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Subject: search.filters.subject.Fuzzy logic

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    Is Szabolcsi’s logic a fuzzy logic?
    (Universidad Panamericana, 2024-12-17)
    Castro-Manzano, J.-Martín
    In this paper we ask ourselves whether Szabolcsi’s numerical term logic is a fuzzy logic. Our answer is in the affirmative. In order to justify such a claim, we first expound some preliminaries that help us understand why the inclusion of fuzzy quantifiers is a sufficient condition for fuzziness. Then we present Szabolcsi’s logic, which includes said quantifiers.
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    Selecting the Distribution System using AHP and Fuzzy AHP Methods
    (Springer Nature, 2024)
    Saucedo-Martínez, Jania Astrid
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    Salais-Fierro, Tomás Eloy
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    Marmolejo Saucedo, José Antonio
    In this research, we present a supporting tool for decision making by designing a distribution system for a trading company of supplies for the welding industry in Mexico. The case study encompasses a distribution system with shortage problems and poor fleet capacity. To address these problems, improvement options were grouped into three possible scenarios through a third-party logistics (3PL) service. Furthermore, for the evaluation and selection of one of the scenarios, the Analytic Hierarchy Process (AHP) methodology was proposed integrating fuzzy logic as a tool for decision making, including factors of uncertainty and subjectivity as well as a comparison with traditional AHP obtaining the best scenario, meeting the requirements of the company, and showing potential improvements in the desired service level for its distribution system. © 2024 Springer Nature
    Scopus© Citations 3  8  1
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    Un modelo de minimización de costos de mantenimiento de equipo médico mediante lógica difusa
    (2019)
    Cabrera Llanos, Agustín Ignacio
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    Cruz Aranda, Fernando 
    This paper presents an algorithm based on fuzzy logic that models a Maintenance Management Plan of Medical Equipment, this is developed in three stages: In the first one, a functional inventory is generated, following the protocols recommended by the WHO and information of each team. In the second, three priority attention protocols are attached, used to select the diffuse system membership functions. In the third, a family of scenarios is generated by Monte Carlo simulation, calculating the degreeof fuzzy maintenance priority for the equipment. The results achieve that the equipment selection of the annual maintenance plan is carried out guaranteeing the availability of the priority equipment. In this article, the application of the Fennigkoh-Smith algorithms and the Wang-Levenson algorithm are improved by placing the ambiguity of the diffuse structure, making the selection of the medical equipment incorporate the range of possibilities that exist when selected in an arbitrary manner. An area of opportunity consists of incorporating a process of optimization of equipment maintenance costs with a budgetary restriction. It is concluded that the system shown is friendly and robust for the purposes proposed.
    Scopus© Citations 3  28  1
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    A hybrid fuzzy-molecular controller enhanced with evolutionary algorithms: A case study in a one-leg mechanism
    (2019)
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    Esparza-Duran, Noel
    Intelligent control systems are able to work well in uncertain nonlinear systems, mainly for: changes in the operating point, presence of environmental noise and disturbances, uncertainty in sensor measurements, miscalibration, uncertain model plant, and others. For instance, fuzzy controllers have been widely studied and applied. Recently, artificial organic controllers (AOC) have been proposed as an ensemble of fuzzy logic and artificial hydrocarbon networks. However, a weakness in AOC is the lack of training methods for tuning parameters for desired output responses in control. In this regard, this paper aims to introduce an evolutionary optimization method, i.e. particle swarm optimization, for tuning artificial organic controllers. Three objectives are proposed for automatic tuning of AOC: overall error, steady-state error and settling time of output response. The proposed methodology is implemented in the well-known cart-pole system. Also, the proposed method is applied on a one-leg unstable mechanism as case study. Results validate that automatic tuning of AOC over simulation systems can achieve suitable output responses with minimal overall error, steady-state error and settling time. © 2019 The Franklin Institute. Journal of the Franklin Institute, Elsevier Ltd.
    Scopus© Citations 3  15  1
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    Project Valuation of a Distribution Centre of an Auxiliary Rail Freight Terminal: Using Real Options with Fuzzy Logic and Binomial Trees
    (2016)
    Cruz Aranda, Fernando 
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    Cabrera Llanos, Agustín Ignacio
    This paper presents the financial evaluation of the extension of an auxiliary rail freight terminal to integrate it to a logistics platform (LP). This investment phase is focused on building a distribution center (CEDI), as part of a comprehensive project of high commercial and strategic impact for Mexico. The project evaluation is done using binomial trees for the valuation of an American type real call option, incorporating the expected volatility over the expected cash flows, in order to determine the benefit of postponing the project three years. In addition, to complement this real option valuation, we incorporate the fuzzy logic theory in the process of assigning probabilities to the branches of tree. The value of the American type real call option to postpone the project three years is 30.37% of investment, while the value of real option, using fuzzy logic is 29.94% of investment, this is a better result. © 2016, ASERS Publishing House. All rights reserved.
      35  2
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    Demand prediction using a soft-computing approach : a case study of automotive industry
    (2020)
    Salais-Fierro, Tomás Eloy
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    Saucedo-Martínez, Jania
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    Vela-Haro, Jose Manuel
    According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data. © 2019 by the authors.
    Scopus© Citations 13  14  2