Fuzzy Multi-criteria Decision-making Method Research Articles

A novel hybrid evaluation framework for public organizations based on employees’ performance factors

Performance evaluation is a comprehensive process for comparing activities compared with predetermined organizational criteria. Based on the results of performance evaluation, organizations can embark on purposeful actions to improve their situation. Obviously, the success of organizations and the economic development of the country depend on the performance of human resources (HR). This paper proposes a novel hybrid evaluation framework for analyzing the performance of public sector organizations. The suggested setting allows for performance evaluation based on a comprehensive approach involving expert knowledge. For this purpose, the research integrates the Balanced Scorecard (BSC) approach and fuzzy multi-criteria decision-making (MCDM) methods. Initially, the employees’ performance factors (criteria) are identified from the literature based on the BSC dimensions. Then, the most relevant of them are finalized through the fuzzy Delphi method (FDM) questionnaires and experts’ opinions. In the second stage, the weights of criteria are determined by adopting the fuzzy best-worst method (FBWM). Moreover, two fuzzy MCDM techniques, namely VIKOR and Grey Relational Analysis (GRA), are used to examine the performance of the eight important public sector organizations of Iran. Finally, a Monte Carlo simulation-based (MCSB) approach and a scenario-based (SCB) approach are applied to compare the effectiveness of fuzzy VIKOR and fuzzy GRA. The results suggest that the critical employees’ performance factors include sharing knowledge with colleagues (0.136), optimal use of facilities (0.123) and participation in solving organizational issues (0.118). Additionally, based on the implemented sensitivity analysis approaches, fuzzy VIKOR generates more reliable results and also has higher robustness than fuzzy GRA.

Identification of Shipyard Priorities in a Multi-Criteria Decision-Making Environment through a Transdisciplinary Energy Management Framework: A Real Case Study for a Turkish Shipyard

Ship building, as an energy-intensive sector, produces significant amounts of air emissions, including greenhouse gases. Most research in greenhouse gas reductions from shipping concentrates on the reduction in emissions during the operational phase. However, as emissions during ship operation are reduced, the construction and dismantling phases of ships are becoming increasingly important in the assessment of the life-cycle impact of ships. In this study, priorities for a Turkish shipyard to become energy efficient were identified by means of a semi-structured questionnaire and an interview. This was undertaken using Fuzzy Multi-Criteria Decision-Making methods, including the Fuzzy Analytical Hierarchy Process and Fuzzy Order of Preference by Similarity to Ideal Solution, which are part of a proposed systematic and transdisciplinary Energy Management Framework and System. By applying Multi-Criteria Decision-Making methods, this framework supports the shipyard’s decision makers to make rational and optimized decisions regarding energy sectors within their activities. Applying the framework has significant potential to help achieve good product quality while reducing costs and environmental impacts, and can thereby enhance the sustainability of shipping. Moreover, the framework can boost both business and socio-economic perspectives for the shipyard, and improve its reputation and competitiveness, in alignment with achieving the Nationally Determined Contributions of States under the Paris Agreement.

Group risk assessment in failure mode and effects analysis using a hybrid probabilistic hesitant fuzzy linguistic MCDM method

Failure mode and effects analysis (FMEA) usually requires multi-domain specialists to implement the group risk assessment for identifying and eliminating system failures. Therefore, this paper combines several multi-criteria decision making (MCDM) techniques with probabilistic hesitant fuzzy linguistic term sets (PHFLTSs) to implement risk assessment of failure modes by a panel of specialists. It aims at overcoming some defects existing in the conventional FMEA, such as without epistemic uncertainty and group risk assessment, as well as with some questions incurring from the risk priority number (RPN). Consequently, group members utilize PHFLTSs to express their subjective uncertain risk assessments on failure modes, in which the social network analysis (SNA) and maximizing consensus method (MCM) are exploited to derive the subjective and objective weights of group members respectively, afterwards their integrated weights are employed to aggregate individual risk assessments into the collective risk assessment. Additionally, the subjective and objective weights of risk factors are garnered by the best-worst method (BWM) and maximizing deviation method (MDM) separately, from which their integrated weights are incorporated into the technique for order preference by similarity to ideal solution (TOPSIS) so as to obtain the risk ranking of failure modes. Finally, an example with sensitive and comparative analyses is presented to demonstrate the effectiveness of the proposed approach.

Hospital site selection using fuzzy EDAS method: case study application for districts of İstanbul

As the Covid-19 pandemic also proved, access to health care plays a crucial role in our lives. Public officials, managers and investors should consider many criteria such as public infrastructure, environment, accessibility and demand for selecting the most appropriate site for a new hospital. Thus, the hospital site selection problem is a critical multi-criteria decision-making (MCDM) problem. This paper is a case study for İstanbul where a recent MCDM methodology, the fuzzy Evaluation based on Distance from Average Solution (EDAS) method, is applied to this problem for the first time. We used a comprehensive set of five main criteria and 17 sub-criteria found in the relevant literature regarding hospital site selection. These criteria were evaluated by three decision-makers to choose the hospital site from three districts. The recommendation of the fuzzy EDAS method was then compared to the outcome of a frequently used fuzzy MCDM method. The methods resulted in different site recommendations.

Trading off Time–Cost–Quality in Construction Project Scheduling Problems with Fuzzy SWARA–TOPSIS Approach

The increasing number of construction projects together with the limited resources of organizations led to tough competition for achieving project goals. Time, cost, and quality have been known as the project iron triangle. Project managers attempt to allocate the appropriate resources and make the best decisions for accomplishing projects with the shortest durations, lowest costs, and the highest quality. No study has examined the time–cost–quality trade-off problem with decision-making approaches. In this study, the fuzzy multi-criteria decision-making (MCDM) methods are exploited to choose the best mode for performing each activity. For this purpose, the SWARA method is applied to determine the importance weights of time, cost, and quality. In addition, the TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) technique is used to rank and select the best activity execution modes. The proposed model is implemented on two medium- and large-size construction projects to evaluate its efficiency. Several execution modes with fuzzy duration, cost, and quality are considered for each project activity. Finally, sensitivity analysis is conducted taking three different conditions into account: the shortest duration of the execution modes, the lowest cost of the execution modes, and the highest quality of execution modes for each activity. The solution of each trade-off is compared with the solution obtained from the fuzzy SWARA–TOPSIS method. The schedule is developed according to the best execution mode for each project activity. The obtained results in two different construction projects show significant improvements in the overall project objectives so that the projects can be completed in fewer durations and costs along with higher quality. Because of the higher importance of cost, the cost of each activity is closer to the lowest cost. The activity duration is also closer to the most likely duration, and quality is closer to the high-quality level. The application of this approach can create new opportunities for research and knowledge development in the field of construction project scheduling.

A new fuzzy multi-criteria decision-making method based on proximity index value

ABSTRACT This work proposes a Fuzzy Proximity Index Ranking method to facilitate multi-criteria decision making in fuzzy environment. The proposed method is derived from proximity index value approach by incorporating the fuzzy concept to take care of vagueness in the information provided by the experts. The method is implemented on several multi-criteria decision-making problems taken from the literature to investigate its performance. Ranking results of the proposed method are found closer to those of Fuzzy TOPSIS, Fuzzy MOORA, Fuzzy VIKOR and Fuzzy ELECTRE. Further, a few case studies on rank reversal are presented on three different fuzzy scenarios to examine the robustness of the method. The results revealed that the proposed method suffers least from the rank reversal issue and it generates reasonably accurate ranking order compared to other techniques. Hence, the proposed method is considered a simple fuzzy based multi-criteria decision approach which can be a reliable substitute for existing methods.

Stock selection multicriteria decision‐making method based on elimination and choice translating reality I with Z‐numbers

Stock selection for effective investment decisions is a valuable and attractive research interest for many years. Owing to the uncertainty and complexity of the stock market, many fuzzy multicriteria decision-making (MCDM) methods were proposed to solve stock selection problems. However, these methods have difficulty in characterizing unreliable information, which is widespread in the stock market, and handling the non-compensation among multiple criteria. In this paper, an innovative method is developed from the perspectives of information reliability and criterion non-compensation to manage stock selection problems. First, the Z-number, which is a powerful tool for describing real-life information and identifying information reliability, is introduced to depict stock evaluation information. Second, the outranking degree of Z-numbers is defined based on the fuzzy and probability information. Subsequently, some outranking aggregation and exploitation procedures are presented based on the idea of Elimination and Choice Translating Reality (ELECTRE) I to handle the non-compensation among stock evaluation criteria. By integrating the above studies, a Z-number ELECTRE I MCDM method is developed. Finally, a stock investment object selection problem is solved, and some discussions and analyses are conducted to testify the applicability and validity of this method.

A NEW HYBRID FUZZY PSI-PIPRECIA-COCOSO MCDM BASED APPROACH TO SOLVING THE TRANSPORTATION COMPANY SELECTION PROBLEM

Nowadays, customers are not only interested in the quality of products, but they also want to have these products in a timely manner. The managers of an organization are faced with two problems when the distribution of products is in question, namely: (1) customers are usually geographically dispersed and (2) transportation should be performed in a cost-effective way. Although managers may have a significant experience and formal knowledge, decisions connected with the selection of an appropriate transportation company may very often be biased. For the purpose of avoiding making the inadequate decisions that might harm the operation of the organization, the application of a hybrid MCDM model is proposed in this paper. The proposed model consists of three fuzzy MCDM methods, including: the PIPRECIA, the PSI, and the CoCoSo methods. The fuzzy-PIPRECIA method is used to achieve the subjective weights of criteria, whereas the fuzzy-PSI method is used to obtain the objective weights of criteria. Fuzzy-CoCoSo is utilized to rank alternative transportation companies according to their performances. The possibilities of the proposed hybrid model are tested on a real case study pointed at the selection of an appropriate company for the transportation of ready-garments to retailers in Turkey.

A fuzzy rule-based Fuzzy Inferior Ratio method with reliability factor

Multi-criteria decision-making (MCDM) is a branch of decision-making method which able to deal with complex and conflicting decision problems. Besides, Fuzzy Multi-Criteria Decision-Making (FMCDM) method is later introduced to deal with vague and uncertain information in decision-making problems that imitates human perception in the evaluation process. Recently, Fuzzy Inferior Ratio (FIR) is developed as one of the effective FMCDM methods as it includes the compromise solution in which both distances to the Fuzzy Positive Ideal Solution (FPIS) and the Fuzzy Negative Ideal Solution (FNIS) are considered simultaneously. Nevertheless, in FIR, there is no consideration on the reliability of the information in the evaluation process. In this paper, an improvised FIR with reliability factor is introduced where the reliability factor is represented by Z-numbers. Furthermore, the lack of capability of providing systematic evaluation and consistent output raises a concern in most FMCDM methods including FIR. Hence, a decision-making procedure of Fuzzy Rule-Based FIR with reliability factor is also been proposed. A numerical example is given using the proposed procedure and a consistency analysis is carried out using the Spearman’s Rho Correlation to evaluate its effectiveness. It is found out that the proposed procedure gives a more comprehensive and systematic evaluation with the inclusion of reliability factor and the Fuzzy Rule-Based evaluation in FIR.

Application of a Fuzzy MCDM Method to Select the Best Operating System for an Efficient Security-Aware Design of Embedded Systems

In this work, the authors present their solution to select the best operating system for an efficient security-aware design of embedded systems. This problem is formulated as a MCDM problem and solved using a hybrid approach combining fuzzy AHP and fuzzy VIKOR. This combination enables the authors to take profit of both methods. From AHP, they exploited the hierarchy and the pairwise comparison between criteria that leads to finding the importance (weight) of each criteria more consistently. On the other hand, from the VIKOR method, they leverage its power to compromise between conflictual criteria. Since they are dealing with unprecise and subjective advises, they opt for the fuzzy versions of the AHP and VIKOR methods dealing with triangular fuzzy numbers. They used fuzzy AHP to calculate weights of criteria which are served later as inputs for the fuzzy VIKOR method. The outcome of this work is to assist embedded designers to select the most appropriate embedded OS for efficient design of secure embedded systems.

Evaluation of government strategies against COVID-19 pandemic using q-rung orthopair fuzzy TOPSIS method

The COVID-19 outbreak, which emerged in China and continues to spread rapidly all over the world, has brought with it increasing numbers of cases and deaths. Governments have suffered serious damage and losses not only in the field of health but also in many other fields. This has directed governments to adopt and implement various strategies in their communities. However, only a few countries succeed partially from the strategies implemented while other countries have failed. In this context, it is necessary to identify the most important strategy that should be implemented by governments. A decision problem based on the decisions of many experts, with some contradictory and multiple criteria, should be taken into account in order to evaluate the multiple strategies implemented by various governments. In this study, this decision process is considered as a multi-criteria decision making (MCDM) problem that also takes into account uncertainty. For this purpose, q-rung orthopair fuzzy sets (q-ROFSs) are used to allow decision-makers to their assessments in a wider space and to better deal with ambiguous information. Accordingly, two different Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches are recommended under the q-ROFS environment and applied to determine the most appropriate strategy. The results of the proposed approaches determine the A1 — Mandatory quarantine and strict isolation strategy as the best strategy. Comparisons with other q-rung orthopair fuzzy MCDM methods and intuitionistic fuzzy TOPSIS method are also presented for the validation of the proposed methods. Besides, sensitivity analyses are conducted to check the robustness of the proposed approaches and to observe the effect of the change in the q parameter.

A calibrated piecewise-linear FGM approach for travel destination recommendation during the COVID-19 pandemic

After months of lockdown due to the COVID-19 pandemic, more people are planning regional trips because overseas travel is still not feasible. However, choosing a suitable travel destination during the COVID-19 pandemic is challenging because the factors critical to the selection process are very different from those usually considered. Furthermore, without sufficient literature or data for reference, existing methods based on psychological analyses or mining past experiences may not be applicable. Consequently, a fuzzy multi-criteria decision-making method – the calibrated piecewise-linear fuzzy geometric mean (FGM) approach – is proposed in this study for travel destination recommendation during the COVID-19 pandemic. The contribution of this research is twofold. First, the critical factors that affect the selection of a suitable travel destination during the COVID-19 pandemic are discussed. Second, the accuracy and efficiency using existing fuzzy analytic hierarchy process (FAHP) methods have been enhanced. The calibrated piecewise-linear FGM approach has been successfully applied to recommend suitable travel destinations to fifteen travelers for regional trips in Taiwan during the COVID-19 pandemic.

Modeling cause-and-effect relationships among predictive variables of human error based on the fuzzy multi-criteria decision-making method

Human error is the result of different variables the study of which helps prevent committing such errors. This study aimed at modeling the cause-and-effect relationships among different variables that can predict human error based on the fuzzy multi-criteria decision-making method. The study was carried out based on the opinions of 17 safety experts in 2020. The variables of the study were determined by reviewing previous research and the expert panel’s brainstorming process. The results revealed that among 14 studied variables, shift work, job satisfaction, environmental discomfort and role conflict can be singled out as the root causes of human error. Although human error is affected by each studied variable, each variable can influence human error differently. Hence, safety and controlling measures presented to reduce and prevent human error need to take into account the different roles and influence weight of variables. The result of this study is applicable to researchers and managers in different work environments.

A robust fuzzy hybrid MCDM ranking method for optimal selection of lithium extraction process from brine and seawater

In this paper, we propose a new framework that integrates two fuzzy group decision making approaches including TOPSIS and ELECTRE II. The main objective of this combination is to improve the outcomes provided by these methods through increasing the differentiation between the alternatives in fuzzy TOPSIS and decreasing the incomparability between the alternatives in fuzzy ELECTRE. In addition, to more precisely compute the criteria weights, the correlation coefficient and standard deviation integrated model (CCSD) are used. This research has been inspired from a real case study in lithium extraction processes. Due to the quite limited mineral resources of lithium and the global efforts to replace a clean and recyclable energy instead of fossil fuels, the challenge for lithium extraction from other resources such as brines and seawater has been increased significantly. The proposed framework helps to select the most suitable process of lithium extraction from brines and seawater based on a complex multi-criteria decision making. In view of the fact that efficiency of the decision-making procedures is significantly depends on the stability of the solution, the sensitivity analysis is performed. The performance and capabilities of the proposed framework are appraised by comparing its results with some existing fuzzy MCDM methods through five lithium extraction processes from brines and seawater, as a case study. The results of the analysis indicate the rationality and enforceability of the proposed method.

Resilience cloud-based global supply chain network design under uncertainty: Resource-based approach

This study presents a fuzzy multi-objective mathematical programming model to design an efficient and resilience global supply chain network structure based on the service-oriented approach. Service composition and transportation within globally scattered resources are considered under the cloud manufacturing paradigm. The proposed comprehensive model is adapted to a global electrical medical device manufacturing system. Key performance measures include robustness, agility, leanness, and flexibility as resiliency pillars are considered in designing the network under uncertainty. To efficiently deal with the computational tractability of this non-linear and multi-objective optimization problem, a new hybrid solution algorithm is developed that incorporates fuzzy multi-criteria decision-making methods and augmented ε-constraint method. Obtained computational results of a real case study present that the proposed service-oriented global supply chain network design can respond to its global customers’ demands in a resilient as well as efficient manner.

Analysis and Comparison of State-of-the-Art Fuzzy Multi-criteria Decision-making Methods Under Different Levels of Uncertainty

Often, data in multi-criteria decision-making (MCDM) problems are imprecise and changeable due to the mandatory participation of human judgement, which is often unclear and vague. Besides, different MCDM methods may produce different results under different levels of uncertainty and require divergent levels of computational resources. Therefore, the quality of the decision and the amount of effort are heavily affected by selection of the MCDM method. With the regular proliferation of such methods and their modifications, it is important to carry out a comparative study that provides comprehensive insight into their performances under uncertain conditions. In this study, we use the randomized quasi-Monte Carlo simulation approach to compare empirically the results produced by 12 classic and contemporary fuzzy MCDM (FMCDM) approaches with rank-reversal perspective over increasing uncertainty in various decision scenarios. Furthermore, this study also investigates the similarity between ranks produced by each pair of methods for the same decision problems. The study further compares the results obtained by quasi-Monte Carlo simulation with the results obtained by Monte Carlo simulation. The findings of this study will assist decision-makers in the selection of most appropriate fuzzy MCDM approach for different decision scenarios. The results of this research are significant additions to the current repository of knowledge in the multi-criteria decision analysis as well as the literature pertaining to the Information Systems. It also provides insights for many managerial applications of these MCDM methods.

AN OVERVIEW OF FUZZY TECHNIQUES IN SUPPLY CHAIN MANAGEMENT: BIBLIOMETRICS, METHODOLOGIES, APPLICATIONS AND FUTURE DIRECTIONS

Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM.

On the Analytic Hierarchy Process Structure in Group Decision-Making Using Incomplete Fuzzy Information with Applications

The multi-criteria decision-making (MCDM) problem has a solution whose quality can be affected by the experts’ inclinations. Under essential conditions, the fuzzy MCDM method can provide more acceptable and efficient outcomes to select the best alternatives. This work consists of a consensus-based technique for selecting and evaluating suppliers in an incomplete fuzzy preference relations (IFPRs) environment utilizing TL-transitivity (Lukasiewicz transitivity). The suggested method is developed based on the criteria of the Analytical Hierarchy Process (AHP) Fframework, and the decision matrix is construtced using consistent fuzzy preference relations (FPRs). We use the symmetrical decisional matrix approach. A variety of numerical explanations and an analysis of quantitative results illustrate the suggested methodology’s logic and effectiveness.

Novel Fuzzy Composite Indicators for Locating a Logistics Platform under Sustainability Perspectives

The purpose of this paper is to help decision-makers choose the location of a logistics platform with sustainability perspectives. This study presents a compensatory and partially compensatory approach to build composite indicators, using mainly fuzzy multi-criteria decision-making methods. In the first instance, the fuzzy full consistency method (F-FUCOM) was used to calculate the weight of the criteria and sub-criteria. In the second instance, two aggregation methods, namely the fuzzy multi-attribute ideal-real comparative analysis (F-MAIRCA) and the fuzzy preference ranking organization method for enrichment evaluation (F-PROMETHEE), were used to rank the location of a logistics platform. The novelty of the work lays in studying the impact of limited sustainability and weak sustainability on the location of a logistics platform. In this respect, the aggregation of various sustainability criterion in fuzzy compensatory and partially compensatory composite indicators is an innovative and interesting approach used to locate a logistics platform. The obtained results show that economic sustainability is the most important criterion for the selection of a logistics platform, followed by the environmental criterion. Obviously, the F-MAIRCA and F-PROMETHEE methods provided the same ranking orders. Finally, sensitivity analyses were performed to validate the robustness of the proposed approach.

A Fuzzy MCDM Method Based on New Fermatean Fuzzy Theories

This paper presents a new Multi-criteria Decision-Making (MCDM) method with Fermatean fuzzy sets (FFSs). The proposed method uses the entropy theory to determine the weights of criteria and utilize cosine similarity measures to determine the best alternative. First, we develop a new Fermatean fuzzy entropy formula based on the Euclidean distance between Fermatean fuzzy number (FFN) and its compliment. The properties of the proposed formula and the proof of the properties are also given. Then, Fermatean fuzzy cosine similarity measures are introduced. We develop four different Fermatean fuzzy cosine similarity measures, also properties and proof of the properties are worked out systematically. Then, the algorithm of the proposed Fermatean fuzzy MCDM method, which includes Fermatean fuzzy entropy and Fermatean fuzzy cosine similarity measures, is introduced. The advantage of the proposed method is that Fermatean fuzzy entropy calculates how much valuable knowledge the current data provides in weights of criteria, and Fermatean fuzzy cosine similarity measures define the similarity between alternatives and ideal solution and negative ideal solution, in this way the method determines the best alternative smoothly. To show the applicability of the proposed method, an illustrative example is given for third party logistic (3PL) firm evaluation problem in cold chain management. In the illustrative example section, we determine six different criteria and six different 3PL alternatives. Then, alternatives are evaluated according to the proposed Fermatean fuzzy MCDM method. Moreover, the results are compared to the Euclidean measure, and sensitivity analysis is also performed. The comparison analysis results show that our model works efficiently and effectively.