Multi-criteria Group Decision-making Approach Research Articles

Sustainable supply chain partner selection and order allocation: A hybrid fuzzy PL-TODIM based MCGDM approach

Sustainability, as a trend of social development and the embodiment of corporate social responsibility, has begun to receive more attention. To achieve this goal, sustainable supplier selection (SSS) and order allocation (OA) are seen as the crucial activities in corporate management. In the process of SSS, the psychological behavior of decision-makers (DMs) could play a critical role in the evaluation results. Therefore, introducing it into the decision-making process may lead to decision in line with the actual situation. In the uncertain multi-criteria group decision-making (MCGDM) problem described by probability linguistic term sets (PLTS), the DMs can evaluate the criteria of each supplier based on his own preference and hesitation, which is useful to avoid the loss of information. For this reason, this study develops a novel multi-criteria group decision-making combined with fuzzy multi-objective optimization (MCGDM-FMOO) model for SSS/OA problems by considering the triple bottom line (TBL) in which includes economic, environmental and social factors. The proposed method includes four stages. (1) the best-worst method (BWM) and entropy weight method are utilized to assign the weights of criteria to obtain the comprehensive weight. According to the output weights, the an acronym for interactive and multi-criteria decision-making in Portugese (TODIM) approach is applied to rank the suppliers under PLTS environment; (2) a FMOO model that can effectively deal with uncertainties and dynamic nature of parameter is formulated for allocating optimal order quantities; (3) two novel approaches are utilized to solve the FMOO model in order to obtain the richer Pareto frontier; and (4) the final OA solution is achieved by technique for order preference by similarity to ideal solution (TOPSIS) method. Finally, the validity and practicability of proposed MCGDM-FMOO model are verified by an example and comparative analysis with other classical MCGDM methods.

A multi-criteria group decision-making approach based on revised distance measures under dual hesitant fuzzy setting with unknown weight information

A multi-criteria group decision-making approach based on revised distance measures under dual hesitant fuzzy setting with unknown weight information

Fermatean Fuzzy Schweizer–Sklar Operators and BWM-Entropy-Based Combined Compromise Solution Approach: An Application to Green Supplier Selection

The Fermatean fuzzy set (FFS) is a momentous generalization of a intuitionistic fuzzy set and a Pythagorean fuzzy set that can more accurately portray the complex vague information of elements and has stronger expert flexibility during decision analysis. The Combined Compromise Solution (CoCoSo) approach is a powerful decision-making technique to choose the ideal objective by fusing three aggregation strategies. In this paper, an integrated, multi-criteria group-decision-making (MCGDM) approach based on CoCoSo and FFS is used to assess green suppliers. To begin, several innovative operations of Fermatean fuzzy numbers based on Schweizer–Sklar norms are presented, and four aggregation operators utilizing the proposed operations are also developed. Several worthwhile properties of the advanced operations and operators are explored in detail. Next, a new Fermatean fuzzy entropy measure is propounded to determine the combined weight of criteria, in which the subjective and objective weights are computed by an improved best-and-worst method (BWM) and entropy weight approach, respectively. Furthermore, MCGDM based on CoCoSo and BWM-Entropy is brought forward and employed to sort diverse green suppliers. Lastly, the usefulness and effectiveness of the presented methodology is validated by comparison, and the stability of the developed MCGDM approach is shown by sensitivity analysis. The results shows that the introduced method is more stable during ranking of green suppliers, and the comparative results expound that the proposed method has higher universality and credibility than prior Fermatean fuzzy approaches.

A novel hybrid multi-criteria group decision-making approach with intuitionistic fuzzy sets to design reverse supply chains for COVID-19 medical waste recycling channels

The COVID-19 pandemic has led to exponential growth in COVID-19 medical waste (CMW) generation worldwide. This tremendous growth in CMW is a major transmission medium for COVID-19 virus and thus brings serious challenges to medical waste (MW) management. Designing an efficient and reliable CMW reverse supply chain in this situation can help to prevent epidemic spread. Nowadays, the assessment of CMW recycling channels has become a challenging mission for health-care institutions, especially in developing countries. It can be seen as a complex multi-criteria group decision-making (MCGDM) problem that requires the consideration of multiple conflicting tangible and intangible criteria. Nevertheless, few academics have been concerned about this issue. Moreover, current MCGDM methods have limited support for CMW recycling channel evaluation and they do not consider hospitals’ reverse supply chain strategy when evaluating. Thus, this study presents a novel MCGDM approach based on intuitionistic fuzzy sets (IFSs) and the VIKOR method for assessing the capacity of CWM recycling channels. According to the characteristics of CMW, processing flow and the TOE (Technology, Organization and Environment) theoretical framework, we established a new CMW recycling channel capacity evaluation index system which makes our proposed method more targeted and efficient. In the decision-making process, we integrate the best-worst method (BWM) and entropy to determine the decision makers (DMs) weighting in a more comprehensive way, considering both subjective and objective criteria, which was ignored by many MCGDM methods. A new aggregation operator called IFWA is proposed by us, considering the priority of DMs. Based on both the ranking of capacity and disposal charges, we then position the alternatives in the recycling channel priority index (RCPI) matrix constructed by us. According to this PCPI matrix and the reverse supply chain strategy of hospitals, a more reasonable CMW allocation strategy is determined and a more efficient CMW reverse supply chain is designed. Finally, a real case study from Wuhan was examined to illustrate the validation of our approach.

A Risk Assessment Model for Cyber-Physical Water and Wastewater Systems: Towards Sustainable Development

Cyber-physical systems (CPS) and their Supervisory Control and Data Acquisition (SCADA) have attracted great interest for automatic management of industrial infrastructures, such as water and wastewater systems. A range of technologies can be employed for wastewater treatment CPS to manage risks and protect the infrastructures of water systems and their wastewater against cyberattacks. In this paper, we develop a novel risk assessment framework, named RAF-CPWS, which perfectly estimates the risks of water and wastewater technologies. To do this, a multi-criteria group decision-making (MCGDM) approach is designed by neutrosophic theory to assess the risks of wastewater treatment technologies (WWTTs). The proposed approach evaluates the best WWTTs, considering various economic, environmental, technological and cybersecurity, and social factors. A decision-making trial and evaluation laboratory (DEMATEL) is employed to evaluate the significance of the adopted factors in a real testbed setting. The proposed approach contributes to a comprehensive measure of WWTTs through several factors, revealing its high sustainability and security in assessing the risks of cyber-physical water and wastewater systems.

Multi-criteria Group Decision-Making Approach for Express Packaging Recycling Under Interval-Valued Fuzzy Information: Combining Objective and Subjective Compatibilities

Multi-criteria Group Decision-Making Approach for Express Packaging Recycling Under Interval-Valued Fuzzy Information: Combining Objective and Subjective Compatibilities

A Fermatean Fuzzy Multi-Criteria Group Decision Making Approach Based on Reliability for the Evaluation of Indian Eletronics Manufacturing Firms

Fermatean fuzzy sets are widely used in multi-criteria group decision-making (MCGDM) process as it offers great convenience in describing expert inference information. In this study, we propose a new Fermatean fuzzy MCGDM approach in which the criteria weights are derived based on the reliability. Firstly, the exponential Fermatean fuzzy entropy (EFFE) measure is introduced to measure the uncertainty of Fermatean fuzzy information. Secondly, combining the EFFE and maximizing deviations approach, a reliability-based optimized model for solving criteria weights is proposed. Subsequencely, We construct a new MCGDM approach with unknown criteria weights by this optimization model. Finally, we implement our approach to evaluation of Indian eletronics manufacturing firms.

Decision support system based on complex T-Spherical fuzzy power aggregation operators

<abstract><p>The goal of this research is to develop many aggregation operators for aggregating various complex T-Spherical fuzzy sets (CT-SFSs). Existing fuzzy set theory and its extensions, which are a subset of real numbers, handle the uncertainties in the data, but they may lose some useful information and so affect the decision results. Complex Spherical fuzzy sets handle two-dimensional information in a single set by covering uncertainty with degrees whose ranges are extended from the real subset to the complex subset with unit disk. Thus, motivated by this concept, we developed certain CT-SFS operation laws and then proposed a series of novel averaging and geometric power aggregation operators. The properties of some of these operators are investigated. A multi-criteria group decision-making approach is also developed using these operators. The method's utility is demonstrated with an example of how to choose the best choices, which is then tested by comparing the results to those of other approaches.</p></abstract>

Multigranulation Modified Rough Bipolar Soft Sets and Their Applications in Decision-Making

The classical theory of rough sets (RSs) established by Pawlak, mainly focused on the approximation of sets characterized by a single equivalence relation (ER) over the universe. However, most of the current single granulation structure models cannot meet the user demand or the target of solving problems. Multi-granulation rough sets (MGRS) can better deal with the problems where data might be spread over various locations. In this article, based on modified rough bipolar soft sets (MRBSs), the concept of multi-granulation MRBSs (MGMRBSs) is introduced. A finite collection of bipolar soft sets (BSs) has been used for this purpose. Several important structural properties and results of the suggested model are carefully analyzed. Meanwhile, to measure the uncertainty of MGMRBSs, some important measures associated with MGMRBSs are presented in MGMRBS-approximation space, and some of their interesting properties are examined. In the framework of multi-granulation, we developed optimistic MGMRBSs (OMGMRBSs) and pessimistic MGMRBSs (PMGMRBSs). The relationships among the MGMRBSs, OMGMRBSs, and PMGMRBSs are also established. After that, a novel multi-criteria group decision-making (MCGDM) approach based on OMGMRBSs is developed to solve some problems in decision-making (DM). The basic principles and the detailed steps of the DM model are presented in detail. To demonstrate the applicability and potentiality of the developed model, we give a practical example of a medical diagnosis. Finally, we conduct a comparative study of the proposed MCGDM approach with some existing techniques to endorse the advantages of the proposed model.

Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology.

Health care systems around the world do not have sufficient medical services to immediately offer elective (e.g., scheduled or non-emergency) services to all patients. The goal of patient admission scheduling (PAS) as a complicated decision making issue is to allocate a group of patients to a limited number of resources such as rooms, time slots, and beds based on a set of preset restrictions such as illness severity, waiting time, and disease categories. This is a crucial issue with multi-criteria group decision making (MCGDM). In order to address this issue, we first conduct an assessment of the admission process and gather four (4) aspects that influence patient admission and design a set of criteria. Even while many of these indicators may be accurately captured by the picture fuzzy set, we use an advanced MCGDM approach that incorporates generalized aggregation to analyze patients' hospitalization. Finally, numerical real-world applications of PAS are offered to illustrate the validity of the suggested technique. The advantages of the proposed approaches are also examined by comparing them to various existing decision methods. The proposed technique has been proved to assist hospitals in managing patient admissions in a flexible manner.

A novel logarithmic operational law and aggregation operators for trapezoidal neutrosophic number with MCGDM skill to determine most harmful virus.

In the current era, the theory of vagueness and multi-criteria group decision making (MCGDM) techniques are extensively applied by the researchers in disjunctive fields like recruitment policies, financial investment, design of the complex circuit, clinical diagnosis of disease, material management, etc. Recently, trapezoidal neutrosophic number (TNN) draws a major awareness to the researchers as it plays an essential role to grab the vagueness and uncertainty of daily life problems. In this article, we have focused, derived and established new logarithmic operational laws of trapezoidal neutrosophic number (TNN) where the logarithmic base μ is a positive real number. Here, logarithmic trapezoidal neutrosophic weighted arithmetic aggregation (Larm) operator and logarithmic trapezoidal neutrosophic weighted geometric aggregation (Lgeo) operator have been introduced using the logarithmic operational law. Furthermore, a new MCGDM approach is being demonstrated with the help of logarithmic operational law and aggregation operators, which has been successfully deployed to solve numerical problems. We have shown the stability and reliability of the proposed technique through sensitivity analysis. Finally, a comparative analysis has been presented to legitimize the rationality and efficiency of our proposed technique with the existing methods.

Solving a Multiple User Energy Source Selection Problem Using a Fuzzy Multi-Criteria Group Decision-Making Approach

In recent years, the evaluation of several energy sources is an extremely significant issue that affects socio-environmental development and techno-economic growth in different sectors. To tackle this concern, many researchers have concentrated on preferring desirable energy sources and adopting multi-criteria group decision-making (MCGDM) approaches for only a single type of user (e.g., agricultural, industrial, tourism, or domestic users). However, every energy user plays an important role in shaping energy policy. In fact, for sustainable energy, it is important to include all main energy users at the same time in energy decision-making. The main objective of this work was to propose a fuzzy MCGDM approach to evaluate and prioritize energy sources in Tunisia from various sectors’ point of views. Many criteria are combined, including the following: technical, economic, social, political and environmental. After applying the fuzzy Delphi method, the proposed approach consists of applying a fuzzy TOPSIS method as a multi-criteria approach. Finally, a sensitivity analysis is performed to evaluate the results of the proposed approach and to study how the optimal solution is affected by the objective function coefficients.

Celebrity selection in social media ecosystems: a flexible and interactive framework

PurposeQuantitative objective studies on the problem of celebrity selection are lacking. Furthermore, existing research does not recognize the group decision-making nature and the possibility of customer involvement in celebrity or influencer selection for social media marketing. This study conceptualizes celebrity selection as a multi-attribute group decision-making problem while deriving the final ranking of celebrities/influencers using interactive and flexible criteria based on the value tradeoff approach. The article thus proposes and demonstrates a quantitative objective method of celebrity selection for a brand or campaign in an interactive manner incorporating customer's preferences as well.Design/methodology/approachEach decision-maker's preferences for celebrity selection criteria are objectively captured and converted into an overall group preference using a modified generalized fuzzy evaluation method (MGFEM). The final ranking of celebrities is then derived from an interactive and criteria-based value tradeoff approach using the flexible and interactive tradeoff method.FindingsThe approach gives a different ranking of celebrities for two campaigns based on group members' perceived importance of the selection criteria in different scenarios. This group includes decision-makers (DMs) from the brand, marketing communication agency and brand's customers. Further, each group member has an almost equal say in the decision-making based on fuzzy evaluation and an interactive and flexible value tradeoff approach to celebrity selection for receiving a rank order.Research limitations/implicationsThe approach uses secondary data on celebrities and hypothetical scenarios. Comparison with other methods is difficult, as no other study proposes a multi-criteria group decision-making approach to celebrity selection especially in a social media context.Practical implicationsThis approach can help DMs make more informed, objective and effective decisions on celebrity selection for their brands or campaigns. It recognizes that there are multiple stakeholders, including the end customers, each of whose views is objectively considered in the aspects of group decision-making through a fuzzy evaluation method. Further, this study provides a selection mechanism for a given context of endorsement by objectively and interactively encapsulating stakeholder preferences.Originality/valueThis robust and holistic approach to celebrity selection can help DMs objectively make consensual decisions with partial or complete information. This quantitative approach contributes to the literature on selection mechanisms of influencers, celebrities, social media opinion leaders etc. by providing a methodological aid that encompasses aspects of interactive group decision-making for a given context. Moreover, this method is useful to DMs and stakeholders in understanding and incorporating the effect of nature or context of the brand and the campaign type in the selection of a celebrity or an influencer.

A Novel Multi-Criteria Group Decision-Making Approach Based on Bonferroni and Heronian Mean Operators under Hesitant 2-Tuple Linguistic Environment

Ambiguous and uncertain facts can be handled using a hesitant 2-tuple linguistic set (H2TLS), an important expansion of the 2-tuple linguistic set. The vagueness and uncertainty of data can be grabbed by using aggregation operators. Therefore, aggregation operators play an important role in computational processes to merge the information provided by decision makers (DMs). Furthermore, the aggregation operator is a potential mechanism for merging multisource data which is synonymous with cooperative preference. The aggregation operators need to be studied and analyzed from various perspectives to represent complex choice situations more readily and capture the diverse experiences of DMs. In this manuscript, we propose some valuable operational laws for H2TLS. These new operational laws work through the individual aggregation of linguistic words and the collection of translation parameters. We introduced a hesitant 2-tuple linguistic weighted average (H2TLWA) operator to solve multi-criteria group decision-making (MCGDM) problems. We also define hesitant 2-tuple linguistic Bonferroni mean (H2TLBM) operator, hesitant 2-tuple linguistic geometric Bonferroni mean (H2TLGBM) operator, hesitant 2-tuple linguistic Heronian mean (H2TLHM) operator, and a hesitant 2-tuple linguistic geometric Heronian mean (H2TLGHM) operator based on the novel operational laws proposed in this paper. We define the aggregation operators for addition, subtraction, multiplication, division, scalar multiplication, power and complement with their respective properties. An application example and comparison analysis were examined to show the usefulness and practicality of the work.

An integrated fuzzy multi-criteria group decision-making approach for supplier evaluation and selection in oil and gas industry

Supplier selection is an important and strategic decision of supply chain. Identifying and selecting the right suppliers to provide specialty chemicals, spares parts, oils and lubricant in an oil and gas industry impacts oilfield operations, supply chain performance and competitiveness of the firm. The supplier evaluation and selection is based on multiple quantitative and qualitative criteria. Thus, it is a multi-criteria group decision making (MCGDM) problem which involves importance ratings of suppliers with respect to criteria by group of decision makers (DMs) which are vague and imprecise. In this paper, a supplier's evaluation and selection based on nine criteria has been demonstrated using integrated fuzzy entropy (FEntropy) and fuzzy complex proportional assessment (FCOPRAS) methods in an Indian oil and gas company. Fuzzy entropy method is used for objective weight determination of criteria while FCOPRAS for suppliers ranking and selection.

An integrated fuzzy multi-criteria group decision-making approach for supplier evaluation and selection in oil and gas industry

Supplier selection is an important and strategic decision of supply chain. Identifying and selecting the right suppliers to provide specialty chemicals, spares parts, oils and lubricant in an oil and gas industry impacts oilfield operations, supply chain performance and competitiveness of the firm. The supplier evaluation and selection is based on multiple quantitative and qualitative criteria. Thus, it is a multi-criteria group decision making (MCGDM) problem which involves importance ratings of suppliers with respect to criteria by group of decision makers (DMs) which are vague and imprecise. In this paper, a supplier's evaluation and selection based on nine criteria has been demonstrated using integrated fuzzy entropy (FEntropy) and fuzzy complex proportional assessment (FCOPRAS) methods in an Indian oil and gas company. Fuzzy entropy method is used for objective weight determination of criteria while FCOPRAS for suppliers ranking and selection.

Weighted interval-valued dual-hesitant fuzzy sets and its application in teaching quality assessment

As a robust generalization to fuzzy set, the interval-valued dual-hesitant fuzzy set (IVDHFS) was put forward, which provided several possible membership and non-membership interval values to be associated with a specific substance. But IVDHFS does not couple the membership and non-membership values with their corresponding weights/importance degrees to assess a variable, which is irrational. To circumvent this issue, this study introduces the weighted interval-valued dual-hesitant fuzzy set (WIVDHFS) which describe the membership and non-membership values in the form of interval along with their weights. These assigned weights give more detail about the level of agreeness and disagreeness and thereby can help the decision makers (DMs) to obtain precise, rational, and consistent decision consequences. After introducing the pioneer notion of WIVDHFS, several related terms, viz. connected weighted interval-valued dual-hesitant element (WIVDHFE), score function, and accuracy function are defined. Using Archimedean t-norm and t-conorm, some basic operational laws for WIVDHFEs are constructed. Next, to aggregate WIVDHFEs, two types of aggregation operators based on Archimedean t-norm and t-conorm, namely the generalized weighted interval-valued dual-hesitant fuzzy-weighted averaging operator and the generalized weighted interval-valued dual-hesitant fuzzy-weighted geometric operator are designed along with their relevant properties. Meanwhile, some of their special cases and relationships are also explored. On the basis of special cases, a novel multi-criteria group decision-making approach under weighted interval-valued dual-hesitant fuzzy environment is then constructed. Finally, an application case about teaching quality assessment is presented, and some analyses and comparisons are provided to validate the proposed approach.

Intuitionistic Fuzzy Sets in Multi-Criteria Group Decision Making Problems Using the Characteristic Objects Method

Over the past few decades, several researchers and professionals have focused on the development and application of multi-criteria group decision making (MCGDM) methods under a fuzzy environment in different areas and disciplines. This complex research area has become one of the more popular topics, and it seems that this trend will be increasing. In this paper, we propose a new MCGDM approach combining intuitionistic fuzzy sets (IFSs) and the Characteristic Object Method (COMET) for solving the group decision making (GDM) problems. The COMET method is resistant to the rank reversal phenomenon, and at the same time it remains relatively simple and intuitive in practical problems. This method can be used for both symmetric and asymmetric information. The Triangular Intuitionistic Fuzzy Numbers (TIFNs) have been used to handle uncertain data. This concept can ensure the preference information about an alternative under specific criteria more comprehensively and allows for easy modelling of symmetrical or asymmetrical linguistic values. Each expert provides the membership and non-membership degree values of intuitionistic fuzzy numbers (IFNs). So this approach deals with a different kind of uncertainty than with hesitant fuzzy sets (HFSs). The proposed combination of COMET and IFSs required an adaptation of the matrix of expert judgment (MEJ) and allowed to capture the behaviour aspects of the decision makers (DMs). Therefore, we get more reliable solutions while solving MCGDM problems. Finally, the proposed method is presented in a simple academic example.

Towards a Hybrid Evaluation Approach to Chinese Sustainable Drinking Water Source Regions within an Uncertain Decision Environment

Confronted with the increasingly serious water pollution in China, companies are implementing scientific measures to strive for sustainable drinking water source regions (DWSRs). The challenge lies in how to evaluate available DWSRs. Considering the complexity and uncertainty existing in the evaluation process, we deem this process a complicated multi-criteria group decision-making (MCGDM) problem. It involves fuzzy decision information, prioritized relationships among different criteria, and distinctively allocated weights of decision makers regarding different alternatives. By utilizing the asymmetrical weak probabilistic hesitant fuzzy elements (P-HFES) to depict fuzzy data, this paper leads a direction to investigating a data-driven MCGDM approach and its application to evaluate available DWSRs. First, considering the common sense that criteria are not always given symmetrical priority, we propose a series of aggregation operators for integrating weak P-HFEs by including the prioritized relationship of criteria. Moreover, the importance weights of criteria and decision makers are objectively determined through a priority-based basic unit interval and monotonic (P-BUM) function and some novel distance measures, respectively. Finally, an evaluation approach is established. It aims to handle MCGDM problems with incomplete weight information regarding the criteria and decision makers in weak probabilistic hesitant fuzzy circumstance. Practically, focusing on evaluating three DWSRs, a realistic example of application is furnished to verify the feasibility of the proposed approach.

Multi-criteria group decision-making approach to the modernization of hold baggage security screening system at an airport

The decisions on the modernization of a hold baggage screening system (HBSS) at an airport require taking into account multiple assessment criteria of the considered variants. These criteria are both objective - precisely quantifiable, and subjective - requiring descriptive assessment by experts. The purpose of this article was to make a multi-criteria assessment of variants of the modernization of the HBSS, considering various points of view of experts and the context of decisions made. Due to the existence of both the criteria described using linguistic variables and those dependent on complex random processes, both fuzzy inference methods and simulation methods based on microscale models were used. The developed method was implemented to assess the modernization variants for Katowice International Airport. The highest score resulting from the group decision-making was obtained by the modernization variant consisting in reducing the number of baggage control levels with the simultaneous increase in the number of people performing manual checks. If we consider a different decision-making process environment, the group's preferred variant changes. For example, for the scenario involving a significant increase in the size of the traffic being handled, the variant of doubling X-ray devices at the first level of control becomes the most advantageous. The change in the group preference, depending on the context of the decision-making situation, clearly indicates that this context must be taken into account in the decision-making process. The proposed method allows one not only to indicate the preferred variant in each scenario but also to numerically determine the strength of preferences for individual variants, which may facilitate making the right decision considering the chances for implementation of individual scenarios.