Hesitant Preference Research Articles

Balancing clean and quality production goals: A multi-analytical approach to scrutinizing the influencing factors in China's manufacturing industry

Under the strategies of "high-quality development" and "dual carbon goals", the manufacturing industry in China is confronted with the simultaneous challenges of ensuring the production of high-quality goods and adopting environmentally friendly production practices. The integration of cleaner production and quality management has received widespread attention; however, existing research cannot comprehensively identify the interactive factors between quality management and cleaner production and analyze their mechanism of action. This research employed a multi-faceted approach involving meetings, interviews, and online data to access primary materials, and complemented by grounded theory and quality tool (5M1E), a total of 18 quality factors that exert a substantial impact on the attainment of clean production objectives were ultimately identified. To meticulously assess the interrelationships among factors, this study employed the interval value hesitant preference relationship DEMATEL-AISM technique. The findings of the study emphasize the significance of Employee Quality Skill Level (F1) and Production Environment Quality (F15) as critical quality factors that should be given priority and careful consideration by managers aiming for a cleaner production process. The results of this study have the potential to enhance the manufacturing industry's ability to predict and prevent quality and environmental issues, thereby ensuring the long-term sustainability of clean production and effective quality management.

An Interactive Consensus Model in Group Decision Making with Heterogeneous Hesitant Preference Relations

This paper proposes an interactive consensus reaching model in the group decision making for heterogeneous hesitant preference relations (i.e., hesitant fuzzy preference relations, hesitant multiplicative preference relations, hesitant fuzzy linguistic preference relations). First, the consistencies of three hesitant preference relations are defined, respectively. Then, based on their definitions, three optimization models are constructed to obtain the weight vector of alternatives, based on which an interactive consensus adjustment algorithm is established based on the direct consensus framework. This framework adopts feedback mechanism to facilitate the information correction of decision makers. After several rounds of adjustment, the decision results with satisfactory consensus level are achieved. Finally, the practicability and effectiveness of the model are illustrated through a case study of mine accident emergency decision making.

A Decision Making Approach Based on Hesitant Fuzzy Linguistic-Valued Credibility Reasoning

Credibility reasoning has attracted a lot of attention due to its distinguished power and efficiency in representing uncertainty and vagueness within the process of reasoning and decision making. Aiming at the problem of inaccurate credibility estimation in uncertainty reasoning and making experts to express hesitant preferences better in evaluation reasoning process, this paper introduces hesitant fuzzy linguistic term set into credibility uncertainty reasoning. First, we propose hesitant fuzzy linguistic-valued credibility (HLCF), and establish the knowledge representation model of the hesitant fuzzy linguistic-valued credibility. Then, in order to solve the problem of incomplete information in the evaluation reasoning process, an information complement algorithm based on maximum similarity is constructed. After that, the algorithms of single rule and multiple rules of parallel relationship of hesitant fuzzy linguistic-valued credibility are proposed to enrich the reasoning rule base and get more accurate reasoning results. The closeness degrees between the conclusions of each alternative after reasoning and the expected value are calculated, so as to select the most suitable alternative. Finally, a practical example which concerned the social risk analysis is given to illustrate the applicability and effectiveness of the proposed approach.

Correlation coefficients of dual hesitant fuzzy sets and their application in engineering management

Engineering management plays an important role in the socio-economic field, where complex and uncertain factors usually exist. Dual hesitant fuzzy sets (DHFSs) are powerful tools to denote the decision makers’ uncertain and hesitant preferences. Correlation measures and correlation coefficients are two important types of indices in decision making. This paper focuses on correlation measures and correlation coefficients for DHFSs and their application in engineering management. To do this, two dual hesitant fuzzy correlation coefficients are defined, and their properties are studied. Considering the situation where interactive characteristics among elements exist, two Shapley correlation coefficients are defined. When the weighting information is incompletely known, models for the optimal two-additive measures are built. Then, an algorithm to clustering analysis and decision making with incomplete weighting information and interactive characteristics are presented, respectively. Two corresponding examples about real estate investment and engineering cost management are offered to demonstrate the application of the approaches.

Multi-stage optimization model for hesitant qualitative decision making with hesitant fuzzy linguistic preference relations

Hesitant fuzzy linguistic preference relation (HFLPR) as a new preference relation is introduced to express the decision makers’ (DMs’) hesitant preference information for each pairwise comparison between different alternatives or criteria. In this paper, the priority vector and consistency of HFLPR are discussed based on a two-stage optimization and multiplicative consistency. Based on the original hesitant preference information, the multiplicative consistency index of an HFLPR is defined to measure the consistency level of the HFLPR. For an unacceptable multiplicative consistent HFLPR, a goal programming model, which is an integer optimization model, is developed to derive an acceptable, multiplicative, consistent HFLPR. According to probability sampling, a linguistic preference relation (LPR) with the best consistency level and an LPR with the worst consistency level with regard to an HFLPR are defined. Combining the two LPRs, a two-stage optimization framework is constructed to obtain the HFLPR’s priority vector, which considers the DM’s risk preference. A multi-stage optimization approach is proposed to solve decision-making problems by integrating the goal programming model and the two-stage optimization framework. Two real life problems are analyzed to show the feasibility of the proposed approach.

Consistency Checking and Improving for Interval-Valued Hesitant Preference Relations

Group decision making (GDM), which aims to obtain a sensible decision result with several decision makers, is a common occurrence in daily life. Since the uncertainty of the objects is a thorny issue in the process of GDM, it is important to eliminate uncertainty in order to achieve an optimal decision result. Considerations of some types of preference relations based on various fuzzy sets have been presented and investigated in previous studies; in this paper, we define the interval-valued hesitant multiplicative preference relation (IVHMPR) and the multiplicative consistency of IVHMPR. Based on these, we provide a detailed discussion on the connections between the interval-valued hesitant fuzzy preference relation (IVHFPR) and the IVHMPR. Then, we give a method to check the for unacceptable consistency of IVHFPR and IVHMPR, and improve them to make the consistency acceptable. Finally, an illustrative example of selecting the optimal treatment for a lung cancer patient is given to demonstrate our work in detail.

Dual Hesitant Fuzzy Set and Intuitionistic Fuzzy Ideal Based Computational Method for MCGDM Problem

In this article, the authors propose a computational method for multi criteria decision making problems using dual hesitant fuzzy information. In this study, the authors mention limitation of fuzzy ideals over a semi ring of positive integers and propose fuzzy ideal over a semi ring over subset of rationals. An intuitionistic fuzzy ideal of semi rings is also defined in this article which is used in idealizing aggregated dual hesitant group preference matrixes. The proposed approach appears in the form of simple computational algorithms. The main characteristic of the proposed approach is it considers the relationship between attributes, and so it takes into account relative preferences of attributes to find out the ranking order of attributes while other methods consider various attributes independently. An example of a supplier selection problem is undertaken to understand the implementation of the proposed computational approach based on MCGDM with dual hesitant information and ranking results compared with different methods.

Two regression methods for hesitant multiplicative preference relations with different consistencies

Multiplicative preference relation (MPR) is an efficient and widely used tool in describing the preferences of decision makers. The hesitant multiplicative preference relation (HMPR), as an extension of the MPR, is commonly used in collecting and representing all hesitant preferences and judgements of the decision makers. Considering that there usually appear some random or illogical preference degrees in the process of constructing the HMPRs, so it is necessary to derive a pithy preference relation (such as MPR) from the original HMPR through selecting the most proper and abandoning improper preference degrees, at the same time, the derived MPR is with the advantage of both terseness and the best consistency. Based on the multiplicative consistency measure of HMPRs, this paper proposes two regression methods to transform the HMPR into MPRs (called reduced MPRs). With the error analysis, the first proposed regression method provides some steps to not only extract the reduced MPRs from original HMPR but also calculate the consistencies of both the HMPR and the reduced MPRs. A case study reflects that the reduced MPR is with the highest consistency degree among those possible separated MPRs from HMPR. To apply this method to solve practical problems conveniently, a group decision-making procedure with hesitant multiplicative information is further given based on the first regression method. Time complexity comparison between our proposed method and an existing method indicates the effectiveness of our method. In addition, according to the weak consistency, we develop the second regression method and design an algorithm to obtain the reduced MPRs from the HMPR. Furthermore, we provide a method to check the weak consistency of the HMPR and repair the inconsistent one. Numerical examples verify that the second regression method proposed in this paper is an effective technique for checking the weak consistency and modifying the inconsistency HMPR to the one with weak consistency.

A consensus framework with different preference ordering structures and its applications in human resource selection

In complex decision making environment, since the attitudes, cognitions and knowledge backgrounds of the decision makers are quite different with each other, they may provide preference information with different preference ordering structures such as preference orderings, interval preference orderings and hesitant preference ordering sets. In order to obtain reliable decision results, we propose a consensus framework to help the decision makers to discuss with each other, share knowledge and information, and reach a consensus. Normally, the consensus framework consists of two parts: (1) selection process; (2) consensus process. In the selection process, we propose some aggregation operators to integrate the decision makers’ preference information with different preference ordering structures. Then, we apply the PROMETHEE II method to obtain the decision results as the standard of the group preferences. In the consensus process, we put forward the weighted ordering consensus degree to judge if the decision makers reach a consensus. Then, an interaction process is designed to improve the consensus degrees of the decision makers with the process of identification and adjustment. Finally, we illustrate the consensus framework with the human resource evaluation and selection problem of Hongqi Chain.

Additive consistency analysis and improvement for hesitant fuzzy preference relations

Hesitant fuzzy preference relation (HFPR) is an effective tool to elicit decision makers’ hesitant preference information over alternatives, and consistency analysis is of great importance for an HFPR since inconsistent judgments may result in unreasonable results. In this paper, the best additive consistency index, the worst additive consistency index and the average additive consistency index are defined to measure the consistency level of an HFPR. To improve the additive consistency of an HFPR, some mixed 0–1 linear programming models which aim to minimize the overall adjustment amount and the number of the elements that need to be adjusted are established. Moreover, the proposed models are extended to improve the additive consistency and impute the missing elements for incomplete HFPRs. Some numerical examples are presented to show the characteristics of the proposed models. The results demonstrate that the proposed models can improve the consistency of an HFPR effectively.

A consensus reaching model for hesitant information with different preference structures

Since the decision makers’ experience, cognitions and knowledge background are quite different from each other, sometimes the preference information they provided may be various and different. In order to help the decision makers share information and make more reliable decisions, we design a consensus reaching model which consists of two parts: 1) selection process; 2) consensus process. In this paper, we apply the hesitant fuzzy preference relations, the hesitant multiplicative preference relations and the hesitant preference orderings to express and integrate these complex preference information in a simple and convenient way. In the selection process, we propose a hybrid nonlinear programming model to process the information with different preference structures. This model overcomes the shortcomings of the traditional decision methods for hesitant information and obtains the decision results in a more accurate and reasonable way. In the consensus process, the consensus measure is put forward to judge whether the decision results are acceptable and identify which decision maker needs to modify his/her provided information. Then, an interaction mechanism is also proposed to help the decision makers exchange information and adjust the evaluation values. In the last, we apply the consensus reaching model to solve the hospital's trust evaluation problem and verify the stability of the model.

Fusion of heterogeneous incomplete hesitant preference relations in group decision making

AbstractIn this paper, we focus on the fusion of heterogeneous incomplete hesitant preference relations (including hesitant fuzzy preference relations and hesitant multiplicative preference relations) under group decision making settings. First, some simple formulae are developed to derive a priority weight vector from an incomplete hesitant fuzzy preference relation or an incomplete hesitant multiplicative preference relation based on the logarithmic least squares method. Based on the priority weight vector, an induced fuzzy or multiplicative preference relation can be derived for an incomplete hesitant preference relation. Moreover, the consistency indices of hesitant fuzzy preference relations and hesitant multiplicative preference relations are defined. Afterwards, an approach to group decision making based on incomplete hesitant fuzzy preference relations and incomplete hesitant multiplicative preference relations is developed to deal with group decision making problems with multiple decision organiz...

Some consistency measures of extended hesitant fuzzy linguistic preference relations

Linguistic preference relations (LPRs) enable decision makers to express preferences by pair-wise comparisons in qualitative setting. The fundamental aspect of LPRs is to measure the degrees of consistency when applying them in decision making. Extended hesitant fuzzy linguistic term sets (EHFLTSs) are a powerful tool for modeling uncertain linguistic information in group decision making. Based on which we first present the concept of extended hesitant fuzzy linguistic preference relations (EHFLPRs), and then develop the additive consistency measure and the weak consistency measure respectively. Furthermore, in order to solve the consistency problem by the perspective of graphs, two algorithms are proposed based on two kinds of predefined graphs, i.e., the hesitant preference graphs and the symmetric hesitant preference graph. The selective algorithm selects the arcs with the highest additive consistency level from the symmetric hesitant preference graph and constructs a LPR. While the broken circle algorithm removes the arcs from circular triads of the hesitant preference graph, it makes the EHFLPR with weak consistency and divides the EHFLPR into several possible LPRs satisfying weak consistency. This paper therefore explores a visible interpretation of consistency measures. The rationality of the proposed algorithms is verified by several examples, and some related issues are also discussed.

Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making

The hesitant fuzzy linguistic term sets (HFLTSs), which can be used to represent an expert’s hesitant preferences when assessing a linguistic variable, increase the flexibility of eliciting and representing linguistic information. The HFLTSs have attracted a lot of attention recently due to their distinguished power and efficiency in representing uncertainty and vagueness within the process of decision making. To enhance and extend the applicability of HFLTSs, this paper investigates and develops different types of distance and similarity measures for HFLTSs. The paper first proposes a family of distance and similarity measures between two HFLTSs. Then a variety of weighted or ordered weighted distance and similarity measures between two collections of HFLTSs are proposed and analyzed for discrete and continuous cases respectively. After that, the application of these measures to multi-criteria decision making problems is given. Based on the proposed distance and similarity measures, the satisfaction degrees for different alternatives are established and are then used to rank alternatives in multi-criteria decision making. Finally a practical example concerning the evaluation of the quality of movies is given to illustrate the applicability and advantage of the proposed approach and the differences between the proposed distance and similarity measures.

Interval-valued Hesitant Multiplicative Preference Relations and Their Application to Multi-criteria Decision Making

Aims: The aim of this paper is to investigate interval -valued hesitant multiplicative preference relations and their application to multi-criteria decision making. Study Design: Based on pseudo-multiplication, we define some basic operations for the interval-valued hesitant multiplicative sets (IVHMSs) an d develop several aggregation operators for aggregating the interval-valued hesitant multiplicati ve information. Some desired properties and special cases of the developed operators are also inv estigated. Furthermore, we present a new preference structure named as the interval-valued hesitant multiplicative preference relation (IVHMPR), each element of which is an IVHMS, denoting all the possible interval multiplicative preference values offered by the decision mak ers for a paired comparison of alternatives. Place and Duration of Study: Interval-valued hesitant fuzzy set (IVHFS), recently introduced by Chen et al., permits the membership degree of an eleme nt to a set to be represented as several possible interval values. However, it is noted that IVHFS us es 0.1‐0.9 scale, which is inconsistent with some practical problems (e.g. the la w of diminishing marginal utility in economics). Methodology: We use the unsymmetrical 1‐9 scale instead of the symmetrical 0.1‐0.9 scale to express the membership degree information in the IVHFS and intr oduce the concept of intervalvalued hesitant multiplicative set (IVHMS). Results: An approach for multi-criteria decision making based on the interval-valued hesitant multiplicative preference relations (IVHMPRs) is developed a nd some numerical examples are provided to illustrate the developed approach. Conclusion: We compare the IVHMPR with the interval-valued hesitant preference relation (IVHPR) and the interval multiplicative preference relat ion (IMPR), and show the effectiveness and practicality of the IVHMPR. Original Research Article

MANAGING HESITANT INFORMATION IN GDM PROBLEMS UNDER FUZZY AND MULTIPLICATIVE PREFERENCE RELATIONS

In the process of group decision making (GDM), preference relations (e.g. fuzzy preference relation and multiplicative preference relation) are very popular tools to express decision makers' preferences, especially when a set of alternatives (or criteria) are compared. However, most of the existing preference relations don't consider the hesitancy information, which allows the decision makers to provide all the possible values when comparing two alternatives (or criteria), and is a common situation in daily life. In this paper, we first define the concept of hesitant fuzzy preference relation and study its properties, based on which we give an approach to GDM. Motivated by the multiplicative preference relation and the hesitant fuzzy set, we introduce the hesitant multiplicative set and develop a series of hesitant multiplicative aggregation operators. Hesitant multiplicative preference relation is also defined to provide decision makers a very useful tool to express their hesitant preferences over alternatives, and then is applied to GDM. Additionally, an example is given to illustrate our results.

Interval-valued hesitant preference relations and their applications to group decision making

We introduce a new type of fuzzy preference structure, called interval-valued hesitant preference relations, to describe uncertain evaluation information in group decision making (GDM) processes. Moreover, it allows decision makers to offer all possible interval values that are not accounted for in current preference structure types when one compares two alternatives. We generalize the concept of hesitant fuzzy set (HFS) to that of interval-valued hesitant fuzzy set (IVHFS) in which the membership degrees of an element to a given set are not exactly defined, but denoted by several possible interval values. We give systematic aggregation operators to aggregate interval-valued hesitant fuzzy information. In addition, we develop an approach to GDM based on interval-valued hesitant preference relations in order to consider the differences of opinions between individual decision makers. Numerical examples are provided to illustrate the proposed approach.