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프린트페이스북

(대학원생 마일리지 적용) Seminar Notice (4/1(Mon) 4pm, 4/4(Thu)10:30am, Stefan Helber at Leibniz University Hannover)

2019.03.29 10:12

Dear ISysE Professors and Students,

 

ISysE department invites you to attend the following seminar.

 

Speaker: Stefan HelberLeibniz University Hannover    

 

Title: Modeling and predicting the throughput of stochastic flow lines with limited local buffer capacity via artificial neural networks

Date & Time: April 1st(Mon), 4:00 pm

Place: E2-2 Bldg, #B105

 

Title: Location Planning for Dynamic Wireless Charging Systems for Electric Airport Passenger Buses

 

Date & Time: April 4th(Thu), 10:30 am

 

Place: E2-2 Bldg, #B105

 

We look forward to your attendance and encourage you to forward this invitation to colleagues who may be interested in the topic.

           

With best regards,

 

Heelang Ryu

 

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Modeling and predicting the throughput of stochastic flow lines with limited local buffer capacity via artificial neural networks

 

Flow lines are frequently used to organize the mass production of physical goods in manufacturing. Such a line consists of serially arranged stations that are designed and equipped to perform dedicated tasks. The product units flow through the line to receive a series of operations at those stations. In a deterministic setting, the slowest station is the system's bottleneck and determines its throughput or production rate (measured in product units per time unit). However, in reality processing times are often stochastic, e.g., because of machine failures. In this case, to avoid blocking and starving, costly buffers can be installed between the stations to limit the propagation of failures up- and downstream of the system. In practice, discrete-event simulation is often used to estimate the production rate of a given (planned) flow line configuration. As an alternative, extremely fast approximate analytical methods have been developed to estimate the production rate of stochastic flow lines without using discrete-event simulation. We use such an analytical method to create and evaluate a large number of hypothetical flow lines and then train an artificial neural network to predict the production rate of flow lines which have not yet been analyzed before. We present first results from a systematic study of this new approach for flow line performance evaluation.

 

 

Location Planning for Dynamic Wireless Charging Systems for Electric Airport Passenger Buses

 

The majority of the ground vehicles operating on the airside parts of commercial airports are currently powered by diesel engines. These include vehicles such as apron buses, fuel trucks, and aircraft tractors. Hence, these vehicles contribute to the overall CO 2 emissions of the aviation transport system and thus negatively influence its environmental footprint. To reduce this damaging environmental impact, these vehicles could potentially be electrified with on-board batteries as their energy sources. However, the conductive charging of such vehicles via stationary cable connections is rather time-consuming. A dynamic wireless charging system to supply public transportation passenger buses with electric energy while in motion has recently been installed on the Korea Advanced Institute of Science and Technology (KAIST) campus and in the Korean city of Gumi. In this paper, we study configuration problems related to the use of this technology to make airport operations more environmentally sustainable. We concentrate on the power supply for apron buses and analyze the location planning problems related to the distribution of the required power supply and the wireless charging units in the apron road system. To this end, we develop a formal optimization model and discuss the first numerical results.

 

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