scholarly journals Production Control Process using Integrated Robust Data Envelopment Analysis and Fuzzy Neural Network

2019 ◽  
Vol 4 (3) ◽  
pp. 580-590 ◽  
Author(s):  
Hadi Gholizadeh ◽  
Hamed Fazlollahtabar
Keyword(s):  
Neural Network ◽  
Data Envelopment Analysis ◽  
Cycle Time ◽  
Measurement Errors ◽  
Fuzzy Neural Network ◽  
Production Control ◽  
Control Process ◽  
Data Envelopment ◽  
Manufacturing Companies ◽  
Fuzzy Neural

Considering the importance of the quality and responsiveness of manufacturing companies to customers, the most important principle can be considered to be the reduction of the cycle time of the production process. Because in the real world due to measurement errors and inaccuracies of information and the uncertainty of processes the concept Fuzzy is used. Therefore, in this study, the parameters for controlling the process of forming are optimized to reduce the cycle time. Since the optimization of an aspect of quality may modify other aspects, so, to overcome this problem, the multi-response method (Taguchi MRT) has been developed. Also, integrated fuzzy-neural network and data envelopment analysis is used for optimization and analysis purposes. Experimental results are done to measure the effectiveness of this approach in a manufacturing company in Iran.

Fuzzy-neural-network-based fluctuation smoothing rule for reducing the cycle times of jobs with various priorities in a wafer fabrication plant: A simulation study

2009 ◽  
Vol 223 (8) ◽  
pp. 1033-1043 ◽  
Author(s):  
T Chen
Keyword(s):  
Neural Network ◽  
Cycle Time ◽  
Fuzzy Neural Network ◽  
Optimal Solution ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
The Difference ◽  
Fluctuation Smoothing

This paper presents a fuzzy-neural-network-based fluctuation smoothing rule to further improve the performance of scheduling jobs with various priorities in a wafer fabrication plant. The fuzzy system is modified from the well-known fluctuation smoothing policy for a mean cycle time (FSMCT) rule with three innovative treatments. First, the remaining cycle time of a job is estimated by applying an existing fuzzy-neural-network-based approach to improve the estimation accuracy. Second, the components of the FSMCT rule are normalized to balance their importance. Finally, the division operator is applied instead of the traditional subtraction operator in order to magnify the difference in the slack and to enhance the responsiveness of the FSMCT rule. To evaluate the effectiveness of the proposed methodology, production simulation is applied to generate some test data. According to the experimental results, the proposed methodology outperforms six existing approaches in the reduction of the average cycle times. In addition, the new rule is shown to be a Pareto optimal solution for scheduling jobs in a semiconductor manufacturing plant.

A Novel Greenhouse Control System Based on Fuzzy Neural Network

2014 ◽  
Vol 668-669 ◽  
pp. 415-418 ◽  
Author(s):  
Xin Yi Zhang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Control Strategies ◽  
Control Process ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Greenhouse Control

Due to the complexity of greenhouse environment, greenhouse system cannot be controlled perfectly by traditional control method. This paper proposes a novel greenhouse control system based on fuzzy neural network to regulate the internal climate of the greenhouse. Temperature and humidity are selected as the inputs of controller, while the skylight, sun-shade net, circulation fan, side windows, fuel heater, and micro-mist humidifier are selected as the outputs. After analyzing every situation that may occur in the control process and the corresponding control strategies, we obtain 35 control “IF-THEN” rules. Simulation results show that the fuzzy neural network controller have certain improvements than the conventional PID controller in the aspects of overshoot, stability and response time.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Inverse Control of Cable-driven Parallel Mechanism Using Type-2 Fuzzy Neural Network

2010 ◽  
Vol 36 (3) ◽  
pp. 459-464 ◽  
Author(s):  
Cheng-Dong LI ◽  
Jian-Qiang YI ◽  
Yi YU ◽  
Dong-Bin ZHAO
Keyword(s):  
Neural Network ◽  
Parallel Mechanism ◽  
Fuzzy Neural Network ◽  
Inverse Control ◽  
Fuzzy Neural

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

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