Fuzzy Logic and Interval Arithmetic-Based TOPSIS Method for Multicriteria Reverse Auctions

2012 ◽  
Vol 4 (2) ◽  
pp. 101-117 ◽  
Author(s):  
Ritesh Kumar Singh ◽  
Lyes Benyoucef
Keyword(s):  
Fuzzy Logic ◽  
Interval Arithmetic ◽  
Topsis Method ◽  
Reverse Auctions

Comparison Analysis of TOPSIS and Fuzzy Logic Methods On Fertilizer Selection

2018 ◽  
Vol 7 (2.3) ◽  
pp. 109 ◽  
Author(s):  
Asmara Indahingwati ◽  
Muh Barid Nizarudin Wajdi ◽  
Dwi Ermayanti Susilo ◽  
Nuning Kurniasih ◽  
Robbi Rahim
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Multiple Criteria Decision Making ◽  
Fruit Trees ◽  
Chemical Fertilizer ◽  
Interactive System ◽  
Topsis Method

Decision Support System is an interactive system that supports decision in the decision-making process through alternatives derived from the processing of data, information and design of the models. Selection decision support system of chemical fertilizer in fruit plant is expected to help anyone who wants to cultivate fruit trees can determine the chemical fertilizer as expected based alternatives and criteria set by the user. In this research method used is TOPSIS Method and Method of Fuzzy Logic. TOPSIS method is one of multiple criteria decision making method that uses the principle that the alternatives selected must have the shortest distance. Fuzzy Logic is a methodology of control systems troubleshooting, the fuzzy logic stated that everything is a binary which means it is only two possibilities, "Yes or No", "True or False", "Good or Bad", and others. Therefore, all of these can have a membership value of 0 or 1.  

scholarly journals A fuzzy system to stimulate the planting of beans in Brazil

2018 ◽  
Vol 9 (2) ◽  
pp. 375
Author(s):  
Frederico Silva Valentim Sallum ◽  
Luiz Flavio Autran Monteiro Gomes ◽  
Maria Augusta Soares Machado
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy System ◽  
Large Scale ◽  
Brazilian Population ◽  
Expected Loss ◽  
Topsis Method

Beans are traditional elements on the table of the Brazilian population. In spite of this, the production of beans has been falling in Brazil in the last years. This article tries to identify the causes for the lack of motivation for growing beans in Brazil. It also aims to point out which factors are the most relevant for obtaining an expressive plantation and bean harvest. Given this, we were able to develop a system based on fuzzy logic that can be useful for measuring the expected loss until the bean harvest. With the output from such system the farmer can be provided incentives to start planting once that loss is considered acceptable.  In order to generate the rules of the system the multicriteria TOPSIS method is used. The prototype fuzzy system explained and proposed in this article can be further expanded by agricultural experts thus leading to a large scale planting of beans.

scholarly journals SISTEM PENDUKUNG KEPUTUSAN PENENTUAN CUTI PEGAWAI MENGGUNAKAN METODE TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS) (STUDI KASUS DI KANTOR PELAYANAN PERBENDAHARAAN NEGARA YOGYAKARTA)

Compiler ◽  
2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Made Intan Dwipayani ◽  
Anton Setiawan Honggowibowo ◽  
Dwi Nugraheny
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Topsis Method ◽  
A Value ◽  
Degree Of Membership ◽  
Calculation Process ◽  
Order Preference

Decision Support System or DSS is a system that helps managers in solving the problems of semi-structured. Many techniques are used to make the Decision Support System, one with Fuzzy Logic. Fuzzy logic is one of problem-solving techniques where the degree of membership which are typically represented by values 0 and 1, with Fuzzy Logic is the degree of membership can be represented with a value between 0 and 1 so that it can be more balanced.             KPPN Yogyakarta in determining an employee on leave not yet have an application, and is still done manually. Therefore, to further assist in making the determination of the employee on leave should be made DSS. DSS will use fuzzy logic techniques MADM applying TOPSIS method in the calculation process. SPK expected this may be an alternative in the determination of an employee on leave in KPPN Yogyakarta later. Decision Support System determination of an employee on leave with TOPSIS method is to obtain results that are not much different from the calculations manually, so it can be applied to support the decision in the determination of employee leave.

Improving Decision-Making in a Business Simulator Using TOPSIS Methodology for the Establishment of Reactive Stratagem

2017 ◽  
pp. 12-21
Author(s):  
Carlos Alberto Ochoa Ortiz Zezzatti
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Group Decision Making ◽  
Hydraulic Excavator ◽  
Linguistic Variables ◽  
Topsis Method ◽  
High Productivity ◽  
Construction Company ◽  
Quantitative Studies ◽  
Expert’S Opinion

This study combines Fuzzy Logic and multicriteria TOPSIS method for the selection, from three different alternatives, which machines of high productivity is more convenient to a construction company. The evaluation of each alternative is made through group decision making which identifies the most important criteria according to the requirements presented by the company. To assess the selected criteria in the TOPSIS method is weighted by a group of experts who, based on their experience and knowledge of this type of machinery, assess the relevance of these in the operation and functioning of the hydraulic excavator. Both qualitative and quantitative studies are used in this work, however the experts evaluate, through surveys based on Likert scale all the criteria in which they want to measure the perception. Data provided from the surveys is used for the construction and association of the groups of expert's opinion through the use of fuzzy sets to avoid ambiguity problems of the linguistic variables.

Hysteresis in Categorization: Fuzzy Logic Makes Fuzzy Boundaries

2012 ◽  
Author(s):  
Thomas M. Crawford ◽  
Justin Fine ◽  
Donald Homa
Keyword(s):  
Fuzzy Logic

Continuous Estimation of CO2 Production during Exercise

1997 ◽  
Vol 36 (04/05) ◽  
pp. 368-371
Author(s):  
R. Soma ◽  
Y. Yamamoto
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Infusion Rate ◽  
Feedback System ◽  
Whole Body ◽  
Co2 Production ◽  
Cycle Exercise ◽  
Continuous Estimation ◽  
Breath Measurement ◽  
13Co2 Enrichment

Abstract.A new method was developed for continuous isotopic estimation of human whole body CO2 rate of appearance (Ra) during non-steady state exercise. The technique consisted of a breath-by-breath measurement of 13CO2 enrichment (E) and a real-time fuzzy logic feedback system which controlled NaH13CO3 infusion rate to achieve an isotopic steady state. Ra was estimated from the isotope infusion rate and body 13CO2 enrichment which was equal to E at the isotopic steady state. During a non-steady state incremental cycle exercise (5 w/min or 10 w/min), NaH13CO3 infusion rate was successfully increased by the action of feedback controller so as to keep E constant.

Modified fuzzy-based greedy routing protocol for VANETs

2020 ◽  
Vol 39 (6) ◽  
pp. 8357-8364
Author(s):  
Thompson Stephan ◽  
Ananthnarayan Rajappa ◽  
K.S. Sendhil Kumar ◽  
Shivang Gupta ◽  
Achyut Shankar ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Routing Protocol ◽  
Intelligent Transportation Systems ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Research Area ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Greedy Routing ◽  
Hoc Networks

Vehicular Ad Hoc Networks (VANETs) is the most growing research area in wireless communication and has been gaining significant attention over recent years due to its role in designing intelligent transportation systems. Wireless multi-hop forwarding in VANETs is challenging since the data has to be relayed as soon as possible through the intermediate vehicles from the source to destination. This paper proposes a modified fuzzy-based greedy routing protocol (MFGR) which is an enhanced version of fuzzy logic-based greedy routing protocol (FLGR). Our proposed protocol applies fuzzy logic for the selection of the next greedy forwarder to forward the data reliably towards the destination. Five parameters, namely distance, direction, speed, position, and trust have been used to evaluate the node’s stability using fuzzy logic. The simulation results demonstrate that the proposed MFGR scheme can achieve the best performance in terms of the highest packet delivery ratio (PDR) and minimizes the average number of hops among all protocols.

Design and Simulation of Smart Control System for Internet Traffic Distribution on Servers by Using Fuzzy Logic System

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Raid Daoud ◽  
Yaareb Al-Khashab
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Input Parameter ◽  
Traffic Density ◽  
The Internet ◽  
Linguistic Variables ◽  
Internet Service ◽  
Traffic Distribution ◽  
Smart Control ◽  
High Level

The internet service is provided by a given number of servers located in the main node of internet service provider (ISP). In some cases; the overload problem was occurred because a demand on a given website goes to very high level. In this paper, a fuzzy logic control (FLC) has proposed to distribute the load into the internet servers by a smart and flexible manner. Three effected parameters are tacked into account as input for FLC: link capacity which has three linguistic variables with Gaussian membership function (MF): (small, medium and big), traffic density with linguistic variables (low, normal and high) and channel latency with linguistic variables (empty, half and full); with one output which is the share server status (single, simple and share). The proposed work has been simulated by using MATLAB 2016a, by building a structure in the Fuzzy toolbox. The results were fixed by two manners: the graphical curves and the numerical tables, the surface response was smoothly changed and translates the well-fixed control system. The numerical results of the control system satisfy the idea of the smart rout for the incoming traffics from the users to internet servers. So, the response of the proposed system for the share of server ratio is 0.122, when the input parameter in the smallest levels; and the ratio is 0.879 when the input parameters are in highest level. The smart work and flexible use for the FLC is the main success solution for most of today systems control.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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