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Mathematics
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Advances and Applications on Fuzzy Logic for Decision Making Processes
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Advances and Applications on Fuzzy Logic for Decision Making Processes

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: 31 October 2024 | Viewed by 2187

Special Issue Editor

Prof. Dr. Jiuqiang Liu

E-Mail Website
Guest Editor
Department of Mathematics, Eastern Michigan University, Ypsilanti, MI 48197, USA
Interests: fuzzy logic; fuzzy games

Special Issue Information

Dear Colleagues,

The fuzzy sets theory was introduced by Zadeh in 1965. Since then, widespread applications of the fuzzy sets theory have been found in many areas, such as the decision theory, differential equations, game theory, mathematical economics, optimization, etc. In decision sciences, fuzzy sets have great impact on preference modeling, and imprecision and uncertainty have been incorporated into the decision-making process. This Special Issue focuses on recent advances and applications of fuzzy logic for decision-making processes, with emphasis on game theory and mathematical economics, providing a platform for researchers to publish their novel, attractive results.

Prof. Dr. Jiuqiang Liu
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

  • cooperative fuzzy games
  • noncooperative games
  • competitive equilibrium
  • cores
  • fixed-point theorems
  • fuzzy bargaining sets
  • fuzzy cores
  • Ky Fan minimax inequality
  • nash equilibrium

Published Papers (2 papers)

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Research

11 pages, 631 KiB  
Article
Fuzzy Evaluation Model for Products with Multifunctional Quality Characteristics: Case Study on Eco-Friendly Yarn
by Kuen-Suan Chen, Tsun-Hung Huang, Kuo-Ching Chiou and Wen-Yang Kao
Mathematics 2024, 12(10), 1446; https://doi.org/10.3390/math12101446 - 8 May 2024
Viewed by 304
Abstract
Numerous advanced industrial countries emphasize green environmental protection alongside athletic healthcare. Many world-renowned sports brands are actively developing highly functional, environmentally friendly, and aesthetically pleasing products. For example, in the production of sports shoes, the eco-friendly yarn process is one of the important [...] Read more.
Numerous advanced industrial countries emphasize green environmental protection alongside athletic healthcare. Many world-renowned sports brands are actively developing highly functional, environmentally friendly, and aesthetically pleasing products. For example, in the production of sports shoes, the eco-friendly yarn process is one of the important processes. This process involves multiple crucial larger-the-better quality characteristics closely tied to the functionality of sports shoes. Facing green environmental regulations and external competitors, it is evidently an imperative issue for enterprises to consider how to improve the quality of newly developed products, increase product value, and lower rates of both rework and scrap to accomplish the goals of saving energy and minimizing waste. Aiming to solve this problem, this study proposed a fuzzy evaluation model for products with multifunctional quality characteristics to assist the sporting goods manufacturing industry in evaluating whether all functional quality characteristics of its products meet the required quality level. This study first utilized the larger-the-better Six Sigma quality index concerning environmental protection for evaluation and then proposed product evaluation indicators for the eco-friendly yarn. Since the parameters of these indicators have not yet been determined, sample data need to be used for estimation. Enterprises require rapid response, so that the sample size is relatively small. Sampling error will increase the risk of misjudgment. Therefore, taking suggestions from previous studies, this study constructed the fuzzy evaluation model based on confidence intervals of quality indicators for the eco-friendly yarn. This method incorporated previous experience with data, thereby enhancing assessment accuracy. Full article
(This article belongs to the Special Issue Advances and Applications on Fuzzy Logic for Decision Making Processes)
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17 pages, 2094 KiB  
Article
Improved Self-Learning Genetic Algorithm for Solving Flexible Job Shop Scheduling
by Ming Jiang, Haihan Yu and Jiaqing Chen
Mathematics 2023, 11(22), 4700; https://doi.org/10.3390/math11224700 - 20 Nov 2023
Viewed by 1170
Abstract
The flexible job shop scheduling problem (FJSP), one of the core problems in the field of generative manufacturing process planning, has become a hotspot and a challenge in manufacturing production research. In this study, an improved self-learning genetic algorithm is proposed. The single [...] Read more.
The flexible job shop scheduling problem (FJSP), one of the core problems in the field of generative manufacturing process planning, has become a hotspot and a challenge in manufacturing production research. In this study, an improved self-learning genetic algorithm is proposed. The single mutation approach of the genetic algorithm was improved, while four mutation operators were designed on the basis of process coding and machine coding; their weights were updated and their selection mutation operators were adjusted according to the performance in the iterative process. Combined with the improved population initialization method and the optimized crossover strategy, the local search capability was enhanced, and the convergence speed was accelerated. The effectiveness and feasibility of the algorithm were verified by testing the benchmark arithmetic examples and numerical experiments. Full article
(This article belongs to the Special Issue Advances and Applications on Fuzzy Logic for Decision Making Processes)
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