• Journal of Environmental Science International
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• v.8 no.3
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• pp.319-323
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• 1999

This study was designed to evaluate the exposure to benzene by residents in neighborhoods near a major roadways, by persons waiting buses, and by drivers and service station attendants while refueling. It was confirmed that the outdoor air benzene concentrations near the major roadways were higher than those further away from the sources. However, neither the indoor air nor breath concentrations were different for two specified residential areas. Smoking was confirmed as an important factor for the indoor air benzene levels. Persons waiting buses, drivers and service station attendants were exposed to elevated benzene levels compared to even the residents in neighborhoods near a major roadways. The mean benzene concentration at bus stop was 2.7 to 6.9 times higher than the mean ambient air concentration. The mean benzene concentrations in the breathing zone of drivers and service station attendants were 95 to 160 and 120 to 202 times higher than the mean ambient air concentrations, respectively.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.105-108
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• 2000

We consider a Markovian retrial queue with waiting space in which the service rates and retrial rates depend on the number of customers in the service facility and in the orbit, respectively. Each arriving customer from outside or orbit decide either to enter the facility or to join the orbit in Bernoulli manner whose entering probability depend on the number of customers in the service facility. In this paper, a stochastic order relation between two bivariate processes (C(t), N(t)) representing the number of customers C(t) in the service facility and N(t) one in the orbit is deduced in terms of corresponding parameters by constructing the equivalent processes on a common probability space. Some applications of the results to the stochastic bounds of the multi-server retrial model are presented.

• Journal of the Korean Statistical Society
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• v.29 no.4
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• pp.473-488
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• 2000

We consider a Markovian retrial queue with waiting space in which the service rates and retrial rates depend on the number of customers in the service facility and in the orbit, respectively. Each arriving customer from outside or orbit decide either to enter the facility or to join the orbit in Bernoulli manner whose entering probability depend on the number of customers in the service facility. In this paper, a stochastic order relation between two bivariate processes(C(t), N(t)) representing the number of customers C(t) in the service facility and one N(t) in the orbit is deduced in terms of corresponding parameters by constructing the equivalent processes on a common probability space. some applications of the results to the stochastic bounds of the multi-server retrial model are presented.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.4
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• pp.95-110
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• 2016

The purpose of this study is to design a service encounter of a duty free shop using a blueprinting method. This case study analyzed differences between city-based and airport-based duty free shop and proposed a flow chart based on administration blueprinting of the duty free shop in South Korea. The paper presented process criteria based on administrative procedures of customers in city-based and airport-based duty free shop. The results of the study provided the cause of failure with waiting point of customer and customer service and suggested management plan to solve failure. Also, the study addressed more customer-oriented service delivery processes to improve service quality and customer satisfaction through waiting and failure points.

• Journal of Korean Port Research
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• v.5 no.2
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• pp.19-37
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• 1991

This work aims to : establish a model of the container physical distribution system of Pusan port comprising 4 sub-systems of a navigational system, on-dock cargo handling/transfer/storage system, off-dock CY system and an in-land transport system : examine the system regarding the cargo handling capability of the port and analyse the cost of the physical distribution system. The overall findings are as follows : Firstly in the navigational system, average tonnage of the ships visiting the Busan container terminal was 33,055 GRT in 1990. The distribution of the arrival intervals of the ships' arriving at BCTOC was exponential distribution of $Y=e^{-x/5.52}$ with 95% confidence, whereas that of the ships service time was Erlangian distribution(K=4) with 95% confidence, Ships' arrival and service pattern at the terminal, therefore, was Poisson Input Erlangian Service, and ships' average waiting times was 28.55 hours In this case 8berths were required for the arriving ships to wait less than one hour. Secondly an annual container through put that can be handled by the 9cranes at the terminal was found to be 683,000 TEU in case ships waiting time is one hour and 806,000 TEU in case ships waiting is 2 hours in-port transfer capability was 913,000 TEU when berth occupancy rate(9) was 0.5. This means that there was heavy congestion in the port when considering the fact that a total amount of 1,300,000 TEU was handled in the terminal in 1990. Thirdly when the cost of port congestion was not considered optimum cargo volume to be handled by a ship at a time was 235.7 VAN. When the ships' waiting time was set at 1 hour, optimum annual cargo handling capacity at the terminal was calculated to be 386,070 VAN(609,990 TEU), whereas when the ships' waiting time was set at 2 hours, it was calculated to be 467,738 VAN(739,027 TEU). Fourthly, when the cost of port congestion was considered optimum cargo volume to be handled by a ship at a time was 314.5 VAN. When the ships' waiting time was set at I hour optimum annual cargo handling capacity at the terminal was calculated to be 388.416(613.697 TEU), whereas when the ships' waiting time was set 2 hours, it was calculated to be 462,381 VAN(730,562 TEU).

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.187-192
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• 2002

In this paper we consider an M/G/1 queueing system with vacation. The length of vacation period may be controlled by the waiting time of the first customer. The server goes on vacation as soon as the system is empty, and resumes service either when the waiting time of the leading customer reaches a predetermined value, or when the vacation period is expired, whichever comes first. We consider two types of vacation, say, multiple vacation type and N-policy type. We derive the steady-state distributions of the number of customers at arbitrary time and arbitrary customer's waiting time by means of decomposition property. Also, the mean lengths of busy period, idle period and a cycle time are given.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.137-156
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• 2016

This study analyzes the optimal service levels of exclusive container terminals in terms of the optimal berth occupancy rate and the ships' waiting ratios, based on the number of berths. We develop a simulation model using berth throughput data from pier P, Busan New Port, a representative port in Korea, and apply the simulation results to different numbers of berths. In addition to the above results, we analyze the financial data and costs of delayed ships and delayed cargoes for the past three years from the viewpoints of the terminal operation company (TOC), shipping companies, and shippers to identify the optimal service level for berth occupancy rates that generate the highest net profit. The results show that the optimal levels in the container terminal are a 63.4% berth occupancy rate and 10.6% ship waiting ratio in berth 4,66.0% and 9.6% in berth 5, and 69.0% and 8.5% in berth 6. However, the results of the 2013 study by the Ministry of Maritime Affairs and Fisheries showed significantly different optimal service levels: a 57.1% berth occupancy rate and 7.4% ship waiting ratio in berth 4; 63.4% and 6.6% in berth 5; and 66.6% and 5.6% in berth 6. This suggests that optimal service level could change depending on when the analysis is performed. In other words, factors affecting the optimal service levels include exchange rates, revenue, cost per TEU, inventory cost per TEU, and the oil price. Thus, optimal service levels can never be fixed. Therefore, the optimal service levels for container terminals need to be able to change relatively quickly, depending on factors such as fluctuations in the economy, the oil price, and exchange rates.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.589-595
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• 2017

We present an estimation model for optimal channel capacity required to data traffic transmission. The optimal channel capacity should be calculated in order to satisfy the permitted transmission delay of each wireless data services. Considering the discrete-time operation of digital communication systems and batch arrival of packet-switched traffic for various wireless services, $Geo^x$/G/1 non-preemptive priority queueing model is analyzed. Based on the heuristic interpretation of the mean waiting time, the mean waiting times of various data packets which have the service priority. Using the mean waiting times of service classes, we propose the procedure of determining the optimal channel capacity to satisfy the quality of service requirement of the mean delay of wireless services. We look forward to applying our results to improvement in wireless data services and economic operation of the network facilities.

• Journal of the Korea Society for Simulation
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• v.28 no.2
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• pp.119-129
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• 2019

Although line production systems with finite buffers have been studied over several decades, except for some special cases there are no explicit expressions for system performances such as waiting times(or response time) and blocking probability. Recently, a max-plus algebraic approach for buffer-sharing systems with constant processing times was introduced and it can lead to analytic expressions for (higher) moment and tail probability of stationary waiting. Theoretically this approach can be applied to general processing times, but it cannot give a proper way for computing performance measures. To this end, in this study we developed simulation models using @RISK software and the expressions derived from max-plus algebra, and computed and compared blocking probability, waiting time (or response time) with respect to two blocking policies: communication(BBS: Blocking Before Service) and production(BAS: Blocking After Service). Moreover, an optimization problem which determines the minimum shared-buffer capacity satisfying a predetermined QoS(quality of service) is also considered.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.219-224
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• 2011

To improve medical service and customers' satisfaction by reducing actual waiting time for medical inspection by personalized examination guidance contents (PEGC) linked with medical information system (MIS). The suggested PEGC was completed with preparing transmission for data and PEGC by using transmission control protocol internet protocol (TCP/IP) which can be built up creditable data transmission service. When patient signal event appeared in MIS while the communication was in standby, the ID and data of the customer were shown as text and image on monitors in waiting room. Guiding inspections, steps of examination, and undressing information extracted from the PEGC was also shown to the appropriate patient. After installation of the suggested PEGC, we rechecked satisfaction rates of 60 customers who visited the hospital with a same method as initial survey. The results of this study suggest that 3.6 point of waiting time satisfaction index were improved, after installation of the suggested PEGC, as 4.6 point.