scholarly journals A Hybrid Approach Combining Fuzzy c-Means-Based Genetic Algorithm and Machine Learning for Predicting Job Cycle Times for Semiconductor Manufacturing

2021 ◽  
Vol 11 (16) ◽  
pp. 7428
Author(s):  
Gyu M. Lee ◽  
Xuehong Gao
Keyword(s):  
Genetic Algorithm ◽  
Cycle Time ◽  
Hybrid Approach ◽  
Cycle Times ◽  
Fuzzy C Means ◽  
Backpropagation Network ◽  
Semiconductor Fabrication ◽  
Time Required ◽  
And Training

Job cycle time is the cycle time of a job or the time required to complete a job. Prediction of job cycle time is a critical task for a semiconductor fabrication factory. A predictive model must forecast job cycle time to pursue sustainable development, meet customer requirements, and promote downstream operations. To effectively predict job cycle time in semiconductor fabrication factories, we propose an effective hybrid approach combining the fuzzy c-means (FCM)-based genetic algorithm (GA) and a backpropagation network (BPN) to predict job cycle time. All job records are divided into two datasets: the first dataset is for clustering and training, and the other is for testing. An FCM-based GA classification method is developed to pre-classify the first dataset of job records into several clusters. The classification results are then fed into a BPN predictor. The BPN predictor can predict the cycle time and compare it with the second dataset. Finally, we present a case study using the actual dataset obtained from a semiconductor fabrication factory to demonstrate the effectiveness and efficiency of the proposed approach.

A PCA-FBPN Approach for Job Cycle Time Estimation in a Wafer Fabrication Factory

2012 ◽  
Vol 2 (2) ◽  
pp. 50-67 ◽  
Author(s):  
Toly Chen
Keyword(s):  
Cycle Time ◽  
Back Propagation ◽  
Principal Component ◽  
Time Estimation ◽  
Cycle Times ◽  
Forecasting Performance ◽  
Semiconductor Manufacturer ◽  
Fuzzy Back Propagation Network ◽  
Propagation Network

Variable replacement is a well-known technique to improve the forecasting performance, but has not been applied to the job cycle time forecasting, which is a critical task to a semiconductor manufacturer. To this end, in this study, principal component analysis (PCA) is applied to enhance the forecasting performance of the fuzzy back propagation network (FBPN) approach. First, to replace the original variables, PCA is applied to form variables that are independent of each other, and become new inputs to the FBPN. Subsequently, a FBPN is constructed to estimate the cycle times of jobs. According to the results of a case study, the hybrid PCA-FBPN approach was more efficient, while achieving a satisfactory estimation performance.

Test Suite Optimization Using Firefly and Genetic Algorithm

2022 ◽  
pp. 1635-1651
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Genetic Algorithm ◽  
Software Engineering ◽  
Software Testing ◽  
Hybrid Approach ◽  
Empirical Evaluation ◽  
Data Generation ◽  
Testing Environment ◽  
Novel Approach ◽  
Test Suite Optimization

Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.

Co-delivered and co-produced: creating a recovery college in partnership

2014 ◽  
Vol 9 (1) ◽  
pp. 16-25 ◽  
Author(s):  
Sara Meddings ◽  
Diana Byrne ◽  
Su Barnicoat ◽  
Emogen Campbell ◽  
Lucy Locks
Keyword(s):  
Design Methodology ◽  
Shared Vision ◽  
Training Curriculum ◽  
Additional Time ◽  
Content Type ◽  
Time Required ◽  
Partnership Approach ◽  
Organisational Cultures ◽  
And Training

Purpose – The purpose of this paper is to explore the process of using a co-production partnership approach in the development of a Recovery College pilot. Design/methodology/approach – This is a case study of the co-production process, using action research to learn from ongoing reflection, mid-project review and feedback questionnaires. Findings – The partnership process is an integral and valued aspect of the Recovery College. Challenges include different organisational cultures and processes and the additional time required. Mutual respect, appreciation of different expertise, communication, a shared vision and development plan have been key to success. The paper focused on governance and fidelity; recruitment and training; curriculum development and evaluation. People are enthusiastic and motivated. Co-production and equal partnership are a valuable approach to developing a Recovery College. Originality/value – At present many regions are developing Recovery Colleges. This paper describes one approach and shows that co-production is valuable to the process of developing a Recovery College.

Optimisation of a Robotic Workcell Layout Using Genetic Algorithms

2005 ◽  
Author(s):  
Siang-Kok Sim ◽  
Meng-Leong Tay ◽  
Ahmad Khairyanto
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Cycle Time ◽  
Production Cycle ◽  
Robot Arm ◽  
Layout Problem ◽  
Common Features ◽  
Placement Method ◽  
The Common

With the advent of robots in modern-day manufacturing workcells, optimization of robotic workcell layout (RWL) is crucial in ensuring the minimization of the production cycle time. Although RWL share many aspects with the well-known facility layout problem (FLP), there are features which set the RWL apart. However, the common features which they share enable approaches in FLP to be ported over to RWL. One heuristic gaining popularity is genetic algorithm (GA). In this paper, we present a GA approach to optimizing RWL by using the distance covered by the robot arm as a means of gauging the degree of optimization. The approach is constructive: the different stations within the workcell are placed one by one in the development of the layout. The placement method adopted is based on the spiral placement method first broached by Islier (1998). The algorithm was implemented in Visual C++ and a case study assessed its performance.

Test Suite Optimization Using Firefly and Genetic Algorithm

2019 ◽  
Vol 11 (1) ◽  
pp. 31-46 ◽  
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Genetic Algorithm ◽  
Software Engineering ◽  
Software Testing ◽  
Hybrid Approach ◽  
Empirical Evaluation ◽  
Data Generation ◽  
Testing Environment ◽  
Novel Approach ◽  
Test Suite Optimization

Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.

scholarly journals An Application of Genetic Algorithm in Determining Salesmen’s Routes: A Case Study

2018 ◽  
Vol 17 (1) ◽  
pp. 26
Author(s):  
Noufal Zhafira ◽  
Feri Afrinaldi ◽  
Taufik Taufik
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Pharmaceutical Products ◽  
Traveling Salesman ◽  
Future Research ◽  
Distribution Problem ◽  
Distribution Company ◽  
Time Required ◽  
The City

This paper presents a case study of determining vehicles’ routes. The case is taken from a pharmaceutical products distribution problem faced by a distribution company located in the city of Padang, Indonesia. The objective of this paper is to reduce the total distribution time required by the salesmen of the company. Since the company uses more than one salesman, then the problem is modeled as a multi traveling salesman problem (m-TSP). The problem is solved by employing genetic algorithm (GA) and a Matlab® based computer program is developed to run the algorithm. It is found that, by employing two salesmen only, the routes produced by GA results in a 30% savings in total distribution time compared to the current routes used by the company (currently the company employs three salesmen). This paper determines distances based on the latitude and longitude of the locations visited by the salesmen. Therefore, the distances calculated in this paper are approximations. It is suggested that actual distances are used for future research.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

scholarly journals Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data

Electricity ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 91-109
Author(s):  
Julian Wruk ◽  
Kevin Cibis ◽  
Matthias Resch ◽  
Hanne Sæle ◽  
Markus Zdrallek
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Network Planning ◽  
Voltage Regulation ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
The Impact

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

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