A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation

The data processing of traffic volume is a key issue in the traffic planning. In order to solve the problem of data processing on traffic volume, the bi-level programming model built is employed to work out production-attraction value of each district and O-D distribution matrix. In this model, upper part is to calculate the minimized variance between investigated value and calculated value. While the lower part of this model aims at the fact that travelers turn to the shortest path. The upper model is solved by genetic algorithm. And the result from an example application reveals that we can obtain reasonable production-attraction value and O-D distribution matrix by this method.

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

Applied Sciences ◽

10.3390/app11167428 ◽

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.

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Analisis Kinerja dan Pengaruh Pelebaran Jalan serta Pembuatan Median Jalan (Studi Kasus Jalan Temanggung Tilung Kota Palangkaraya)

Media Ilmiah Teknik Sipil ◽

10.33084/mits.v7i1.685 ◽

2018 ◽

Vol 7 (1) ◽

pp. 51-60

Author(s):

Fitri Wulandari ◽

Nirwana Puspasari ◽

Noviyanthy Handayani

Keyword(s):

Data Retrieval ◽

Service Level ◽

Traffic Volume ◽

Primary Data ◽

Volume Data ◽

Saturation Degree ◽

The Road ◽

Before And After ◽

Road Widening ◽

The Way

Jalan Temanggung Tilung is a 2/2 UD type road (two undirected two-way lanes) with a road width of 5.5 meters, which is a connecting road between two major roads, namely the RTA road. Milono and the path of G. Obos. Over time, the volume of traffic through these roads increases every year, plus roadside activities that also increase cause congestion at several points of the way. To overcome this problem, the local government carried out road widening to increase the capacity and level of road services. The study was conducted to determine the amount of traffic volume, performance, service level of the Temanggung Tilung road section at peak traffic hours before and after road widening. Data retrieval is done by the direct survey to the field to obtain primary data in the form of geometric road data, two-way traffic volume data, and side obstacle data. Performance analysis refers to the 1997 Indonesian Road Capacity Manual (MKJI) for urban roads. From the results of data processing, before increasing the road (Type 2/2 UD), the traffic volume that passes through the path is 842 pcs/hour and after road widening (Type 4/2 UD) the traffic volume for two directions is 973 pcs/hour, with route A equaling 528 pcs/hour and direction B equaling 445 pcs/hour. Based on the analysis of road performance before road enhancement, the capacity = 2551 pcs/hour, saturation degree = 0.331, and the service level of the two-way road are level B. Based on the analysis of the performance of the way after increasing the way, the direction capacity A = 2686 pcs/hour and direction B = 2674 pcs /hour, saturation degree for direction A = 0.196 and direction B = 0.166, service level for road direction A and direction B increase to level A

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