Multi-granularity Linguistic Term Sets Research Articles

A simplified multi-granular linguistic term sets method

A simplified multi-granular linguistic term sets method

A New Decision-Making Framework for Site Selection of Electric Vehicle Charging Station With Heterogeneous Information and Multigranular Linguistic Terms

As a key technology to perform sustainable development in transportation, electric vehicles have been largely welcomed due to their advantages in energy savings and low carbon emission. The main step in promoting these vehicles is the selection of the appropriate electric vehicle charging station (EVCS) site. EVCS site selection is a laborious task because it involves a series of conflicting quantitative and qualitative criteria from several dimensions. The quantitative criteria are usually expressed by numerical data, while qualitative criteria are commonly represented by linguistic terms. Furthermore, the linguistic terms generated by different decision makers are usually defined on multigranular linguistic term sets. In this article, we present a new decision-making framework to select sustainable EVCS sites within the context of heterogeneous information and multigranular linguistic terms. First, three information transformation mechanisms are defined to unify the heterogeneous information and multigranular linguistic terms into interval-valued belief structures. Afterward, shadowed sets theory is utilized to reflect the personalized individual semantics of linguistic terms. Then, with the aid of the evidential reasoning (ER) algorithm, a new information fusion method is proposed to generate the interval-valued expected utilities of alternatives. Subsequently, an improved minimax regret approach is developed to compare and rank the interval-valued expected utilities. The proposed decision-making framework is then implemented to solve a case study for EVCS site selection. Further analysis and comparisons with other methods are also conducted to show the applicability and feasibility of the current proposal.

Supplier selection with different risk preferences and attribute sets: An innovative study based on generalized linguistic term sets

Selecting the optimal supplier is crucial to the management of the company’s supply chain, which has received widespread attention from academia and business circles. Generally, a variety of suppliers and multiple attributes are usually involved in this selection proceeding which can be comparatively regarded as a linguistic multiple attribute group decision making (MAGDM) problem. However, common linguistic MAGDM problems may not take the following characteristics into consideration. Due to the limited knowledge, distinctive interests, and different semantic value expectations of decision makers (DMs), it is necessary for them to consider their diverse risk preferences and use multi-granular linguistic term sets (LTSs) to assess suppliers on individual attribute sets independently. Meanwhile, the complex decision environment may have influences on the integrity of the attribute weight information, such that it is always incompletely known. To deal with the afore-mentioned situations, this paper presents a procedure based on risk preferences and several attribute sets with incomplete weight information for choosing the desirable supplier. Firstly, a new multi-granular fuzzy linguistic transformation model is constructed to normalize linguistic domains of multi-granular generalized linguistic term sets (GLTSs). Multi-granular GLTSs are introduced to describe semantic values of multi-granular LTSs given by DMs with risk preferences. Secondly, according to the maximizing deviation method with incomplete attribute weight information, an optimization model is also established to determine attribute weight vectors of individual attribute sets. Thirdly, a novel method that comprises of the aforesaid models is presented to handle supplier selection problems with risk preferences and several attribute sets. Finally, an illustrative example on supplier selection and comparative analyses are provided to clarify the validity and feasibility of our proposed method. Significantly, the initiation of the proposed method and its application could afford to the theoretical development of linguistic MAGDM, as well as the practical expansion in the domain of supplier selection.

A Novel Group Decision-Making Method Based on Generalized Distance Measures of PLTSs on E-Commerce Shopping

In multiattribute group decision-making (MAGDM), due to quantity, fuzziness, and complexity of evaluation linguistic information on commodities, traditional distance measures need to be extended to the integration of evaluation information under a multigranular probabilistic linguistic environment. A more reasonable method is proposed to deal with the missing value in the evaluation information. On the basis of the generalized distance measures and filling in the missing evaluation information, some novel distance measures between two multigranular probabilistic linguistic term sets (PLTSs) are presented in this paper. Based on these distance measures, three extended decision-making (DM) algorithms based on TOPSIS, the extended TOPSIS, and VIKOR are proposed, which are MGPL-TOPSIS, MGPL-ETOPSIS, and MGPL-VIKOR, respectively. The case analyses on purchasing a car are provided to illustrate the application of the extended multiattribute group decision-making (MAGDM) algorithms. Then, sensitivity analyses based on PT are proposed as well. In particular, the extended TOPSIS method is presented. These results demonstrate the novelty, feasibility, and rationality of the distance measures between two multigranular PLTSs proposed in this paper.

Consensus reaching for group decision making with multi-granular unbalanced linguistic information: A bounded confidence and minimum adjustment-based approach

In group decision making problems, there exist the situations that decision makers may use unbalanced linguistic term sets that are not uniformly and symmetrically distributed to provide their linguistic assessments over alternatives. Moreover, due to the difference in knowledge and culture backgrounds, it is also possible that multi-granular linguistic term sets may also be used by decision makers. How to manage multi-granular unbalanced linguistic information in consensus-based group decision making has becoming an important topic in linguistic decision making. In this paper, we first revise Herrera’s unbalanced linguistic term sets and propose a simplified linguistic computational model to fuse multi-granular unbalanced linguistic terms. Afterwards, for multi-criteria group decision making problems with multi-granular unbalanced linguistic information, we develop two optimization models to generate adjustment advice for decision makers who have to change his/her opinions in consensus reaching process, which consider both the bounded confidence levels and minimum adjustment of decision makers’ linguistic assessments. Moreover, an algorithm is further proposed to help decision makers reach consensus in group decision making. Eventually, an application example for ERP system supplier selection and some simulation results are presented to illustrate and justify the consensus reaching algorithm.

An interpretable knowledge-based decision support system and its applications in pregnancy diagnosis

This paper aims to propose an interpretable knowledge-based decision support system (IKBDSS) that will assist physicians to predict the risk level of a disease. Our system enables to integrate both historical cases extracted from database and opinions provided by different experts in order to set up a medical knowledge base and provide relevant advises by inferring from the knowledge base. To present various experts’ opinions, the Multi-granularity Linguistic Term Sets (MLTS) model is used to address the ambiguity and intangibility of knowledge. Our work mainly focuses on knowledge acquisition, similarity degree calculation and consistency checking process. It is worth mentioning that a criterion weights calculation method is introduced to objectively obtain the weights based on knowledge from experts, rather than subjectively predefined.The developed system leads to a better performance in specificity, sensitivity and F1 score compared to other methods in the literature. To conclude, our work contributes to: (1) The development of a medical decision support system to combine clinical records and domain knowledge to predict diagnosis. (2) The decision-making process ensures interpretability, which increases the reliability of our system in terms of being a decision supporter. (3) The criterion weights are calculated based on the professional knowledge presented in MLTS form, and this process improves the capacity of providing diagnostic recommendations.

A Fuzzy Best-Worst Multi-Criteria Group Decision-Making Method

Fuzzy best-worst method (BWM) has emerged as an efficient choice because of its comparison consistency to model the real-life and the consideration of fuzziness and uncertainties of decision-makers (DMs). However, how to extend the fuzzy BWM to group decision-making (GDM) environment has become an important topic because there are usually more than one decision-maker. For the GDM, decision makers may use different concepts to establish their individual assessment information due to the difference of cultural background and priori knowledge. To overcome this challenge, this article proposed a novel fuzzy best-worst multi-criteria group decision-making method to solve the GDM problem with multi-granular linguistic approach, which is an effective and promising technique to tackle this issue. In the proposed method, the selectable multi-granularity linguistic term sets (LTS) are firstly provided for experts to expressed their individual assessment information. Then, the improved fuzzy BWM is employed to calculate the weights of criteria with the form of fuzzy numbers. In current several studies using the BWM for group decision-making, only two unified best and worst criteria are given, which cannot reflect the evaluation of the best and worst criteria by different experts, resulting in the omission of information. Moreover, the difference between the best and worst criteria initially given and the experts’ ideas will cause the experts to be inaccurate in the comparison of each criterion. Therefore, in this article, in order not to omit too much information, each expert will determine the best and the worst criteria. Each expert’s assessment information which is based on his/her best and worst criteria is integrated into two vectors. What’s more, an improved input-based consistency measurement is proposed, which can provide the DMs with a clear guideline on the revision of the inconsistent judgement(s). Finally, two examples are given to illustrate the effectiveness and applicability of the proposed method.

Managing Multigranular Unbalanced Hesitant Fuzzy Linguistic Information in Multiattribute Large-Scale Group Decision Making: A Linguistic Distribution-Based Approach

With the increase of technological and societal demands, more and more decision makers are involved in the process of group decision making, which is called large-scale group decision making (LGDM). For an LGDM problem with linguistic information, it is common that different decision makers tend to provide linguistic assessments defined on multigranular linguistic term sets due to the difference in knowledge and culture background, and that hesitant fuzzy linguistic term sets (HFLTSs) are used by decision makers to model the hesitancy of their assessments. This article proposes first an algorithm to represent a linguistic distribution assessment (LDA) using a hesitant linguistic distribution (HLD). Two other algorithms are then proposed to transform an unbalanced HFLTS into a balanced LDA and to transform a balanced LDA into an unbalanced LDA, respectively. An approach is then proposed to deal with multiattribute LGDM problems with multigranular unbalanced hesitant fuzzy linguistic information based on these algorithms. In the proposed approach, all unbalanced hesitant fuzzy linguistic information is transformed into LDAs defined on a balanced linguistic term set, and then an LDA-based clustering algorithm is devised to cluster decision makers. Based on the clustering result, decision makers’ linguistic distribution decision matrices are further fused to obtain collective assessments of alternatives. In order to provide easy-to-understand linguistic results for decision makers, all LDAs of alternatives are represented by HLDs defined on each decision maker's initial linguistic term set. Finally, an example for the selection of subway lines is used to demonstrate the proposed approach.

Acquiring and Sharing Tacit Knowledge in Failure Diagnosis Analysis Using Intuitionistic and Pythagorean Assessments

Nowadays knowledge management has received a considerable attention from both academics and industrial sectors, and expert knowledge is recognized as the most important resource of enterprises, particularly in the knowledge-intensive organizations. Dealing with knowledge creation, transfer, and utilization is increasingly critical for the long-term sustainable competitive advantage and success of any organization. Thus, a lot of efforts have been required from companies and researchers in developing and supporting knowledge management in different organizations. In industrial sectors as the highly competitive environment, capturing and disseminating of tacit knowledge are significant to an organization’s success with the development of knowledge-based systems. Safety and reliability analysis is an important issue to prevent an event which may be the occurrence of catastrophic accident in process industries. In this context, conventional safety and reliability assessment techniques like fault tree analysis have been widely used in this regard; however, in practical knowledge acquisition process, domain experts tend to express their judgments using multi-granularity linguistic term sets, and there usually exists uncertain and incomplete information since expert knowledge is experience-based and tacit. In addition, although the technical capabilities of expert systems based on fuzzy set theory are expanding, they still fall short of meeting the increasingly complex knowledge demands and still suffer in subjective uncertainty processing and dynamic structure representation which are important in risk assessment procedure. In this paper, a new framework based on 2-tuple intuitionistic fuzzy numbers, Pythagorean fuzzy sets, and Bayesian network mechanism is proposed to evaluate system reliability, to deal with mentioned drawbacks, and to recognize the most critical system components which affect the system reliability.

Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment

The sustainability challenge is increasingly driving the adoption of supercritical water gasification (SCWG) technology to ensure the elimination and recovery of pollution produced by sewage sludge treatment (SST). Risk evaluation by failure mode and effects analysis (FMEA) plays a crucial role in guaranteeing the reliability and safety of SCWG systems. However, some limitations in existing FMEA methods need to be ameliorated. Multiple risk factors are involved in prioritizing risk levels for failure modes in SCWG systems, it is essential a multiple criteria decision making (MCDM) process, in which overall assessments of failure modes should be provided according to their performances from several points during a system operation period. Due to differences in knowledge backgrounds and experiences, FMEA team members prefer to utilize multi-granular linguistic term sets to express their assessments of system risk. A hybrid risk evaluation model by FMEA is exploited with multi-granular linguistic distribution assessments to suit practical case. Best-worst and maximizing derivation methods are adopted to determine subjective and objective combined weights for distinguishing the importance of risk factors. Complex proportional assessment method is used to prioritize failure modes for explicitly and effectively reflecting the risk level of each failure mode. The proposed model is applied in a practical case of an SCWG system used in SST. Results derived from comparative and sensitivity analyses fully demonstrate the reliability and validity of the model.

A novel multi-criteria decision making method for assessing health-care waste treatment technologies based on D numbers

Health-care waste (HCW) management is a complex problem influenced by many aspects, such as, economic, environment, technical, social, etc. A selection of the best treatment technology for HCW management can be regarded as a complex multi-criteria decision making problem where a number of alternatives and multiple evaluation criteria are need to be considered. In addition, decision makers often express their personal assessments by using multi-granularity linguistic term sets. Due to the involvement of human judgment, various uncertainties are introduced in the HCW process. One critical issue of the HCW treatment technology selection is the representation and handling of uncertain information. In response, a novel multi-criteria decision making method is proposed for the HCW treatment technology selection problem based on an effective representation model of uncertain information, called D numbers. In the proposal, the assessment results of HCW treatment are expressed and modeledby D numbers. It provides a new framework for the HCW treatment technology selection problem in which it was effective and feasible to handle MCDM problems under various uncertainties environment. Comparing with the existing method, the proposed method is clear and concise. Finally, an empirical case study in Shanghai, China is illustrated to validate the feasibility and applicability of the proposed method. As shown in the results, the proposed method selects steam sterilization as the optimal technology to deal with the health-care wastes which is consistent with the existing work where fewer pollutants are discharged and non-hazardous residues are produced so that it decreases more impacts on the environment and public health. The experimental results demonstrate that the proposed method can handle the HCW treatment technology selection problem effectively under complex and uncertain environments.

Acquiring and Sharing Tacit Knowledge Based on Interval 2-Tuple Linguistic Assessments and Extended Fuzzy Petri Nets

In the highly competitive environment, capturing and disseminating of tacit knowledge are significant to an organization’s success with the development of knowledge-based systems. However, in practical knowledge acquisition process, domain experts tend to express their judgments using multigranularity linguistic term sets, and there usually exists uncertain and incomplete information since expert knowledge is experience-based and tacit. In addition, although the technical capabilities of expert systems based on fuzzy Petri nets (FPNs) are expanding, they still fall short of meeting the increasingly complex knowledge demands. Therefore, this paper develops a theoretical model based on linguistic interval 2-tuples and interval-valued intuitionistic FPNs (IVIFPNs) for acquiring and representing tacit knowledge to increase and sustain the competitive advantages of knowledge intensive organizations. An empirical case study in medical practice is provided to demonstrate the application and feasibility of the proposed model, and the results show that it can well capture experts’ tacit knowledge and reuse the acquired knowledge productively.

Health-Care Waste Treatment Technology Selection Using the Interval 2-Tuple Induced TOPSIS Method.

Health-care waste (HCW) management is a major challenge for municipalities, particularly in the cities of developing nations. Selecting the best treatment technology for HCW can be regarded as a complex multi-criteria decision making (MCDM) issue involving a number of alternatives and multiple evaluation criteria. In addition, decision makers tend to express their personal assessments via multi-granularity linguistic term sets because of different backgrounds and knowledge, some of which may be imprecise, uncertain and incomplete. Therefore, the main objective of this study is to propose a new hybrid decision making approach combining interval 2-tuple induced distance operators with the technique for order preference by similarity to an ideal solution (TOPSIS) for tackling HCW treatment technology selection problems with linguistic information. The proposed interval 2-tuple induced TOPSIS (ITI-TOPSIS) can not only model the uncertainty and diversity of the assessment information given by decision makers, but also reflect the complex attitudinal characters of decision makers and provide much more complete information for the selection of the optimum disposal alternative. Finally, an empirical example in Shanghai, China is provided to illustrate the proposed decision making method, and results show that the ITI-TOPSIS proposed in this paper can solve the problem of HCW treatment technology selection effectively.

Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching

In group decision making (GDM) dealing with Computing with Words (CW) has been highlighted the importance of the statement, words mean different things for different people, because of its influence in the final decision. Different proposals that either grouping such different meanings (uncertainty) to provide one representation for all people or use multi-granular linguistic term sets with the semantics of each granularity, have been developed and applied in the specialized literature. Despite these models are quite useful they do not model individually yet the different meanings of each person when he/she elicits linguistic information. Hence, in this paper a personalized individual semantics (PIS) model is proposed to personalize individual semantics by means of an interval numerical scale and the 2-tuple linguistic model. Specifically, a consistency-driven optimization-based model to obtain and represent the PIS is introduced. A new CW framework based on the 2-tuple linguistic model is then defined, such a CW framework allows us to deal with PIS to facilitate CW keeping the idea that words mean different things to different people. In order to justify the feasibility and validity of the PIS model, it is applied to solve linguistic GDM problems with a consensus reaching process.

Managing multi-granularity linguistic information in qualitative group decision making: an overview

Linguistic term sets are usually preferred to represent evaluations and preferences in qualitative group decision making (QGDM). Due to the different backgrounds and levels of knowledge of experts, linguistic term sets with different cardinality and/or semantics are used. Hence, it is vital to manage these linguistic terms from distinct information sources in QGDM. In this paper, we present a comprehensive review on relative developments of multi-granularity linguistic term sets by several perspectives, such as the transformation techniques, the aggregation functions, decision making processes and the applications. Finally, some possible directions for future research are pointed out as well.

The consistency measures of multi-granularity linguistic group decision making

When different experts have different uncertainty degrees on the phenomenon, several linguistic term sets with different granularity of uncertainty are necessary. In this paper, we deal with the consistency problem with multi-granularity linguistic term sets applied to group decision making. Firstly, we develop a transformation model to maintain the uncertainty degrees of different granularity associated with each expert. We use the uncertain linguistic variables as the unified form. Some computational rules about uncertain linguistic variables are given. Then consistency index of linguistic preference relations based on the distance of a multi-granularity linguistic preference relation is defined. Chi-square statistic is used to establish the consistency thresholds. Finally two numerical examples are used to show the process and effects of our new proposed method.

A fuzzy multi-granularity linguistic approach under group decision-making for the evaluation of supply chain flexibility

With global competition rapidly intensifying and shifting to the supply chain level, supply chain flexibility (SCF) is becoming increasingly important. Despite its importance, the literature on SCF is limited. In group decision-making problems, the experts have their different backgrounds, experience, and knowledge. The experts use different linguistic term sets with different cardinalities (multi-granularity linguistic term sets) to express individual assessment information. Therefore, the objective of this paper is to develop a new fuzzy fusion method to deal with the heterogeneous group of experts’ problem with multi-granularity linguistic assessment information, and used to improve the SCF evaluation process. The results of this study are more objective and unbiased for two reasons. First, the results are generated by heterogeneous group decision-making with multi-granularity linguistic assessment information. Second, the fuzzy linguistic approach has more advantage to preserve no loss of information. Additionally, an example using a case study is used to illustrate the availability of the proposed method. We also show that the linguistic assessment of SCF is reasonable.

Consensus-Based Group Decision Making Under Multi-granular Unbalanced 2-Tuple Linguistic Preference Relations

In group decision making (GDM) situations, it is quite natural that the decision makers who may have different background and knowledge will provide their preferences by means of different linguistic term sets. Specifically, multi-granular linguistic term sets that are not uniformly and symmetrically distributed will be employed. To deal with this type of GDM problems, this paper proposes a consensus-based GDM model by using two existing 2-tuple linguistic representation models (i.e., the Herrera and Martinez model and the Wang and Hao model), which we called the GDM model based on multi-granular unbalanced 2-tuple linguistic preference relations. First, the framework of the GDM model with multi-granular unbalanced 2-tuple linguistic preference relations is proposed. Then, the transformation function is obtained to relate multi-granular unbalanced linguistic preference relations with uniform balanced linguistic preference relations. Further, a consensus model is presented to help the decision makers reach a consensus. This consensus model not only provides a new way to simultaneously manage individual consistency and group consensus in a linear programming model, but also minimizes information loss (or consensus cost) when reaching the established consensus level. Finally, an example is given to illustrate the feasibility and validity of the proposed model.

A Novel Approach for FMEA: Combination of Interval 2-Tuple Linguistic Variables and Gray Relational Analysis

Failure mode and effect analysis (FMEA) is a powerful tool for defining, identifying, and eliminating potential failures from the system, design, process, or service before they reach the customer. Since its appearance, FMEA has been extensively used in a wide range of industries. However, the conventional risk priority number (RPN) method has been criticized for having a number of drawbacks. In addition, FMEA is a group decision behavior and generally performed by a cross-functional team. Multiple experts tend to express their judgments on the failure modes by using multigranularity linguistic term sets, and there usually exists uncertain and incomplete assessment information. In this paper, we present a novel FMEA approach combining interval 2-tuple linguistic variables with gray relational analysis to capture FMEA team members’ diversity opinions and improve the effectiveness of the traditional FMEA. An empirical example of a C-arm X-ray machine is given to illustrate the potential applications and benefits of the proposed approach. Copyright © 2014 John Wiley & Sons, Ltd.

An interval 2-tuple linguistic MCDM method for robot evaluation and selection

Nowadays selection of an optimal robot has become a challenging task for manufacturers with the increment of production demands and availability of more different robot models. Robot selection for a particular industrial application can be viewed as a complicated multi-criteria decision-making problem which requires consideration of a number of alternative robots and conflicting subjective and objective criteria. Furthermore, decision-makers tend to use multigranularity linguistic term sets to express their assessments on the subjective criteria, and there usually exists uncertain and incomplete assessment information. In this paper, an interval 2-tuple linguistic TOPSIS (ITL-TOPSIS) method is proposed to handle the robot selection problem under uncertain and incomplete information environment. This method considers both subjective judgements and objective information in real-life applications, and models the uncertainty and diversity of decision-makers’ assessments using interval 2-tuple linguistic variables. An example is cited for demonstrating the feasibility and practicability of the proposed method, and results show that the ITL-TOPSIS is an effective decision-making tool for robot evaluation and selection with uncertain and incomplete information.