Pythagorean fuzzy CODAS and its application to supplier selection in a manufacturing firm

A novel extension of Pythagorean fuzzy MULTIMOORA approach for new product development

This study explores how to enhance the decision-making processes in the phases of idea generation or alternative selection during the new product development (NPD) process. NPD is acknowledged as the central function of businesses in an increasingly competitive marketplace. In the current era, the highly uncertain and rapidly changing market environment makes the NPD extremely vague and complex. To be able to find a suitable solution to this complexity, the proposed research aims to categorize the decision points in the process of NPD regarding software development and identify the fuzziness elements affecting the process. The goal of a decision-making process is to prioritize several alternatives with respect to some required objectives and to select the best among them. Multi-Criteria Decision-Making (MCDM) may support reaching a consensus judgment with the collective assessment of Decision Makers (DMs). We introduce a novel evaluation approach for this problem. The proposed approach employs a MULTIMOORA (Multi-objective Optimization by Ratio Analysis plus the Full Multiplicative Form) MCDM technique under Pythagorean Fuzzy Sets (PFSs) objective world environment to cope with an ambiguous environment using Group Decision Making (GDM) setting to shape the decisions. The PFSs have demonstrated their advantages in dealing with vagueness and uncertainty over crisp, fuzzy, or intuitionistic fuzzy sets. Therefore, PFSs can represent the DMs' judgments and preferences in a better structure, ensuring enhanced decision-making in a group consensus. A case study on gaming software and app development is presented to validate the functionality of the proposed method. The results are compared and assessed with the help of a sensitivity analysis. This research makes a real contribution to the literature by proposing a novel evaluation method to rate and select NPD (gaming software and apps) to deal with the inexactness and vagueness associated with the criteria and alternatives.

Innovative CODAS Algorithm for q-Rung Orthopair Fuzzy Information and Cancer Risk Assessment

Due to insufficient healthcare facilities for the fight against cancer, a large percentage of individuals die. Utilizing computational tools inside the health and medical system helps to minimize fatalities. Timely cancer detection enhances the likelihood of effective therapy. Cancer risk assessment is important for legal and regulatory reasons, for cancer prevention, and to avoid the risks. The approach for assessing cancer risk based on the q-rung orthopair fuzzy set (q-ROFS) is described. The technique is predicated on a multifactor evaluation of the likelihood of a cancerous. q-ROFS is a robust approach for modeling uncertainties in multicriteria decision making (MCDM). The combinative distance-based assessment (CODAS) technique integrates two separate approaches, namely the “simple additive weighting” (SAW) method and the “weighted product method (WPM)”. In this study, the CODAS approach is extended to the q-rung orthopair fuzzy framework with application to cancer risk assessment. Additionally, the symmetry of the optimal decision in cancer risk assessment is carried out by a comparison analysis of the suggested model with some existing models.

Evaluation of the route selection in international freight transportation by using the CODAS technique based on interval-valued Atanassov intuitionistic sets

The selection of a proper international freight transport route is one of the crucial tasks for decision-makers since it can affect costs, efficiency, and transportation performance. Besides, the selection of suitable and appropriate freight routes can also reduce external costs of transportation such as emissions, noise, traffic congestions, accidents, and so on. Route selection in international transportation is a complicated decision-making problem as many conflicting factors and criteria affect the assessment process. It has been observed that there is no mathematical model and methodological frame used for solving these selection problems, and decision-makers make decisions on this issue based on their own experiences and verbal judgments in the research process. Therefore, a methodological frame is required to make rational, realistic, and optimal decisions on route selection. From this perspective, the current paper proposes using the IVAIF CODAS, an extended version of the traditional CODAS techniques, and using the Atanassov interval-valued intuitionistic fuzzy sets (IVAIFS) for processing better the existing uncertainties. The proposed model is applied to solve the route selection, a real-life decision-making problem encountered in international transportation between EU countries and Turkey. According to the results of the analysis, option A6 (i.e., Route-6 (Bursa-Istanbul-Pendik-Trieste (Ro-Ro)-Austria-Frankfurt/Germany) has been determined as the best alternative. These obtained results have been approved by a comprehensive sensitivity analysis performed by using different MCDM techniques based on interval-valued intuitionistic fuzzy sets. Hence, it can be accepted that the proposed model is an applicable, robust, and powerful mathematical tool; also, it can provide very reliable, accurate, and reasonable results. As a result, the proposed model can provide a more flexible and effective decision-making environment as well as it can provide valuable advantages to the logistics and transport companies for carrying out practical, productive, and lower cost logistics operations.

Sustainability prioritization of technologies for cleaning up soils polluted with oil and petroleum products: A decision support system under complex spherical fuzzy environment

The transition towards a sustainable land management (SLM) needs for utilizing appropriate technologies for controlling soil pollution, and ensures the development of environmental, economic, technical, and social dimensions in region. Using these technologies for cleaning up soils polluted with oil and petroleum products in Behbahan city (in the southwest of Iran) is considered. The soil pollution control technologies (SPCTs) can assist local authorities, governments, investors, and developers to reduce climate change, mitigate soil, water, and air pollution and construct the sustainable communities. With the aim of balance between the issues in the context of sustainability policy, criteria (principles) including environmental, economic, technical, and social aspects are considered. The research takes into consideration the applicability of a novel multi-criteria decision-making approach namely complex spherical fuzzy set-based CODAS (Combinative distance-based assessment). The approach can be used to choose the suitable technology in a sustainable mode, considering the principles related to sustainability pillars. It comprises various technologies for soil pollution control and prioritizes technologies from the most to least as: Biorestoration technology, Excavation technology, Soil venting technology, Soil flushing, and Hydraulic barriers technology.

A novel risk assessment model for work-related musculoskeletal disorders in tea harvesting workers

Work-related musculoskeletal disorders (WMSDs) are the most common occupational health problems in agriculture workers due to repetitive and excessive force movement activities involved in their job processes. The Fine-Kinney method has been commonly used as a quantitative evaluation method in risk assessment studies. Classically, the risk value via Fine–Kinney is calculated by the mathematical multiplication irrespective of the degree of importance of each risk parameter (probability (P), exposure (E), and consequence (C)). Hence, a novel risk management model was proposed based on integrating Fine-Kinney and spherical fuzzy AHP-TOPSIS. First, each risk parameter is weighted using the spherical fuzzy AHP (SF-AHP). Second, the spherical fuzzy TOPSIS (SF-TOPSIS) method is used for hazard ranking. The proposed model is applied to evaluate risks in tea harvesting workers for work-related musculoskeletal disorders. Subsequently, a sensitivity analysis is carried out to test the proposed model. Finally, we compare the proposed model’s applicability and effectiveness with the spherical fuzzy COmbinative Distance-based ASsessment (SF-CODAS) method based on Fine-Kinney. The ranking similarity between the proposed Fine-Kinney-based SF-TOPSIS and SF-CODAS methods is checked by applying Spearman’s rank correlation coefficient, in which 92% of rankings are matched.

A modified range directional measure for assessing the sustainability of suppliers by DEA/UTASTAR

Sustainability in the supply chain means considering environmental, social, and economic practices. Conventional data envelopment analysis (DEA) models deal with desirable, discretionary, and nonnegative data. However, there might be undesirable outputs, nondiscretionary factors, and negative data. On the other hand, some criteria can be considered as outputs and inputs. These factors are named as the dual-role criteria. The objective of this paper is to develop a non-radial DEA model for dealing with negative data in the presence of undesirable, non-discretionary, and dual-role factors in weight restrictions context. The ordinal regression method, UTASTAR, is performed to define priorities in terms of criteria. The capabilities of the proposed method are compared with other methods. A case study is presented in which the best sustainable suppliers of SAPCO are selected. To check the importance of dual-role variables, two extra cases are considered.

A novel interval-valued spherical fuzzy CODAS: Reopening readiness evaluation of academic units in the era of COVID-19

Combinative distance-based assessment (CODAS) is a multi-criteria decision-making (MCDM) method that is based on the Euclidean and Hamming distances of alternatives from the average scores of attributes. Spherical fuzzy sets, as the recent extensions of ordinary fuzzy sets, were developed based on Pythagorean and neutrosophic sets and enable decision-makers to express their membership, non-membership, and hesitancy degrees independently and in a larger domain than most other fuzzy extensions. This paper proposes a new interval-valued spherical fuzzy CODAS method and provides extra space for catching the vagueness in the nature of the problem. The feasibility and practicality of the proposed model are illustrated with an application for evaluating the reopening readiness of academic units for campus education in the era of COVID-19. Three decision-makers from a higher education institution evaluate four academic units with respect to five strategic criteria and prioritize them according to their readiness levels for the campus type of education. Sensitivity and comparative analyses, theoretical and practical contributions, limitations, and future research avenues are also presented in the study.

Research on service quality evaluation of sports clubs with Pythagorean fuzzy information

In this paper, considering the traditional geometric operation laws and Pythagorean fuzzy information, a varies of new distance measures of Pythagorean fuzzy set are proposed, such as generalized Pythagorean fuzzy ordered geometric distance (GPFOWGD) measures and generalized Pythagorean fuzzy hybrid weighted geometric distance (GPFHWGD) measures. Besides, some special issues including Hamming distance, Euclidean distance and Hausdorff distance of these raised geometric distance measures are investigated. To testify the valid of these new presented distance measures, a decision-making model is built and illustrated by a mathematical calculation example to evaluate the service quality of sports clubs using Pythagorean fuzzy information. The results display that the approach is uncomplicated, valid and simple to compute. The example illustrates that the method presented in the paper can be used to deal with problems of uncertainty such as dynamic multiple attribute decision making.

Investigating the sustainable development goals derived due to adoption of circular economy practices

Recently, the novel concept of the circular supply chain (CSC) has gained substantial attention amongst researchers across the globe. It is due to the issues of unsustainability in supply chain operation of manufacturing industry. CSC amalgamates the circular economy (CE) into the supply chain of manufacturing organizations. Business organizations can achieve several sustainable development goals by adopting CSC as an innovative strategy. However, limited attention has been given to its implementation in emerging economies. Thus, this research aim is to identify and analyze the essential CE practices that help to accomplish the several sustainable development goals (SDGs) of CSC management. This research presents an integrated framework of Pythagorean fuzzy analytic hierarchy process (PF-AHP) and Pythagorean fuzzy combinative distance-based assessment (PF-CODAS) techniques. PF-AHP is employed to determine the relative importance of CE practices, whereas PF-CODAS method ranks the SDGs derived due to the adoption of CE practices. The effectiveness of the proposed framework is validated with the help of an Indian manufacturing organization. The finding of this research reveals that practices based on 'government', 'management', and 'economy' initiatives play a significant role and contribute 50 % of its influence on the effective CSC adoption, whereas, 'mitigate waste and enhance environmental sustainability', is identified as the most critical SDG realized due to adoption of CE practices. Sensitivity analysis is conducted to check the robustness of proposed methods. This research provides the systematic, accurate, and valuable decision support tools to practitioners to execute the CE practices efficiently for achieving the various sustainability goals.

Models for multiple attribute decision making with dual hesitant pythagorean fuzzy information

In this paper, we investigate the multiple attribute decision making (MADM) problem based on the Muirhead Mean (MM) operators with dual Pythagorean hesitant fuzzy information. Then, motivated by the ideal of MM operators, we have developed some MM operators for aggregating dual hesitant Pythagorean fuzzy information. The prominent characteristic of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the dual hesitant Pythagorean fuzzy multiple attribute decision making problems. Finally, a practical example for supplier selection in supply chain management is given to verify the developed approach and to demonstrate its practicality and effectiveness.

A Proposed Framework for Developing FMEA Method Using Pythagorean Fuzzy CODAS

The purpose of this research article is to develop a hybridization between the Failure Mode and Effect Analysis (FMEA) method and the Combinative Distance-Based Assessment (CODAS) method under Pythagorean Fuzzy environment. The traditional FMEA procedure is based on the multiplication between the parameters of severity, occurrence, and detectability where everyone has equal relative importance; therefore, different combinations of these parameters can generate the same result creating uncertainty in the analysis. In this mode, the hybridization proposed in this research deal with relative importance of each parameter; in the fact to have a more suitable combination which consider the level of knowledge of the experts in the assessment. Finally, a numerical case was carried out concerning the public transportation service to validate our proposal; the results show that 31 failure modes and potential risks can be evaluated using user perceptions, a dominant with high level of knowledge about the public transportation service and an apprentice or common user, as team of experts and exploiting the subjectivity of the information in a mathematical model. Also, we compare the results with a variation of the proposed model with the multi-criteria method multi-objective optimization method by relationship analysis (MOORA); it was observed that the convergence of the failure modes depends on the nature of the mathematical model even under the same conditions at the start.

ERP selection using picture fuzzy CODAS method

The purpose of this study is to develop an extension of CODAS method using picture fuzzy sets. In this respect, a new methodology is introduced to figure out how picture fuzzy numbers can be applied to CODAS method. COmbinative Distance-based Assessment (CODAS) is a new MCDM method proposed by Ghorabaee et al. Picture fuzzy sets (PFSs) are a new extension of ordinary fuzzy sets for representing human’s judgments having possibility more than two answers such as yes, no, refusal and neutral. Compared with other studies, the proposed method integrates multi-criteria decision analysis with picture fuzzy uncertainty based on Euclidean and Taxicab distances and negative ideal solution. ERP system selection problem is handled as the application area of the developed method, picture fuzzy CODAS. Results indicate that the new proposed method finds meaningful rankings through picture fuzzy sets. Comparative analyzes show that the presented method gives successful and robust results for the solutions of MCDM problems under fuzziness.

A novel MADM approach to the ground-handling agent selection problem in B2B markets

Purpose

Ground-handling services are important for effective aircraft operations in the air transportation system. Airlines often outsource these services to ground-handling agents through business-to-business (B2B) marketing decisions. Therefore, this paper aims to address the problem of ground-handling agent selection in the airline industry.

Design/methodology/approach

A real-world case study was carried out to demonstrate the applicability of the integrated best worst method and fuzzy multi-attribute ideal real comparative analysis (F-MAIRCA) approach to solve ground-handling agent selection problems under uncertainty and imprecision. A two-stage sensitivity analysis was also conducted to ensure the credibility and validity of the application.

Findings

In the weighting stage, “Quality” was determined as the most important criterion in terms of supplier performance. With regard to the performance of the ground-handling agents, A2 was found as the optimal supplier in terms of both credibility and validity.

Practical implications

This study enumerated several criteria that ground-handling agents must meet in order to effectively supply services for the airlines. In addition, this study provides a novel framework from which managers can gain additional benefits from their businesses. Finally, it is concluded that this approach will help airline managers quantitatively in choosing the most appropriate ground-handling agent.

Originality/value

The contributions of this study to the existing literature are twofold. First, we propose a novel multiple attribute decision-making approach to address the problem of supplier selection for airlines under uncertainty and imprecision. Second, the selection of ground-handling agents from the B2B perspective is addressed for the first time in literature.

QUALIFLEX method for evaluating human factors in construction project management with Pythagorean 2-tuple linguistic information

The overall quality evaluation of operation personnel helps contain site safety accidents, in this study, we proposed a combination of the Pythagorean 2-tuple linguistic fuzzy set and qualitative flexible multiple criteria (QUALIFLEX) method to evaluate comprehensive quality of operation personnel in engineering projects, Pythagorean 2-tuple linguistic fuzzy numbers to express decision makers’ evaluation on each scheme with original QUALIFLEX approach to decision making process. In the end, an example of the performance evaluation of operation personnel in the engineering project is provided to test the applicability and practicability of the method, comparison analysis for further elaboration.

Assessment Urban Transport Service and Pythagorean Fuzzy Sets CODAS Method: A Case of Study of Ciudad Juárez

The purpose of this research article is to provide a comprehensive method of evaluation for public transportation. In this sense, this study considers transport lines that offer in Ciudad Juárez, Chihuahua. Hence, this study presents a description of the public transport system as part of the literature review. Likewise, the document describes an appropriate model based on the more outstanding publications about urban mobility and public transportation for passengers. Nevertheless, it is based on the Pythagorean Fuzzy combinative distance-based assessment (CODAS) to analyze and evaluate the alternatives through criteria that define general performance. Thus, the integration of these methods provides an adequate methodology for decision-making concerning urban planning and mobility to detect and improve the performance of criteria not considered within sustainable urban mobility plans. Results show how the applied approach can work as a powerful tool for the appraisal of the transport service. Finally, the results give relevant information to the local authority of the transport management of Ciudad Juarez to do improvements focused on the user.

Evaluation of smart health technologies with hesitant fuzzy linguistic MCDM methods

Smart health applications are raising a growing interest around the world thanks to its potential to act proactively and solve health related problems with smart technologies. Smart health technologies can provide effective healthcare services such as personalization of treatments through big data, robotics in cure and care, artificial intelligence support to doctors, etc. The mixed structure of the evaluation of smart health technologies involves various contradictory criteria. However, when information is of uncertain nature, it is difficult to decide on how to treat. A hesitant fuzzy linguistic term set (HFLTS) approach is applied to overcome such uncertainties related to this multi-criteria decision-making (MCDM) problem. This approach can be used to facilitate experts’ decision-making processes in complex and uncertain situations. In this study, an integrated hesitant fuzzy linguistic (HFL) MCDM approach is proposed to evaluate smart health technologies. The criteria are weighted with HFL Analytic Hierarchy Process (AHP), and then, smart health technologies are evaluated with the HFL Combinative Distance-based Assessment (CODAS) method. A comparative analysis with HFL COPRAS and HFL TOPSIS is applied. Lastly, the potential of this approach is presented through a case study.

Dry Port Terminal Location Selection by Applying the Hybrid Grey MCDM Model

Globalization and decentralization of production generate the intensive growth of goods and transport flows, mostly performed by the maritime transport. Ports, as the main nodes in the global logistics networks, are becoming congested, space for their expansion limited, and traffic in their hinterland congested. As a solution to these and many other hinterland-transport-related problems stands out the development of dry port (DP) terminals. Selection of their location is one of the most important strategic decisions on which depends their competitiveness in the market and the functionality of the logistics network. Accordingly, the evaluation and selection of locations for the development of the DP in accordance with the requirements of various stakeholders is performed in this paper, as a prerequisite for the establishment of an ecological, economic, and socially sustainable logistics network in the observed area. To solve this problem, a new hybrid model of multi-criteria decision-making (MCDM) that combines Delphi, AHP (Analytical Hierarchy Process), and CODAS (Combinative Distance-based Assessment) methods in a grey environment is developed. The main contributions of this paper are the defined model, the problem-solving approach based on finding a compromise solution, simultaneous consideration of the environmental, economic, and social sustainability of the DP concept and its implementation in the regional international markets. The applicability of the approach and the defined MCDM model is demonstrated by solving a real-life case study of ranking the potential DP locations in the Western Balkans region. Based on the obtained results, it is concluded that in the current market conditions, it would be most realistic to open three DP terminals, in Zagreb, Ljubljana, and Belgrade.

An integrated decision-making model for supplier evaluation in public healthcare system: the case study of a Spanish hospital

Purpose

In this study, an integrated decision-making model consisting of decision-making trial and evaluation laboratory (DEMATEL), best worst method (BWM) and a modified version of evaluation based on distance from average solution (EDAS) methods is proposed for supplier selection problem in a public procurement system considering sustainable development goals.

Design/methodology/approach

DEMATEL and BWM methods are used to determine weights of the criteria that are defined for the supplier selection problem. Weight aggregation method is applied to combine the weights obtained from these two methods. A modified version of EDAS method is then used in order to rank the alternative suppliers.

Findings

The proposed decision-making model is investigated for a supplier selection problem for a hospital in Spain. The validity of the results is checked using comparison with other decision-making methods and several performance analysis tests.

Practical implications

The proposed multi-criteria decision-making (MCDM) model contributes to the healthcare supply chain management (SCM) and aims to lead the policy makers in selecting the best supplier.

Originality/value

There is no such study that combines DEMATEL and BWM together for weight generation. The application of the modified EDAS method is also new. In real time situations, the decision experts may confront to the difficulty of using BWM while identifying the best and the worst criteria choices. The idea of using DEMATEL is to aid the experts to make them enable in distinguishing between the best/worst criteria and handle BWM easily.

How to use fuzzy screening system and data envelopment analysis for clustering sustainable suppliers? A case study in Iran

Purpose

The aim of this paper is to classify suppliers into some clusters based on sustainability factors. However, there might be some unqualified suppliers and we should identify and remove those suppliers before clustering.

Design/methodology/approach

First, using fuzzy screening system, the authors identify and remove the unqualified suppliers. Then, the authors run their proposed clustering method. This paper proposes a data envelopment analysis (DEA) algorithm to cluster suppliers.

Findings

This paper presents a two-aspect DEA-based algorithm for clustering suppliers into clusters. The first aspect applied DEA to consider efficient frontiers and the second aspect applied DEA to consider inefficient frontiers. The authors examine their proposed clustering approach by a numerical example. The results confirmed that their method can cluster DMUs into clusters.

Originality/value

The main contributions of this paper are as follows: This paper develops a new clustering algorithm based on DEA models. This paper presents a new DEA model in inefficiency aspect. For the first time, the authors’ proposed algorithm uses fuzzy screening system and DEA to select suppliers. Our proposed method clusters suppliers of MPASR based on sustainability factors.

Call center performance measurement using intuitionistic fuzzy sets

Purpose

The companies are struggling to collect invoices due to the decrease in the economic growth. This global trend does not only affect undeveloped countries, but it also has a strong impact on the developed countries. Improving the debt collection process become a significant element to maintain financial stability. The institutions that are specialized on collecting payments, debt collection agencies and their call centers, with their expertise in the field can improve the payment process. Yet, managing evaluating the performance of debt collection agencies is a very hard process that involves uncertainty and imprecision. Performance measurement (PM) is a combination of numerically expressed characteristics which give insight about the success or degree of accomplishment of an activity. PM can be handled in various levels such as individual, team, department or company. The aim of this study is to present a systematic and objective PM method for call centers.

Design/methodology/approach

In this study, first an exploratory approach is used to understand the call center measurement problem. Several meetings are done with the representatives of both call center firms and the parent firms that outsource debt collection process. Simultaneously, a broad literature review is conducted. An iterative approach is selected to reach deeper knowledge on the process. New meetings are planned and scope of the literature review has changed based on this iterative approach. After these steps, the problem has been considered as the multi-criteria decision-making problem since more than one criteria should be considered for evaluating the performances of call centers. The result of the literature review and the meetings with experts show that defining the weights for the criteria is very crucial for evaluating the performances accurately. Collecting human judgment for defining the weights of call center criteria necessitates dealing with vagueness and uncertainty. The intuitionistic fuzzy sets excellent tools for representing uncertainty. Interval valued intuitionistic fuzzy sets can easily represent the human judgments. Thus, in this study, an intuitionistic fuzzy multi-criteria decision making approach is used to design the proposed methodology. Incomplete interval-valued intuitionistic preference relations are used to determine the weights of the indicators aggregating linguistic evaluations of the decision makers.

Findings

The proposed approach provides an objective calculation of performance measurement. In order to provide objectivity, indicator performance functions are proposed for the first time in this study. Nine different functions and related parameters are defined to objectively measure indicator performances.

Originality/value

The paper proposes an objective and easy-to-modify approach for call-center PM, which can be used by call center managers. It presents a new fuzzy multi-criteria decision-making (MCDM) method for call center performance evaluation, which can consider the multi-experts' judgments under vagueness and impreciseness, which may be conflicting and incomplete interval-valued intuitionistic fuzzy preference relations. Also nine new functions are defined for indicator performance.

Pythagorean 2-Tuple Linguistic Taxonomy Method for Supplier Selection in Medical Instrument Industries

Supplier selection in medical instrument industries is a classical multiple attribute group decision making (MAGDM) problem. The Pythagorean 2-tuple linguistic sets (P2TLSs) can reflect uncertain or fuzzy information well and solve the supplier selection in medical instrument industries, and the original Taxonomy is very appropriate for comparing different alternatives with respect to their advantages from studied attributes. In this study, we present an algorithm that combines Pythagorean 2-tuple linguistic numbers (P2TLNs) with the Taxonomy method, where P2TLNs are applied to express the evaluation of decision makers on alternatives. Relying on the Pythagorean 2-tuple linguistic weighted average (P2TLWA) operator or Pythagorean 2-tuple linguistic weighted geometric (P2TLWG) operator to fuse P2TLNs, the new general framework is established for Pythagorean 2-tuple linguistic multiple attribute group decision making (MAGDM) under the classical Taxonomy method. Ultimately, an application case for supplier selection in medical instrument industries is designed to test the novel method’s applicability and practicality and a comparative analysis with three other methods is used to elaborate further.

Pythagorean 2-Tuple Linguistic VIKOR Method for Evaluating Human Factors in Construction Project Management

Since the reform and opening up, Chinese economic and social development has undergone great changes, and the people’s living standards have improved markedly. For the national economy, the engineering construction is not only a carrier for specific economic tasks, but also a driving force for rapid and sustained economic development. With the continuous expansion of the scale of construction projects, safety management problems of construction projects are constantly exposed. How to effectively avoid accidents has become an important issue to be solved urgently in the construction industry. This paper mainly evaluates human factors in the process of construction project management, such as workers’ proficiency, workers’ safety awareness, technical workers’ quality, and workers’ emergency capacity, with the purpose of helping China’s construction projects proceed smoothly. In this research, we provide a multiple attribute group decision-making (MAGDM) technique based on Pythagorean 2-tuple linguistic numbers (P2TLNs) and the VIseKriterijumska Optimizacija I KOmpromisno Resenje (VIKOR) method for evaluating the human factors of construction projects. P2TLNs are used to represent the performance assessments of decision makers. Relying on a P2TLWA operator, P2TLWG operator, and the essential VIKOR method, a general framework is established. An application is presented to test the validity of the new method, and a comparative analysis with two algorithms and the P2TL-TODIM method is illustrated with detail.

Application of the Fuzzy CODAS Method Based on Fuzzy Envelopes for Hesitant Fuzzy Linguistic Term Sets: A Case Study on a Personnel Selection Problem

Fuzzy multi-criteria decision-making (MCDM) methods are useful and reliable for multi-criteria selection problems under uncertain and imprecise situations. In these methods, if decision-makers hesitate among several linguistic terms, hesitant fuzzy linguistic term sets (HFLTSs), represented by a set of successive linguistic terms instead of single linguistic terms, may be more appropriate to make evaluations. The notion of a fuzzy envelope for the HFLTSs is a beneficial tool that can be directly applied to fuzzy MCDM methods to elicit comparative linguistic expressions (CLEs). The aim of this study is to present a methodology that combines the fuzzy CODAS (COmbinative Distance-based Assessment) method with the fuzzy envelope of HFLTs based on CLEs to solve a personnel selection problem. In order to examine the feasibility of the presented methodology, a case study on blue-collar personnel selection in a manufacturing firm is conducted. A sensitivity analysis is performed to demonstrate the stability and validity of the ranking results. Furthermore, the ranking results of the presented methodology are compared with various fuzzy MCDM methods, including fuzzy EDAS, fuzzy TOPSIS, fuzzy WASPAS, fuzzy ARAS, and fuzzy COPRAS. The results show that the presented methodology is efficient and stable for solving personnel selection problems in a hesitant fuzzy environment.

Dimensional Analysis under Pythagorean Fuzzy Approach for Supplier Selection

The supplier appraisal process is one of the most important decision problems for companies focused on improving supply chain costs. Supplier selection is typically a multi-criteria decision making (MCDM) issue, as there is a lot of uncertain information. In order to overcome this issue, The Pythagorean Fuzzy Set is applied to handle the uncertainties involved in comparing the alternatives, criteria and opinions of decision makers. At the same time, a potential of Dimensional Analysis is a technique which deploys an association of the criteria capturing the interrelationship normally present in MCDM. In this sense, the purpose of this paper is to evaluate the suppliers in a supply chain cycle using Pythagorean Fuzzy Set and Dimensional Analysis. Finally, the applicability of the proposed method is illustrated through numerical examples, and a validation via Spearman correlation and Cronbach’s alpha.

An Interval Pythagorean Fuzzy Multi-criteria Decision Making Method Based on Similarity Measures and Connection Numbers

Interval Pythagorean fuzzy set (IPFS), which can handle imprecise and ambiguous information, has attracted considerable attention in both theory and practice. However, one of the main difficulties under IPFSs is the comparison between interval numbers. To overcome this shortcoming, connection number theory is first introduced, and interval numbers are transformed into connection numbers in the operating process. Considering that similarity measures play an important role in assessing the degree between ideal and proposal alternatives in the decision making process, this paper aims to develop new similarity measures with IPFSs and apply them to multi-criteria decision making (MCDM) problems. The main contributions of this paper are as follows: (1) introduction of a comparison method through transforming interval numbers into connection numbers; (2) development of three new similarity measures with IPFSs based on the minimum and maximum operators, and investigation of their properties; (3) calculation of the similarity measures considering weights of membership and non-membership degrees; (4) establishment of an interval Pythagorean fuzzy decision making method applying the presented similarity measures. A case study on selecting a project delivery system is made to show the applicability of the proposed approach.