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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 2989

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 (3 papers)

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Research

33 pages, 6557 KiB  
Article
A Sustainable Supply Chain Model with a Setup Cost Reduction Policy for Imperfect Items under Learning in a Cloudy Fuzzy Environment
by Basim S. O. Alsaedi
Mathematics 2024, 12(10), 1603; https://doi.org/10.3390/math12101603 - 20 May 2024
Viewed by 418
Abstract
The present paper deals with an integrated sustainable supply chain model with the effect of learning for an imperfect production system under a cloudy fuzzy environment where the demand rate is treated as a cloudy triangular fuzzy (imprecise) number, which means that the [...] Read more.
The present paper deals with an integrated sustainable supply chain model with the effect of learning for an imperfect production system under a cloudy fuzzy environment where the demand rate is treated as a cloudy triangular fuzzy (imprecise) number, which means that the demand rate of the items is not constant, and shortages and a warranty policy are allowed. The vendor governs the manufacturing process to serve the demand of the buyer. When the vendor supplies the demanded lot after the production of items, it is also considered that the delivery lots have some defective items that follow an S-shape learning curve. After receiving the lot, the buyer inspects the whole lot, and the buyer classifies the whole lot into two categories: one is the defective-quality items and the other is the imperfect-quality items. The buyer returns the defective-quality items to the seller after a screening process, for which a warranty cost is included. During the transportation of the items, a lot of carbon units are emitted from the transportation, damaging the quality of the environment. The seller includes carbon emission costs to achieve sustainability as per considerations. A one-time discrete investment is also included for the minimizing of the setup cost of the seller for the next cycles. We developed models for the scenario of the separate decision and for the integrated decision of the players (seller/buyer) under the model’s consideration. Our aim is to jointly optimize the integrated total fuzzy cost under a cloudy fuzzy environment sustained by the seller and buyer. Numerical examples, sensitivity, analysis limitations, future scope and conclusions have been provided for the justification of the proposed model, and the impact of the input parameters on the decision variables and integrated total fuzzy cost for the supply chain are provided for the validity and robustness of this proposed model. The effect of learning in a cloudy fuzzy environment was positive for this proposed model. Full article
(This article belongs to the Special Issue Advances and Applications on Fuzzy Logic for Decision Making Processes)
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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 435
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 1328
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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