• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.53-60
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• 2022

In a group-testing method, instead of testing a sample, for example, blood individually, a batch of samples are pooled and tested simultaneously. If the pooled test is positive (or defective), each sample is tested individually. However, if negative (or good), the test is terminated at one pooled test because all samples in the batch are negative. This paper considers a queueing system with a two-stage group-testing policy. Samples arrive at the system according to a Poisson process. The system has a single server which starts a two-stage group test in a batch whenever the number of samples in the system reaches exactly a predetermined size. In the first stage, samples are pooled and tested simultaneously. If the pooled test is negative, the test is terminated. However, if positive, the samples are divided into two equally sized subgroups and each subgroup is applied to a group test in the second stage, respectively. The server performs pooled tests and individual tests sequentially. The testing time of a sample and a batch follow general distributions, respectively. In this paper, we derive the steady-state probability generating function of the system size at an arbitrary time, applying a bulk queuing model. In addition, we present queuing performance metrics such as the offered load, output rate, allowable input rate, and mean waiting time. In numerical examples with various prevalence rates, we show that the second-stage group-testing system can be more efficient than a one-stage group-testing system or an individual-testing system in terms of the allowable input rates and the waiting time. The two-stage group-testing system considered in this paper is very simple, so it is expected to be applicable in the field of COVID-19.

• 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 the Korean Mathematical Society
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• v.35 no.3
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• pp.503-525
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• 1998

In the theory of retrial queues it is usually assumed that the flow of primary customers is Poisson. This means that the number of independent sources, or potential customers, is infinite and each of them generates primary arrivals very seldom. We consider now retrial queueing systems with a homogeneous population, that is, we assume that a finite number K of identical sources generates the so called quasi-random input. We present a survey of the main results and mathematical tools for finite source retrial queues, concentrating on M/G/1//K and M/M/c//K systems with repeated attempts.

• Proceedings of the Safety Management and Science Conference
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• 2003.11a
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• pp.271-284
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• 2003

We developing for Gate-Container Yard model to execute the Input & Output process of containers which are waiting in container yard effectively and fast. The optimal model is developed to manage and trace the containers in yard. We developed the put-away strategies which considered the storage time for volume of transportation and the distance between blocks and gates using the MICRO-CRAFT (Computerized Relative Allocation Facilities Technique).

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.2
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• pp.45-65
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• 1997

We build a queueing model for buffered leaky bucket system. First, we set up system equations and them calculate the steady-state probabilities at an arbitrary time epoch by recursive method. We derive the mean waiting time and the mean number of cells in the input buffer, and evaluate the performance of the buffered leaky bucket system to find the optimal queue capacity and token generation rate that meet the quality of service(QoS).

• Journal of the Korea Society for Simulation
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• v.8 no.4
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• pp.61-72
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• 1999

This paper presents an estimation method of container handling capacity and selection of resource allocation strategies of container terminals using the computer simulation models. Simulation models are developed to model container terminal consisting of 4 berths considering the berth allocation strategies, crane allocation strategies and the total number of container cranes using Arena simulation package. The proposed models do not consider the yard operations and gate operations. All the input parameters for the models are estimated on the basis of the existing container terminal operation data and the planning data for the automated container terminal planned by Korean government. Four berth allocation strategies and three crane allocation strategies are considered. The total number of container cranes considered ranges from 12 to 15. Non-terminating simulation techniques are utilized for the performance comparison among alternatives. The performance measures such as average ship turnaround time, average ship waiting time, average ship service time, the number of containers handled per year, and the number of ships processed per year are used. The result shows that the berth allocation strategy minimizing the sum of the number of ships waiting, the number of busy container cranes and number of ships handled performs better than any other berth allocation strategies. In addition, the crane allocation strategy allocating up to 5 container cranes per berth performs better than any other crane allocation strategies. Finally there are no significant performance differences among the alternatives consisting of different total number of container cranes allocated.

• Journal of KIISE:Information Networking
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• v.27 no.1
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• pp.39-47
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• 2000

This paper is to propose an efficient and reliable control procedure of Base Station Controller of mobile communication system for bulk input messages which was delivered instantaneously. We divided the input messages that are processed by the base station controller of the mobile communication system into two things ; they are messages related to call connection and o&m(operation and maintenance). In addition, we analyzed the properties of the input messages and then performed computer simulation on each input message by using M/M/1/K queueing model in term of the following viewpoints : the loss probability of input messages, the average queue length, the utilization of process controller(server),the average waiting time in queue. And we compared the performance of the two input messages in the overload controlling which was caused by the congested input messages.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.63-71
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• 1986

In the organization with a hierarchical network queue structure a production function is derived whose input factors are the numbers of servers at nodes and output is the number of served customers. Its useful properties are investigated. Using this production function, the contributions of servers to the number of served customers are studied. Also given an expected waiting time in the system for each customer, the optimal numbers of servers at nodes are obtained minimizing a cost function.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2254-2259
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• 2011

The introduction of Personal Rapid Transit (PRT) has been widely discussed in the Korean transportation research field. However, there is no robust criterion to derive the throughput of cars and passengers at PRT stations, which plays a primary role in determining the overall capacity of PRT systems. The present study provided a methodology to rigorously compute the capacity for simple-serial PRT stations with a single platform, considering three decisive factors, i.e., the demand level of incoming cars and outgoing passengers, the station structure, and the operation strategy. A micro-level simulator was developed for the analysis of station capacity. And, by using this, station capacities were presented for various combinations of the decisive factors. In particular, the relationship between capacity and station structure was investigated in detail. Station structure is represented by the numbers of platform berths, input queue berths, and output queue berths. Moreover, both waive rate and waiting time, which represent the level of passenger service, were taken into account when the station throughput was computed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.643-646
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• 2002

In this paper, we analyse the performance of the system connected with several networks, that is, the system has one bulk input and several server by using queueing model - G $I^{⒳}$/M/c/N. in this paper we investigate some parameters from steady state probability and examine the average waiting time and blocking probability from those parameters.s.