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Mathematics
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Fuzzy Group Decision Making
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Fuzzy Group Decision Making

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: closed (17 July 2022) | Viewed by 10005

Special Issue Editor

Prof. Dr. Tin-Chih Toly Chen

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu 30010, Taiwan
Interests: ambient intelligence; ergonomics; cloud and virtual manufacturing; applied soft computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fuzzy group decision making is an innovative application of fuzzy collaborative intelligence to decision making. The philosophy of fuzzy collaborative intelligence is to analyze a problem from diverse perspectives to increase the chance that no relevant aspects of the problem will be ignored. In a fuzzy group decision-making system, some decision makers (or decision-making units) with various backgrounds attempt to make a decision collaboratively. Since they have different knowledge and points of view, they may use various fuzzy decision-making methods to analyze the problem and make their choices. The key to such a system is that these decision makers share and exchange their observations, settings, experiences, and knowledge with each other when making a decision. This feature makes the fuzzy group decision-making system distinct from the ensemble of multiple fuzzy decision-making systems. Fuzzy group decision-making methods can be divided into two categories: anterior-aggregation fuzzy group decision-making methods, and posterior-aggregation fuzzy group decision-making methods. The former aggregates decision makers’ judgments before making a decision jointly; the latter aggregates decision makers’ decisions to look for a consensus. In either category, both the collaboration among decision makers and the aggregation of decision makers’ judgments (or decisions) are critical issues. In the context of the COVID-19 pandemic, fuzzy group decision making is particularly meaningful because decision makers may not be able to gather in the same place to make a decision jointly. This Special Issue aims to provide technical details on advances in fuzzy group decision making, including methodology, collaboration and aggregation mechanisms, system architectures, and applications. These details should be of great interest to researchers in decision making, artificial intelligence, soft computing, operations management, and information management, as well as practicing managers and engineers.

Prof. Dr. Tin-Chih Toly Chen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fuzzy decision tree
  • fuzzy goal programming
  • fuzzy OWA
  • fuzzy AHP and fuzzy ANP
  • fuzzy MAUT
  • fuzzy TOPSIS
  • fuzzy ELECTRE
  • fuzzy MACBETH
  • fuzzy PROMETHEE
  • collaboration mechanism
  • format of information granule
  • communication mechanism (protocol)
  • quality of collaboration assessment
  • autonomous agent
  • aggregation mechanism

Published Papers (5 papers)

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Research

14 pages, 291 KiB  
Article
The Fuzzy Complex Linear Systems Based on a New Representation of Fuzzy Complex Numbers
by Zhiyong Xiao and Zengtai Gong
Mathematics 2022, 10(15), 2822; https://doi.org/10.3390/math10152822 - 08 Aug 2022
Cited by 1 | Viewed by 1158
Abstract
Since the product of complex numbers and rectangular fuzzy complex numbers (RFCN) is not necessarily a RFCN in the former fuzzy complex linear system (FCLS), the scalar multiplication and addition operations of complex numbers and fuzzy complex numbers (FCN) based on a new [...] Read more.
Since the product of complex numbers and rectangular fuzzy complex numbers (RFCN) is not necessarily a RFCN in the former fuzzy complex linear system (FCLS), the scalar multiplication and addition operations of complex numbers and fuzzy complex numbers (FCN) based on a new representation of FCN are proposed. We also introduce a new method for solving FCLS, which can convert FCLS into two distinct linear systems. One is an n×n complex linear system, and the other is an (mn)×(mn) real linear system, where n is the number of unknown variables, and m is the number of substitutional cyclic sets composed of coefficients of FCLS. In particular, using this method to solve one-dimensional fuzzy linear systems, a (2n)×(2n) RLS is obtained, which is consistent with Friedman’s method. Finally, FCLS based on the RFCN as a special case are also investigated. Full article
(This article belongs to the Special Issue Fuzzy Group Decision Making)
26 pages, 3229 KiB  
Article
Evaluating Pallet Investment Strategy Using Fuzzy Analytic Network Process: A Case in Chinese Chain Supermarkets
by Hung-Lung Lin, Yu-Yu Ma and Chin-Tsai Lin
Mathematics 2021, 9(24), 3210; https://doi.org/10.3390/math9243210 - 12 Dec 2021
Cited by 3 | Viewed by 2365
Abstract
Presently in Chinese chain supermarkets, many enterprises have built automatic equipment and information facilities in the logistics center of their supply chain systems. Modern logistics technology and equipment largely depend on the resource integration of each role in the chain (such as suppliers, [...] Read more.
Presently in Chinese chain supermarkets, many enterprises have built automatic equipment and information facilities in the logistics center of their supply chain systems. Modern logistics technology and equipment largely depend on the resource integration of each role in the chain (such as suppliers, manufacturers, wholesalers, and retailers), especially logistics facilities and equipment resources, to realize the circulation of products. The pallet, which is an indispensable basic tool for a supply chain system in the process of product circulation, is most often used in the handling, stacking, storage, and transportation of products. The process of building automation and informationization in the supply chain system of Chinese supermarket chains requires the solving of the problems of cost and sharing pallets in logistics operations. Large-scale enterprises often spend millions of dollars on investment, the failure of which can cause significant harm to the enterprise. Therefore, the authors of this paper adopted the fuzzy analytic network process (FANP), combining fuzzy and ANP models to evaluate our studied case. We utilized an actual case as the research object to resolve the important decisions regarding pallet resource sharing investment in the supply chain system. Importantly, it is expected that the proposed method can provide an important reference standard or a new idea for decision makers in the chain supermarket industry or related industries. Full article
(This article belongs to the Special Issue Fuzzy Group Decision Making)
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23 pages, 358 KiB  
Article
Hesitant Fuzzy 2-Dimension Linguistic Programming Technique for Multidimensional Analysis of Preference for Multicriteria Group Decision Making
by Xiaoyue Liu and Dawei Ju
Mathematics 2021, 9(24), 3196; https://doi.org/10.3390/math9243196 - 10 Dec 2021
Cited by 2 | Viewed by 1617
Abstract
The hesitant fuzzy 2-dimension linguistic element (HF2DLE) allows decision makers to express the importance or reliability of each term included in a hesitant fuzzy linguistic element as a linguistic term. This paper investigates a programming technique for multidimensional analysis of preference for hesitant [...] Read more.
The hesitant fuzzy 2-dimension linguistic element (HF2DLE) allows decision makers to express the importance or reliability of each term included in a hesitant fuzzy linguistic element as a linguistic term. This paper investigates a programming technique for multidimensional analysis of preference for hesitant fuzzy 2-dimension linguistic multicriteria group decision making. Considering the flexibility of HF2DLEs in expressing hesitant qualitative preference information, we first adopt HF2DLEs to depict both the evaluation values of alternatives and the truth degrees of alternative comparisons. To calculate the relative closeness degrees (RCDs) of alternatives, the Euclidean distances between HF2DLEs are defined. Based on RCDs and preference relations on alternatives, the group consistency and inconsistency indices are constructed, and a bi-objective hesitant fuzzy 2-dimension linguistic programming model is established to derive the criteria weights and positive and negative ideal solutions. Since the objective functions and partial constraint coefficients of the established model are HF2DLEs, an effective solution is developed, through which the RCDs can be calculated to obtain the individual rankings of alternatives. Furthermore, a single-objective assignment model is constructed to determine the best alternative. Finally, a case study is conducted to illustrate the feasibility of the proposed method, and its effectiveness is demonstrated by comparison analyses. Full article
(This article belongs to the Special Issue Fuzzy Group Decision Making)
24 pages, 14827 KiB  
Article
Evaluating Appointment of Division Managers Using Fuzzy Multiple Attribute Decision Making
by Hsu-Lin Chen, Yi-Chung Hu and Ming-Yen Lee
Mathematics 2021, 9(19), 2417; https://doi.org/10.3390/math9192417 - 28 Sep 2021
Cited by 2 | Viewed by 1939
Abstract
Subsidiaries typically start out as a company division. As the company expands its product lines, the regions it operates in, or the customers it serves, the company is likely to combine the related research and development, procurement, production, and sales departments into a [...] Read more.
Subsidiaries typically start out as a company division. As the company expands its product lines, the regions it operates in, or the customers it serves, the company is likely to combine the related research and development, procurement, production, and sales departments into a relatively discrete organizational structure. As such, the head of the division is often of equal importance as the company president. In particular, when the product has a competitive advantage, the head of the division has more authority in the company’s future operational planning. Thus, when a company has a multidivisional organizational structure, the heads of those divisions typically have considerable responsibilities. In this study, literature data were combined with a fuzzy Delphi expert questionnaire survey to determine the constructs and criteria for assessing candidates for division manager. Subsequently, the fuzzy decision-making trial and evaluation laboratory (DEMATEL) and the fuzzy DEMATEL-based analytic network process were used to identify the causal relationships between criteria and their weights, and the fuzzy technique for the order of preference by similarity to ideal solution was used to rank the solutions to approximate the company’s optimal candidate for division manager and provide the ideal decision-making solutions, which may offer companies with the reference of selecting the senior executives. Full article
(This article belongs to the Special Issue Fuzzy Group Decision Making)
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18 pages, 2201 KiB  
Article
A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication
by Yu-Cheng Wang, Horng-Ren Tsai and Toly Chen
Mathematics 2021, 9(12), 1430; https://doi.org/10.3390/math9121430 - 19 Jun 2021
Cited by 13 | Viewed by 1582
Abstract
Forecasting the cycle time of each job is a critical task for a factory. However, recent studies have shown that it is a challenging task, even with state-of-the-art deep learning techniques. To address this challenge, a selectively fuzzified back propagation network (SFBPN) approach [...] Read more.
Forecasting the cycle time of each job is a critical task for a factory. However, recent studies have shown that it is a challenging task, even with state-of-the-art deep learning techniques. To address this challenge, a selectively fuzzified back propagation network (SFBPN) approach is proposed to estimate the range of a cycle time, the results of which provide valuable information for many managerial purposes. The SFBPN approach is distinct from existing methods, because the thresholds on both the hidden and output layers of a back propagation network are fuzzified to tighten the range of a cycle time, while most of the existing methods only fuzzify the threshold on the output node. In addition, a random search and local optimization algorithm is also proposed to derive the optimal values of the fuzzy thresholds. The proposed methodology is applied to a real case from the literature. The experimental results show that the proposed methodology improved the forecasting precision by up to 65%. Full article
(This article belongs to the Special Issue Fuzzy Group Decision Making)
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