Selection third-party logistics service providers in supply chain finance by a hesitant fuzzy linguistic combined compromise solution method

In supply chain finance, the multiple criteria decision making (MCDM) problem of selecting a suitable third-party logistics (3PL) service supplier is of great significance to financial institutions. A suitable 3PL service supplier can not only help financial institutions carry out the supply chain finance business, but also can replace financial institutions to supervise the operation of a target financing supply chain, thus reducing the operational risks of financial institutions. As a useful MCDM method, the combined compromise solution (CoCoSo) method mainly combines a compromise decision algorithm with an aggregation strategy to obtain a compromise solution. This study extends the CoCoSo method to hesitant fuzzy linguistic environment to solve the multi-expert MCDM problem of the 3PL service supplier selection. Target criteria whose values are in linguistic forms are considered in the process of normalizing the decision matrix. A new integration approach with respect to subordinate compromise scores is introduced, and the subjective and objective weights of criteria are considered simultaneously in this extended process to avoid one-sidedness of criterion weights. A case study about the 3PL service supplier selection is given, in which the sensitivity analysis and comparative analysis are provided to highlight the advantages of the proposed method

An Intuitionistic Multiplicative ORESTE Method for Patients’ Prioritization of Hospitalization

The tension brought about by sickbeds is a common and intractable issue in public hospitals in China due to the large population. Assigning the order of hospitalization of patients is difficult because of complex patient information such as disease type, emergency degree, and severity. It is critical to rank the patients taking full account of various factors. However, most of the evaluation criteria for hospitalization are qualitative, and the classical ranking method cannot derive the detailed relations between patients based on these criteria. Motivated by this, a comprehensive multiple criteria decision making method named the intuitionistic multiplicative ORESTE (organísation, rangement et Synthèse dedonnées relarionnelles, in French) was proposed to handle the problem. The subjective and objective weights of criteria were considered in the proposed method. To do so, first, considering the vagueness of human perceptions towards the alternatives, an intuitionistic multiplicative preference relation model is applied to represent the experts’ preferences over the pairwise alternatives with respect to the predetermined criteria. Then, a correlation coefficient-based weight determining method is developed to derive the objective weights of criteria. This method can overcome the biased results caused by highly-related criteria. Afterwards, we improved the general ranking method, ORESTE, by introducing a new score function which considers both the subjective and objective weights of criteria. An intuitionistic multiplicative ORESTE method was then developed and further highlighted by a case study concerning the patients’ prioritization.The work was supported by the National Natural Science Foundation of China (71501135, 71771156, 71532007), the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23), and the Grant from the FEDER funds (No. TIN2016-75850-R)

Pension service institution selection by a personalized quantifier-based MACONT method

With the emergence of a variety of pension service institutions, how to choose a suitable institution has become a strategic decision-making problem faced by pension service demanders. To solve this problem, this study identifies key evaluation criteria of pension service institutions through the analysis of the relevant literature. Then, this study proposes a mixed aggregation by comprehensive normalization technique (MACONT) with a personalized quantifier to select pension service institutions, where the personalized qualifier with cubic spline interpolation is used to derive the position weights of criteria, and the MACONT is improved to determine the ranking of alternatives. A case study about the selection of pension service institutions is provided to verify the feasibility of the proposed model. It is found that the proposed method is effective in dealing with heterogeneous evaluation information, and the personalized quantifiers can be combined with MACONT methods to obtain an optimal solution associated with the attitude of pension service demanders. The identified key evaluation criteria are not only significant for pension service demanders, but also conducive to the further improvement of property management related to pension services

Supplier selection for housing development by an integrated method with interval rough boundaries

Residential whole-decoration is an important initiative for housing industrialization in China. Selecting the most suitable component supplier for housing development is of great significance for both property developers and buyers in the implementation of such a strategy. To address such a problem, this study uses hesitant fuzzy linguistic term sets to express the inaccurate judgments of individuals and then introduces a novel probability aggregation approach based on interval rough boundaries to enable a realistic presentation of the collective evaluations of a group. Then, we propose a hybrid multi-expert multiple criteria decision-making model by integrating the Best Worst Method (BWM) and Combined Compromise Solution (CoCoSo) method based on the interval rough boundaries. A case study about the supplier selection for housing development is carried out, which demonstrates the feasibility and applicability of our proposed hybrid model. A comparison study is also performed to further validate the robustness of the model

Green Logistic Provider Selection with a Hesitant Fuzzy Linguistic Thermodynamic Method Integrating Cumulative Prospect Theory and PROMETHEE

In the process of evaluating the green levels of cold-chain logistics providers, experts may hesitate between several linguistic terms rather than give precise values over the alternatives. Due to the potential profit and risk of business decisions, decision-making information is often based on experts’ expectations of programs and is expressed as hesitant fuzzy linguistic terms. The consistency of evaluation information of an alternative can reflect the clarity of the alternative in the mind of experts and its own controversy. This paper proposes a method to use the value transfer function in the cumulative prospect theory to convert the original hesitant fuzzy linguistic terms into evaluation information based on reference points. We also introduce the parameters related to the disorder of the systemin the hesitant fuzzy thermodynamic method to describe the quantity and quality characteristics of the alternatives. In these kinds of multi-criteria decision-making problems, the weights of criteria are of great importance for decision-making results. Considering the conflicting cases among criteria, the weights were obtained by utilizing the PROMETHEE method. An illustrative example concerning green logistics provider selection was operated to show the practicability of the proposed method.The work was supported in part by the National Natural Science Foundation of China (Nos. 71501135, 71771156, and 71771153), the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23), and the Scientific Research Foundation for Scholars at Sichuan University (No. YJ201535)

Interval-valued 2-tuple hesitant fuzzy linguistic term set and its application in multiple attribute decision making

[EN] The hesitant fuzzy linguistic term sets can retain the completeness of linguistic information elicitation by assigning a set of possible linguistic terms to a qualitative variable. However, sometimes experts cannot make sure that the objects attain these possible linguistic terms but only provide the degrees of confidence to express their hesitant cognition. Given that the interval numbers can denote the possible membership degrees that an object belongs to a set, it is suitable and convenient to provide an interval-valued index to measure the degree of a linguistic variable to a given hesitant fuzzy linguistic term set. Inspired by this idea, we introduce the concept of interval-valued 2-tuple hesitant fuzzy linguistic term set (IV2THFLTS) based on the interval number and the hesitant fuzzy linguistic term set. Then, we define some interval-valued 2-tuple hesitant fuzzy linguistic aggregation operators. Afterwards, to overcome the instability of subjective weights, we propose a method to compute the weights of attributes. For the convenience of application, a method is given to solve the multiple attribute decision making problems with IV2THFLTSs. Finally, a case study is carried out to validate the proposed method, and some comparisons with other methods are given to show the advantages of the proposed method.The work was supported in part by the National Natural Science Foundation of China (Nos. 71501135, 71771156), the China Postdoctoral Science Foundation (2016T90863, 2016M602698), the Fundamental Research Funds for the central Universities (No. YJ201535), and the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23).Si, G.; Liao, H.; Yu, D.; Llopis Albert, C. (2018). Interval-valued 2-tuple hesitant fuzzy linguistic term set and its application in multiple attribute decision making. Journal of Intelligent & Fuzzy Systems. 34(6):4225-4236. https://doi.org/10.3233/JIFS-171967S4225423634

Hesitant Fuzzy Linguistic Analytic Hierarchical Process With Prioritization, Consistency Checking, and Inconsistency Repairing

Analytic hierarchy process (AHP), as one of the most important methods to tackle multiple criteria decision-making problems, has achieved much success over the past several decades. Given that linguistic expressions are much closer than numerical values or single linguistic terms to a human way of thinking and cognition, this paper investigates the AHP with comparative linguistic expressions. After providing the snapshot of classical AHP and its fuzzy extensions, we propose the framework of hesitant fuzzy linguistic AHP, which shows how to yield a decision for qualitative decision-making problems with complex linguistic expressions. First, the comparative linguistic expressions over criteria or alternatives are transformed into hesitant fuzzy linguistic elements and then the hesitant fuzzy linguistic preference relations (HFLPRs) are constructed. Considering that HFLPRs may be inconsistent, we conduct consistency checking and improving processes after obtaining priorities from the HFLPRs based on a linear programming method. Regarding the consistency-improving process, we develop a new way to establish a perfectly consistent HFLPR. The procedure of the hesitant fuzzy linguistic AHP is given in stepwise. Finally, a numerical example concerning the used-car management in a lemon market is given to illustrate the ef ciency of the proposed hesitant fuzzy linguistic AHP method.This work was supported in part by the National Natural Science Foundation of China under Grant 71771156, in part by the 2019 Sichuan Planning Project of Social Science under Grant SC18A007, in part by the 2019 Soft Science Project of Sichuan Science and Technology Department under Grant 2019JDR0141, and in part by the Project of Innovation at Sichuan University under Grant 2018hhs-43

An integrated QFD and FMEA approach to identify risky components of products

Identifying risk components is crucial to improve product quality. Failure mode and effects analysis as a useful risk assessment method has become a prevalent application in product design. However, the critical data, which contain failure causality relationships (FCRs) between failure modes, importance correlations among risk factors, and customer requirements of the product component, are not considered. This study develops an integrated approach for identifying risky components considering customer requirements and FCRs. First, a quality function deployment is established to characterize the customer requirements under fuzzy assessment semantics. Second, the FCRs between and within the product components are characterized by a directed network model. In this network, the failure modes are modelled as vertices, and the causality relationships between the failure modes are modelled as directed edges. The values of the directed edges are characterized by weighted risk priority numbers, and the weight of risk factors is optimized by a nonlinear programming model. Then, the interactive relationships among failure modes between and within product components are characterized by the internal failure effect and external failure effect. Finally, a real-world case application of wheel loader is conducted to demonstrate the validity and feasibility of the proposed approach. The results have shown that the proposed method is more effective in identifying risk components

Fuzzy Group Decision Making for Influence-Aware Recommendations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Group Recommender Systems are special kinds of Recommender Systems aimed at suggesting items to groups rather than individuals taking into account, at the same time, the preferences of all (or the majority of) members. Most existing models build recommendations for a group by aggregating the preferences for their members without taking into account social aspects like user personality and interpersonal trust, which are capable of affecting the item selection process during interactions. To consider such important factors, we propose in this paper a novel approach to group recommendations based on fuzzy influence-aware models for Group Decision Making. The proposed model calculates the influence strength between group members from the available information on their interpersonal trust and personality traits (possibly estimated from social networks). The estimated influence network is then used to complete and evolve the preferences of group members, initially calculated with standard recommendation algorithms, toward a shared set of group recommendations, simulating in this way the effects of influence on opinion change during social interactions. The proposed model has been experimented and compared with related works