted to validate the effectiveness of our proposed models. Due to the ordered weighted averaging (OWA) operator having smaller average width of the 2-rank range, and a larger minimum distance between the impersonal and strategic attribute weight vectors, we argue that the OWA operator has a better performance than the weighted averaging (WA) operator in defending against 2RSWM.

A New Vertical Handover Prediction Method for Heterogeneous Wireless Networks

Long Term Evolution (LTE) is the fourth generation (4G) cellular network technology that provides improved performance that related to data rate, coverage and capacity compared to earlier cellular systems. The addition of many small cells in a heterogeneous network configuration provides a means to enhance capacity through extreme frequency reuse. The heterogeneous environment of different network technologies can provide high data rate and enhance multimedia services, but it is challenging to provide optimized handover (HO). In this paper, a new method is proposed to select the optimal network from available networks to which a UE may be handed over to achieve better QoS performance. The aggregation of multiple criteria for calculation of overall ranking of networks is obtained by Analytical Hierarchy Process (AHP) [1] and is combined with the history of previously visited cells and regression analysis of the Layer 1 (L1) and Layer 3 (L3) filtered Received Signal Strength (RSS) data for prediction of future values. The AHP is used to calculate the weights for the system attributes and to rank the available networks based on multiple criteria Multiple Attribute Decision Making (MADM). The sequence of visited cells is used in target network selection as it reduces frequent handover. The sequence of visited cells would be modeled as a Markov Chain. To assess RSS, beside L1, L3 filtering RSS prediction and smoothing is used to predict future signal samples to hasten the process of HO. RSS forecasting is used to predict handover necessity so as to reduce the handover delay. The results show that the number of handovers is reduced by up to 50% compared to the conventional AHP. The results also show that the threshold crossing rate and average duration of fades are reduced by 47% compared with the AHP. Handover delay is also reduced by up to 40 ms due to RSS forecasting.

Some novel point operators and multiple rounds voting process based decision-making algorithm under picture fuzzy set environment

Picture fuzzy set (PFS), a generalization of the Atanassov-intuitionistic fuzzy set, is one of the most trustworthy tool to handle the uncertainties in the data during the decision-making process. The objective of this paper is to present some different kinds of the point operators for the PFS. The key feature of the presented point operators is to provide a smaller degree of uncertainty which is equivalent to the corresponding PFS. The desirable properties of the proposed operators are also derived in details. Also, we provide a memoryless multiple rounds voting process under the PFS environment to strength the result, and hence deduce a relationship between the proposed PFS and the existing set. In addition to this, we address the MADM (multiple attribute decision-making) problems by presented an algorithm to solve the problem based on the proposed point operators. The applicability and the superiority of the proposed algorithm are shown through two numerical examples. At last, the validity of the proposed algorithm is tested using different test criteria.

Location Selection of Low-carbon Logistics Park Based on the Neutrosophic Numbers Multiple Attribute Decision Making

Location Selection of Low-carbon Logistics Park Based on the Neutrosophic Numbers Multiple Attribute Decision Making

Ranking the Alternatives With a Modified TOPSIS Method in Multiple Attribute Decision Making Problems

In this article, we present a method for ranking alternatives in multiple attribute decision making (MADM) and group MADM problems. Our method first determines the importance of separations of each decision from positive ideal solution and negative ideal solution and then computes the relative closeness of each alternative. The alternatives are ranked based on relative closeness. The method is illustrated using three examples and the results are compared with other methods.

A Novel Operator to Solve Decision-Making Problems Under Trapezoidal Fuzzy Multi Numbers and Its Application

This article investigates solutions to multiple attribute decision-making (MADM) problems in which the attribute values take the form of trapezoidal fuzzy multi-numbers. To do this, this paper proposes a kind of mean aggregation operator called the Bonferroni harmonic mean operator for aggregating trapezoidal fuzzy information. Then, an approach that is a solution algorithm has been developed to find a solution to multi-attribute decision-making problems. Afterwards, an illustrative example has been given to verify the developed approach and to show its usefulness and efficiency. Finally, a comparison table has been presented to compare the proposed method with some existing methods.

DECISION SUPPORT SYSTEM BY USING MULTIPLE ATTRIBUTE DECISION MAKING WITH SIMPLE ADDITIVE WEIGHTING METHOD (MADM-SAW) METHOD IN SELECTING THE BEST BROMELAIN

Bromelain is a protease enzyme obtained from pineapple. In choosing bromelain only pay attention to its chemical analysis and there is no rank yet. Therefore, research has been carried out in making the best bromelain decision using the Multiple Attribute Decision Making with Simple Additive Weighting Method (MADM-SAW) method based on the shelf life of pineapples. The basic concept of this method is to find the weighted sum of the performance branches for each alternative (treatment) of all attributes so that the best alternative will be obtained in the selection of crude bromelain enzymes based on pineapple storage. The results showed that the best pineapple fruit was with storage of day 0 as rank 1, with a total value was 0.9025.

Tackling Comprehensive Evaluation of Tourism Community Resilience: A Probabilistic Hesitant Linguistic Group Decision Making Approach

Community-based tourism (CBT) has been adopted as an effective and practical solution to land use policies by governments that simultaneously pursue upgrading of local economy, conservation of local ecosystem and development of local communities. Confronting with new normality of detrimental eventualities in situated environments, destination management organizations (DMOs) or local governments have to employ effective governance strategies for fostering tourism community resilience in order to sustain development of CBT destinations. In viewing of that facilitating development through evaluation usually manifests as an efficient strategy in governance practices, this paper contributes to fill two main gaps in tackling comprehensive evaluation of tourism community resilience. Firstly, by noticing the fact that current literature overlooks processual characteristics of tourism community resilience, which originate from integration of disaster management and destination management (DM2), we have developed an analytical framework comprised of six attributes for comprehensively evaluating tourism community resilience. Secondly, aiming at the phenomena that cognitive assessments on attributes of tourism community resilience often exhibit complicate uncertainties caused by low-structured or ill-structured problem nature, we have put forward a powerful expression tool of probabilistic dual hesitant fuzzy uncertain unbalanced linguistic set (PDHF_UUBLS) to simultaneously capture evaluators’ cognitive characteristics of decision hesitancy, bipolar epistemic notions and relative importance among assessments. Then by formalizing comprehensive evaluation of tourism community resilience as a multiple attributes decision making process, we construct an effective multiple attributes group decision making (MAGDM) approach with assessments in the form of PDHF_UUBLS. Theoretical analyses verify the effectiveness of our constructed MAGDM approach and also show the approach avoids potential information distortion in comparison with other approaches. Overall, this paper provides effective and pertinent solutions, with both analytical framework and methodology, to the urgent task of comprehensive evaluation of tourism community resilience in DM2 agenda, thereby is of apparent significance in governance practice of CBT.

Fuzzy multi-attribute decision making method based on new similarity measure under interval-valued neutrosophic sets

Interval-valued neutrosophic set (IVNS) plays an important role in dealing with imprecise judgment information. For a multi-attribute decision making problem, the information of alternatives under different attributes is given in the form of interval valued neutrosophic number(IVNN). The objective of the presented paper is to develop a multiple-attribute decision making (MADM) method under interval-valued neutrosophic sets(IVNSs) using the new similarity measurement. The similarity measurement of IVNSs has always been a research hotspot. A new similarity measurement of IVNSs is first proposed in this paper based on Chebyshev distance. The proposed method enriches the existing similarity measurement methods. It can be applied to not only IVNSs, but also single-valued neutrosophic sets(SVNSs). The influence of each attribute on the decision-making result can be described by the weight. How to formulate the weight scientifically is vital as well. In this paper, the objective weight is calculated by normalizing the grey correlation coefficient obtained by a score function which can be applied to IVNSs. The objective weight is then combined with the subjective one by considering an adjustment factor with the weighted summation method. The adjustment factor is determined by the importance of subjective weight. Finally, an example is used to illustrate the comparison results of the proposed algorithm and other three ones. The comparison shows that the proposed algorithm is effective and can identify the optimal scheme quickly.

A Novel D–SCRI–EDAS Method and Its Application to the Evaluation of an Online Live Course Platform

D number theory removes the constraints of mutual exclusion and completeness in the frame of discernment of DS evidence theory, and is therefore widely used to deal with uncertain and incomplete information. EDAS (evaluation based on distance from average solution) selects the optimal solution according to the distance from each solution to the average. This method is very suitable for solving issues of multiple attribute decision making (MADM) with conflicting attributes. In this study, we propose an evaluation method that combines D numbers with the EDAS method. D number theory is used to express the evaluation of the alternatives for experts. Then, we use the SCRI (stepwise comparison and replacement integration) method of D numbers for data integration, and finally the EDAS method is used to select the optimal one by sorting the alternatives. We apply this method to address the user experience evaluation problems of platforms that offer online live courses and compare the evaluation results with other methods to verify the applicability and practicability of the method.

Designing an efficient vaccine supply chain network using a two-phase optimization approach: a case study of COVID-19 vaccine

The upsurge of the COVID-19 pandemic and its consequences on the world’s population has led to the accelerated production of vaccines across the world. The next challenge in the fight against COVID-19 is to distribute those vaccines to the right person in an optimized way by establishing an efficient vaccine supply network. Consequently, this paper studies an integrated two-phase planning framework for the vaccine distribution network. In the first phase, the population target is classified into several groups to determine their priority for vaccination using a multiple attribute decision making (MADM) technique. The second phase uses a mathematical model to decide on the location of distribution centers, inventory policies, and routing decisions to minimize the total procurement, inventory, and distribution costs. Since the proposed model falls into the category of NP-hard problems, a metaheuristic algorithm entitled population-based simulated annealing (PBSA) is developed to solve the presented model. Besides, the improved grey wolf optimizer (I-GWO) is customized and used as another solution method. A few generated datasets are utilized to evaluate and validate the performance of these solution methods. In addition, this methodology is implemented in a case study of the COVID-19 vaccine supply chain in Iran to demonstrate its practicability.

Large-scale group decision-making (LSGDM) for performance measurement of healthcare construction projects: Ordinal Priority Approach.

People with various skill sets and backgrounds are usually found working on projects and thus, group decision-making (GDM) is one of the most important functions within any project. However, when projects concern healthcare or other critical services for proletariat or general public (especially during COVID19), the importance of GDM can hardly be overstated. Measuring the performance of healthcare construction projects is a critical activity and should be gauged based on the input from a large number of stakeholders. Such problems are usually recognized as large-scale group decision-making (LSGDM). In the current study, we aim to propose a decision support system for measuring the performance of healthcare construction projects against a large number of experts using ordinal data. The study identifies several key indicators from literature and recorded the observations of a large number of experts about these indicators. After that, the acceptable range of complexity is specified, the Silhouette plot is provided to find the optimal number of clusters, and the ordinal K-means method is employed to cluster the experts’ opinions. Later, the confidence level is measured using a novel Weighted Kendall’s W for the optimal number of the clusters, and the threshold is checked. Finally, the conventional problem is solved using the Group Weighted Ordinal Priority Approach (GWOPA) model in multiple attributes decision making (MADM), and the performance of the projects is determined. The validity of the proposed approach is confirmed through a comparative analysis. Also, a real-world case is solved, and the performance of some healthcare construction projects in China is gauged with a comprehensive sensitivity analysis.

DECISION SUPPORT SYSTEM FOR WI-FI MODEM SELECTION USING MULTIPLE ATTRIBUTE DECISION MAKING WITH TOPSIS

Modem is a device that has the ability to connect cellphones, PCs or laptops to the internet network. The type of modem that is popular today is MiFi or Wi-Fi Modem, which is a portable modem that makes it easier for users to carry the device anywhere. Currently, there are many Wi-Fi modems circulating in the market with various brands and brands that have different specifications and capabilities. For this reason, people must be observant in choosing the right Wi-Fi Modem for their needs. The selection of the right Wi-Fi Modem will maximize the performance of the internet network so as to facilitate activities in internet use. The purpose of this research is to be able to develop a decision support system by implementing the Multiple Attribute Decision Making (MADM) approach using the TOPSIS method for the selection of a Wi-Fi Modem, in order to facilitate the selection of the best Wi-Fi Modem and according to user needs. Based on black-box testing, it shows that the system built can run well. In addition, the calculation results of the TOPSIS method generated by the system with manual calculations produce the same value.

A Generalized Belief Entropy With Nonspecificity and Structural Conflict

The Dempster–Shafer (D-S) evidence theory has been widely used in many fields from probabilistic inference, information fusion to decision analysis due to its superiority to formulate uncertain and incomplete information under a weaker condition than the Bayesian probability theory. To support the elicitation of basic probability assignment (BPA), numerous uncertainty measures have been proposed in the recent decades, which take into account nonspecificity and structural conflict (SC) in either the entropy-alike or nonentropy form. This article is focused primarily on developing a generalized belief entropy to measure and quantify the uncertainty of BPA in a consistent and comprehensive way. The definition of the proposed belief entropy involves three components. Specifically, the first component is Shannon’s definition of entropy of probability functions which can be interpreted as the measure of discord of the mass function among various focal elements. The second component is the measure of total nonspecificity which is the generalization of Dubois–Prade’s definition of nonspecificity measure. Furthermore, the critical component introduced in this article is the utilization of the SC coefficient within an evidence, which fully considers the intersection between each pair of focal elements of the BPA. The characteristics and properties of the new generalized belief entropy are analyzed systematically with theoretical proofs. Finally, the proposed belief entropy is embedded to a three-tuple approach to deal with a practical multiple attribute decision-making (MADM) problem to demonstrate its validity and strengths of measuring nonspecificity and SC in uncertain decision making.

SISTEM PENDUKUNG KEPUTUSAN TINDAK PIDANA PADA PENGADILAN NEGERI LUBUK PAKAM MENGGUNAKAN FUZZY MADM

The foundation of the judiciary in Indonesia is a judiciary based on Pancasila which places human dignity in its place and carries out the protection and guarantee of human rights. In making a decision, the judge's decision must always be based on a case delegation letter containing all charges for the defendant's guilt. The judge must be able to process and process the data obtained during the trial process in terms of evidence, witness statements, defense of the defendant, as well as prosecutors' demands, and psychological content. The task of legal judges in determining the decision to be handed down to the defendant is very heavy. Therefore, a system is needed to assist the judges in court, in this case, the judge, so a decision support system is needed to facilitate judges in making decisions based on the facts of the investigation and the facts of the trial. The aim is to assist judges in making decisions on criminal cases of murder that are suspected of being accused. This support system will be applied using the Fuzzy Multiple Attribute Decision Making (FMADM) method using the criteria of Accompanying Allegations, Goods/evidence, mitigating and aggravating things. The results show that the decision support system can help judges, where the system provides recommendations for decisions against the defendant such as the sentence does not need to be reduced, the sentence is reduced and the defendant is released.