Superiority And Inferiority Ranking Research Articles

On SIR-based MCDM approach: Selecting a charcoal firm using hybrid fuzzy number on a Triple Vague structure

Multi-Criteria Decision Making (MCDM) is a widely used method across various sectors to help decision-makers assess and choose the most suitable solutions from a range of available options. This research introduces an innovative framework called the Triple Vague (TV) structure for Superiority and Inferiority Ranking (SIR) within the context of MCDM. This framework incorporates interval fuzzy numbers and hesitant fuzzy numbers, including negative interval fuzzy numbers for different vague ideas. The main goal of this research is to present an effective solution to the challenge of selecting a firm, specifically focusing on the manufacturing of charcoal from waste coconut shells. The SIR approach is employed to establish two complete rankings, and a TV value is used to articulate the assessment information. In this article, our contribution lies in the proposed Triple Vague approach, which utilizes the superiority and inferiority ranking methodology to identify the best firm for charcoal manufacturing. Further, it demonstrates through an application example involving the selection of the most suitable firm among four different options based on specified criteria for charcoal production from coconut shell waste. Also, comparative analysis has been done with other MCDM methods like TOPSIS, Grey method and PROMETHEE method. This analysis examines that the proposed structure is better than other MCDM technique. Moreover, the results highlight the framework's capability to handle the hesitation, vagueness, and like other undesirable characteristics. Hence it provides valuable insights for decision-makers to thoroughly evaluate and consider the best alternatives.

Intuitionistic SIR Technique with Double Parameters to Detect the Operative Vaccine of COVID-19

Since 2019, COVID disease threats the world. It originated in China, and due to its easy spread characteristics, it spreads quickly among all the countries. As the world faced a new kind of virus, the government could not control its spread, and the fatality rate had also hugely increased. In 2021, the vaccine for the virus was introduced by various countries, which aids in controlling the spread and mortality rate. The mutant form of this virus exhibits high efficiency. So, this study analyzes the effective of COVID vaccines released after 2020 against all COVID variants through the fuzzy superiority and inferiority ranking (SIR) decision-making method. The vaccines and variants are analyzed by the [Formula: see text] matrix pattern under the fuzzy SIR-integrated weight method. The disease variants are taken as criteria and that are assigned integrated weight based on subjective, objective weight approaches. The intuitionistic fuzzy set-double parameter (IFS-DP) handles the vagueness by strictly including only membership and non-membership functions with the condition [Formula: see text]. As a result, the vaccines Moderna and Covishield are attained top rank and have a high likelihood to combat all the COVID variants compared to other vaccines. The reached outcomes are corroborated through sensitivity and comparative analysis. The sensitivity and comparative studies are measured by increasing the order of IFS-DP and extant fuzzy MCDM methods, respectively. The comparison of these results with those obtained from the proposed method is similar to the sensitivity study, along with minor variations observed in the comparative study. Therefore, the proposed method generates an efficient outcome. Moreover, the testing hypothesis technique is applied to check the efficiency of proposed method.

Green Supplier Selection in the Construction Industry Using a Novel Fuzzy Decision-Making Approach

Concern for the environment among individuals, stakeholders, and governments has steadily increased during the last few decades. Strict environmental regulations and policies have prompted enterprises to develop positive improvements such as green supply chain selection practices. Selecting a suitable supplier is essential for the competitiveness and profitability of an enterprise due to environmental and economic impacts. Managers face challenges as a result of uncertainty in supply chain management. This research narrows the knowledge gap by applying a decision-making approach in a fuzzy environment to consider the uncertainties for green supplier selection in the construction industry. In this paper, a new approach using the best-worst method (BWM) and the superiority and inferiority ranking (SIR) is applied to assist managers in the appropriate supplier selection, in which the assessment is carried out with probabilistic dual hesitant fuzzy sets (PDHFS). The BWM with PDHFSs is used to generate the weights for criteria and experts. Also, a consistency index based on an input-based consistency ratio (CR) is introduced to solve the problem of poor consistency and make the computing process simpler. Moreover, the extended SIR method with the PDHFS is established using the criteria weights obtained by the BWM-PDHFS. A numerical study is employed to show the reliability and validity of the proposed approach, and comparisons are made to highlight its superiority.

A Novel SIR Approach to Closeness Coefficient-Based MAGDM Problems Using Pythagorean Fuzzy Aczel–Alsina Aggregation Operators for Investment Policy

In this study, a novel Pythagorean fuzzy aggregation operator is presented by combining the concepts of Aczel–Alsina ( A A ) T-norm and T-conorm operations for multiple attribute group decision-making (MAGDM) challenge for the superiority and inferiority ranking (SIR) approach. This approach has many advantages in solving real-life problems. In this study, the superiority and inferiority ranking method is illustrated and showed the effectiveness for decision makers by using multicriteria. The Aczel–Alsina aggregation operators on interval-valued IFSs, hesitant fuzzy sets (HFSs), Pythagorean fuzzy sets (PFSs), and T-spherical fuzzy sets (TSFSs) for multiple attribute decision-making (MADM) issues have been proposed in the literature. In addition, we propose a Pythagorean fuzzy Aczel–Alsina weighted average closeness coefficient ( PF − A A − WA − C C ) aggregation operator on the basis of the closeness coefficient for MAGDM challenges. To highlight the relevancy and authenticity of this approach and measure its validity, we conducted a comparative analysis with the method already in vogue.

Topological Structure of Single-Valued Neutrosophic Hesitant Fuzzy Sets and Data Analysis for Uncertain Supply Chains

From production to retail, the food supply chain (FSC) encompasses all stages of food production. Food is now transmitted across continents over long ranges. People depend on supply chains for basic necessities such as food, water, drinks, etc. Any disruption in these shipment pipelines poses a serious threat to human life. Supplier selection (SS) has been identified as a crucial component of FSC, which has been contemplated as a multi-criteria decision-making (MCDM) problem in many studies. The failure of some specific MCDM problems is due to failure in contemplating the relationships between alternatives under uncertain circumstances. To address such challenges, we present a contemporary method for designating green suppliers based on single-valued neutrosophic hesitant fuzzy (SVNHF) information, in which the input assessment is taken into account using single-valued neutrosophic hesitant fuzzy numbers (SVNHFNs). The foremost purpose of this analysis is to construct a topological structure on single-valued neutrosophic hesitant fuzzy sets (SVNHFSs) as well as to validate several key properties with examples. We discuss certain properties of SVNHF topology such as the SVNHF closure, SVNHF interior, SVNHF exterior, and SVNHF frontier. We also examine the conceptualization of the SVNHF dense set and SVNHF base in SVNHF topology using comprehensive examples. Eventually, to demonstrate and validate the superiority and inferiority ranking (SIR) method and choice value (CV) method in terms of their rationality and scientific basis, a real-world example of supplier selection in a food supply chain is provided. A comparative analysis is also performed to discuss the symmetry, validity and advantage of the proposed techniques.

Innovative Bipolar Fuzzy Sine Trigonometric Aggregation Operators and SIR Method for Medical Tourism Supply Chain

Bipolar fuzzy sets (BFSs) are effective tool for dealing with bipolarity and fuzziness. The sine trigonometric functions having two significant features, namely, periodicity and symmetry about the origin, are helping in decision analysis and information analysis. Taking the advantage of sine trigonometric functions and significance of BFSs, innovative sine trigonometric operational laws (STOLs) are proposed. New aggregation operators (AOs) are developed based on proposed operational laws to aggregate bipolar fuzzy information. Certain characteristics of these operators are also discussed, such as boundedness, monotonicity, and idempotency. Moreover, a modified superiority and inferiority ranking (SIR) method is proposed to cope with multicriteria group decision-making (MCGDM) with bipolar fuzzy (BF) information. To exhibit the relevance and feasibility of this methodology, a robust application of best medical tourism supply chain is presented. Finally, a comprehensive comparative and sensitivity analysis is evaluated to validate the efficiency of suggested methodology.

Cubic M-polar Fuzzy Hybrid Aggregation Operators with Dombi’s T-norm and T-conorm with Application

A cubic m-polar fuzzy set (CmPFS) is a new hybrid extension of cubic set (CS) and m-polar fuzzy set (mPFS). A CS comprises two parts; one part consists of a fuzzy interval (may sometimes be a fuzzy number) acting as membership grade (MG), and the second part consists of a fuzzy number acting as non-membership grade (NMG). An mPFS assigns m number of MGs against each alternative in the universe of discourse. A CmPFS deals with single as well as multi-polar information in the cubic environment. In this article, we explore some new aspects and consequences of the CmPFS. We define score and accuracy functions to find the priorities of alternatives/objects in multi-criteria decision-making (MCDM). For this objective, some new operations, like addition, scalar/usual multiplication, and power, are defined under Dombi’s t-norm and t-conorm. We develop several new aggregation operators (AOs) using cubic m-polar fuzzy Dombi’s t-norm and t-conorm. We present certain properties of suggested operators like monotonicity, commutativity, idempotency, and boundedness. Additionally, to discuss the application of these AOs, we present an advanced superiority and inferiority ranking (SIR) technique to deal with the problem of conversion from a linear economy to a circular economy. Moreover, a comparison analysis of proposed methodology with some other existing methods is also given.

High-end equipment development task decomposition and scheme selection method

Decomposition of tasks and selection of optimal schemes are key procedures in high-end equipment development processes. However, such procedures are highly innovative, technology intensive, interdisciplinary, and multi-party engineering projects, making the decomposition and scheme selection more difficult and complicated than that in the development of ordinary equipment. In this study, we consider three factors, namely, functional structure, task granularity, and task feasibility in task decomposition of high-end equipment development. Based on the principles of systems engineering, a method of task decomposition is proposed. As for decomposition scheme selection, a method based on the superiority and inferiority ranking (SIR) method mixed information and multiple attribute decision making is proposed by considering attributes of scheme feasibility, uncertainty risk and task integration complexity. To verify the proposed method, development of a military electric vehicle is used as an example to demonstrate the calculation process.

Q-ROF-SIR methods and their applications to multiple attribute decision making

q-rung orthopair fuzzy set (q-ROFS) is a useful tool to express uncertain information. With the parameter q increasing, q-ROFSs have broader space for describing uncertain information than intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs). This paper extends the superiority and inferiority ranking (SIR) methods to solve multiple attribute decision making (MADM) problems within the q-ROF environment, named q-ROF-SIR methods. In the q-ROF-SIR methods, the possibility degree (PD) for q-rung orthopair fuzzy numbers (q-ROFNs) is introduced to improve the preference intensity. Further, the q-ROF entropy weight (q-ROF-EW) method is constructed to determine the attribute weights suppose the weights of attribute are unknown. Finally, the effectiveness and applicability of the q-ROF-SIR methods are verified.

Evaluating Building Systems Energy Performance Superiority and Inferiority Ranking

Nowadays, the demand for sustainable buildings is increasing. The main purpose of buildings is to provide a comfortable living environment for their occupants, considering different aspects including thermal, visual, and acoustic comfort as well as Indoor Air Quality. Decreasing carbon footprint and energy consumption rates while increasing comfort level can help to achieve better living and working environment for building users. This research proposes a framework that aims at improving building system energy performance using building information modeling (BIM) during buildings’ design stage by evaluating different alternatives for installed building systems. According to experts’ opinions, evaluating buildings’ energy performance by analyzing the energy consumption rates alone without including economic and environmental factors is insufficient. Therefore, in this paper, building systems are evaluated using four main criteria; operating cost savings, total energy consumption per year, Lifecycle cost savings, and carbon emissions. A Multiple Criteria Decision-making (MCDM) technique is applied using Superiority and Inferiority Ranking (SIR) to study the behavior of different alternatives. Sensitivity analysis is performed to detect the criticality and effectiveness of the different defined criteria that influence environmental concerns and building system energy performance. A case study is presented to demonstrate the use of the proposed framework on an academic building by considering four criteria which are Operating Costs, Life Cycle Cost, Energy Consumption, and Carbon Emissions. Sensitivity analysis is performed on the weights of the criteria to determine how critical each criterion is and how they affect the ranking of the alternatives. A total of 36 combinations are simulated, considering changing the weights and procedure (SAW vs. TOPSIS). The rank that has the top repetitive percentage is considered to identify the most dominating alternative.

Parametric analysis of a green electrical discharge machining process using DEMATEL and SIR methods

To achieve safer machining environment, and minimize emission of harmful and toxic substances during electrical discharge machining (EDM) process along with improvement in its performance, this paper emphasizes on identifying the best parametric combination of a green EDM process using superiority and inferiority ranking (SIR) method. Decision making trial and evaluation laboratory method is also employed to visualize the interrelationships between the responses of the said process while splitting them into cause and effect groups. In this process, peak current, pulse duration, dielectric level and flushing pressure are the input parameters, while process time, relative tool wear ratio, process energy, concentration of aerosol and dielectric consumption are considered as the responses. The optimal parametric combination as derived employing the SIR method is validated with the help of developed regression equations for each of the responses, which show that the adopted approach outperforms the other popular optimization techniques in obtaining the best mix of the green EDM process parameters for having improved machining performance and less hazardous effects on the environment.

Generalized Pythagorean Fuzzy Maclaurin Symmetric Means and Its Application to Multiple Attribute SIR Group Decision Model

Pythagorean fuzzy sets, originally proposed by Yager (IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), pp 57–61, 2013), which are convenient to describe vagueness and impression in real-world problems, can be regarded as a novel extension of intuitionistic fuzzy sets. Generalized Maclaurin symmetric mean comes as an extension of classical Maclaurin symmetric mean. Their prominent feature is that they can both capture overall interrelationships and also focus on individual importance among the arguments. However, the generalized Maclaurin symmetric mean can only be considered for aggregating numeric information. In this paper, we investigate the generalized Maclaurin symmetric mean and further extend to Pythagorean fuzzy environment. We develop the Pythagorean fuzzy generalized Maclaurin symmetric mean (PFGMSM), the Pythagorean fuzzy weighted generalized Maclaurin symmetric mean (PFWGMSM), and discuss a variety of their desirable properties. To better understand the proposed parameterization operators, we provide an innovative way to extend the classical superiority and inferiority ranking (SIR) group decision model to Pythagorean fuzzy environment. Then, an approach formed with the aid of PFWGMSM and SIR to multiple attribute group decision-making with Pythagorean fuzzy information is developed. Finally, an illustrative example concerning supply chain financial risk decision-making is provided to verify the proposed method and demonstrate its effectiveness.

Modified intuitionistic fuzzy SIR approach with an application to supplier selection

In this paper, we propose a modified fuzzy intuitionistic multiple attribute decision (MAD) method retaining basic framework of the superiority and inferiority ranking (SIR) method. We utilize relative comparisons based on the advantage and disadvantage scores of intuitionistic fuzzy (IF) numbers s o that the modified SIR method is better adapted to treat real-world MAD problems. The relative comparisons of the IF values use all the three parameters, namely membership degree (“more the better”), non-membership degree (“less the better”), and hesitancy degree (“less the better”) leading to trade-off values of the parameters. The modified IF-SIR flows, namely IF-superiority and IF-inferiority flows are calculated using the advantage and disadvantage score matrices, respectively, derived from the relative comparisons. These score matrices are based on positive contributions of all three parameters of the IF values, wherever applicable. The ranking of the alternatives is done using a consolidated score function derived from the modified IF-SIR flows. The methodology is validated through an application of a real-case of supplier selection problem.

Benchmarking of product recovery alternatives in reverse logistics

Purpose – Selection of best product recovery alternative in reverse logistics (RL) has gained great attention in supply chain community. The purpose of this paper is to provide a robust group decision-making tool to select the best product recovery alternative. Design/methodology/approach – In this paper, fuzzy values, assigned to various criteria and alternatives by a number of decision makers, are converted into crisp values and then aggregated scores are evaluated. After obtaining experts’ scores, objective and subjective weights of the criteria have been calculated using variance method and analytic hierarchy process, respectively. Then integrated weights of criteria are evaluated using different proportions of the two weights. The superiority and inferiority ranking (SIR) method is then employed to achieve the final ranking of alternatives. An example is presented to demonstrate the methodology. Findings – The proposed methodology provides decision makers a systematic, flexible and realistic approach to effectively rank the product recovery alternatives in RL. The alternatives can easily be benchmarked and best recovery strategy can be obtained. The sensitivity analysis carried out by changing different proportion of objective and subjective weights reveals that best ranking alternative never changes and proves the robustness of the methodology. The present benchmarking framework can also be used by decision makers to simplify any problem which encounters multi-attribute decision making and multiple decision makers. Research limitations/implications – The proposed methodology should be tested in different situations having varied operational and environmental conditions dealing with different products. A real case study from an industrial set up can help to assess the behavior of the proposed methodology. The presented methodology however can deal with such multi-disciplinary and multi-criteria issues in a simple and structured manner and ease the managers to select the best alternative. Originality/value – A novel approach for decision making taking into account both objective and subjective weights for criteria has been proposed to rank the best recovery alternatives in RL. The proposed methodology uses SIR method to prioritize the alternatives. As RL alternative selection is an important issue and involves both technical and managerial criteria as well as multiple decision makers, the proposed robust methodology can provide guidelines for the practicing managers.

MEASURING SENSITIVITY OF PROCUREMENT DECISIONS USING SUPERIORITY AND INFERIORITY RANKING

Wrong decisions or inappropriate selection of equipment may lead to increase in cost and reduction in efficiency and effectiveness. Selecting right equipment has always been a key factor in the success of the process it is used for. In this study, superiority and inferiority ranking (SIR) methodis utilized for evaluation of most suitable offer for procurement of equipment installed inside a facility, whereas, analytical hierarchy process (AHP) is used to calculate the weights of factors that influence procurement decision. To achieve this target, a methodological framework of a series of interviews are conducted, then two questionnaire surveys are developed for identifying the important factors affecting the selection process of equipment and determining their relative importance. A solution of the problem is then designed in a model using AHP and SIR methods in addition to using the simple additive weighting (SAW) and technique for order preference by similarity to the ideal solution (TOPSIS) procedures to generate the superiority and inferiority flows. The model is generic and flexible and is used for the application of the multiple criteria decision making (MCDM) methods in the procurement process. The model also offers an efficient and convenient tool that aids its users to act in an orderly and methodical thinking, and guides them in making logical and robust decisions. A case study is presented to demonstrate the use of the developed model and sensitivity analysis is carried out to measure the robustness of the model in different scenarios.

A NEW RULE-BASED SIR APPROACH TO SUPPLIER SELECTION UNDER INTUITIONISTIC FUZZY ENVIRONMENTS

Multiple Criteria Decision Making (MCDM) aims at giving people a knowledge recommendation concerning a set of objects evaluated from multiple preference-ordered attributes. The Superiority and Inferiority Ranking (SIR) is a generation of the well-known outranking approach-PROMETHEE, which is an efficient approach for MCDM. As the traditional MCDM approach, however, it faces the obstacle in handling uncertainties of real world. We are concerned about the issue on how to extend the traditional MCDM approach for applications in uncertain environments. This paper proposes a new Intuitionistic Fuzzy SIR (IF-SIR for short) approach and focuses on its application to supplier selection which is the important activity in supply chain management. Toward practical applications, two factors are considered here: (1) multiple decision makers and (2) decision information in the form of linguistic terms. We firstly identify these terms via Intuitionistic Fuzzy Set (IFS) which is proven to be a powerful mathematical tool in modeling uncertain information. Then, we provide the IF-SIR approach for group aggregation and decision analysis. Hereinto, a rule-based method is developed for ranking and selection of suppliers. Finally, an illustrative example is used for illustration of the proposed approach.

A superiority and inferiority ranking model for contractor selection

PurposeContractor selection is carried out in order to choose a competent and capable contractor to do the work. To help in this selection, baselines are established to ensure that the contractors have the required skills, resources, and abilities to execute the project. Contractor selection is a multiple criteria decision making wherein several criteria are required to be evaluated simultaneously. This paper aims to propose a decision‐making model.Design/methodology/approachThe proposed model utilizes superiority and inferiority ranking (SIR) method and it provides six preference structures in order to compare the performance of alternatives' criteria. As such, it can represent discrete or continuous criteria. The preference structures utilize indifference and preference thresholds to capture the characteristics of functions that represent the specified criteria. The model provides two aggregation procedures (simple additive weighting and technique for order preference by similarity to the ideal solution) to generate superiority and inferiority flows.FindingsThe proposed model is generic and can be used as a tool to evaluate alternatives in several applications such as value engineering, optimum organization structure, and constructability analysis. It enables its users to define the criteria that are deemed important for evaluation.Originality/valueThe proposed multiple criteria decision making (SIR method) is novel to construction. This ranking method can be utilized as a successful tool in contractor selection problem.

Locating large‐scale harbour‐front project developments using SIR method with grey aggregation approach

PurposeThe purpose of this paper is to propose a variant of superiority and inferiority ranking (SIR) method called SIR‐Grey for determining the location of large‐scale harbour‐front project development.Design/methodology/approachThe study is illustrated with an application example obtained from the Environmental Protection Department of the Hong Kong Special Administrative Region to demonstrate the concept and application procedure of SIR‐Grey. The strengths and weaknesses of SIR‐Grey are highlighted when compared with the traditional weighted average approach.FindingsThe strengths and weaknesses of SIR‐Grey are highlighted when compared with the traditional weighted average approach. Among the strengths, the global comparison scores of SIR‐Grey can give a clearer and easier comprehensible algorithm. Further, the global comparison generated from superiority flows (S‐flows) [S‐flow: A is preferred to A′(AP>A′) or A is indifferent to A′(AI>A′)] and inferiority flows (I‐flows) [I‐flow: A is preferred to A′(AP<A′) or A is indifferent to A′(AI<A′)] can be used to select a solution matching the nature of the problem; e.g. a conservative approach can adopt the ranking from I‐flow because the selected option will have the criteria farthest from the virtual worst site while the ranking from S‐flow can be adopted for an aggressive approach because the final decision will have the criteria closest to the virtual perfect site. Regarding the weaknesses, the major one is the requirement of a full appreciation of the nature of criteria in setting the thresholds and preference structure, which may complicate the application of the model.Originality/valueThis study proposes a variant of SIR method called SIR‐Grey for determining the location of large‐scale harbour‐front project development. This approach can overcome the problem encountered in using other methods which could lead to variation in the final ranking and hence an inconsistent result.

Decision Model in Selecting Engineering Materials for Pump Manufacture

This paper presents a decision model that applies the Superiority and Inferiority Ranking (SIR) method in identifying the best material option. It has the strength to handle imprecise information, in different units of analysis, for the evaluation process that ranks the material options in preference order. It is demonstrated with an example of selecting materials for a pump.

Selection of Concrete Pump Using the Superiority and Inferiority Ranking Method

The Superiority and Inferiority Ranking (SIR) method, a multicriteria decision-making technique, is proposed for facilitating the process of construction plant selection. The SIR method draws together the strengths of most multicriteria decision-making models in handling nonquantifiable, cardinal, and/or ordinal data and allows imprecise information by introducing concepts like indifference and preference thresholds for each criterion. In addition, the technique is capable of dealing with data in different units. The application of the SIR method is illustrated with a case study in the evaluation of 10 alternative concrete pumps based upon nine decision criteria. From the case study, the benefits and strengths of the SIR method in the selection process are unveiled.