인명 사고, 차량 화재, 자연재해, 돌발상황 등 긴급상황 발생 시 신속한 대응을 위해 영향권을 정확하게 분석하는 것이 필수적이다. 특히 도로망을 중심으로 한 영향권은 해당 도로의 교통량, 통제 차로 수, 사고 처리 시간에 따라 달라지며, 이를 관리하기 위한 대책 이 필요하다. 따라서 본 연구는 향후 도로 관리 대책 및 의사결정을 지원하기 위해 대기행렬이론을 기반으로 긴급상황 영향권을 분석 하였다. 본 연구에서 영향권은 공간적인 개념으로서 사고 발생지점으로부터 해당 도로 진행 방향 기준 후방으로 도로 소통에 영향을 끼치는 거리로 정의하였다. 사고 발생지점, 사고 발생 링크의 교통량, 차로 수 및 통제 차로 수, 사고 발생 및 사고 종료 시점 등의 변수를 입력 데이터로 설정하였고 긴급상황으로 인해 발생하는 대기행렬길이를 파악하기 위해 대기행렬이론을 적용하였다. 돌발상황 정보를 분석하여 사고 지속시간의 범위를 도출하였으며 이를 기반으로 여러 가지 상황별 영향권을 산출하였다. 유스 케이스별 영향권 산출을 통해 교통량, 이용 가능한 차로 수, 사고 지속시간 각각이 영향권과 어떠한 관계가 있는지 확인할 수 있었다. 본 연구에서 설정한 영향권을 통해 실시간 교통 데이터를 활용하여 유사 상황에서의 영향권을 신속히 파악할 수 있다. 이는 교통사고와 같은 긴급 상황 발생 시 신속하고 정확한 영향권 파악으로 현장 대응 및 의사결정 지원 시스템의 효율성을 높이는 데 중요한 역할을 할 것으로 기대된다.

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.

COVID-19 has been spreading all around the world, and threatening global health. In this situation, identifying and isolating infected individuals rapidly has been one of the most important measures to contain the epidemic. However, the standard diagnosis procedure with RT-PCR (Reverse Transcriptase Polymerase Chain Reaction) is costly and time-consuming. For this reason, pooled testing for COVID-19 has been proposed from the early stage of the COVID-19 pandemic to reduce the cost and time of identifying the COVID-19 infection. For pooled testing, how many samples are tested in group is the most significant factor to the performance of the test system. When the arrivals of test requirements and the test time are stochastic, batch-service queueing models have been utilized for the analysis of pooled-testing systems. However, most of them do not consider the false-negative test results of pooled testing in their performance analysis. For the COVID-19 RT-PCR test, there is a small but certain possibility of false-negative test results, and the group-test size affects not only the time and cost of pooled testing, but also the false-negative rate of pooled testing, which is a significant concern to public health authorities. In this study, we analyze the performance of COVID-19 pooled-testing systems with false-negative test results. To do this, we first formulate the COVID-19 pooled-testing systems with false negatives as a batch-service queuing model, and then obtain the performance measures such as the expected number of test requirements in the system, the expected number of RP-PCR tests for a test sample, the false-negative group-test rate, and the total cost per unit time, using the queueing analysis. We also present a numerical example to demonstrate the applicability of our analysis, and draw a couple of implications for COVID-19 pooled testing.

In this paper, we present a new way to derive the mean cycle time of the G/G/m failure prone queue when the loading of the system approaches to zero. The loading is the relative ratio of the arrival rate to the service rate multiplied by the number of servers. The system with low loading means the busy fraction of the system is low. The queueing system with low loading can be found in the semiconductor manufacturing process. Cluster tools in semiconductor manufacturing need a setup whenever the types of two successive lots are different. To setup a cluster tool, all wafers of preceding lot should be removed. Then, the waiting time of the next lot is zero excluding the setup time. This kind of situation can be regarded as the system with low loading. By employing absorbing Markov chain model and renewal theory, we propose a new way to derive the exact mean cycle time. In addition, using the proposed method, we present the cycle times of other types of queueing systems. For a queueing model with phase type service time distribution, we can obtain a two dimensional Markov chain model, which leads us to calculate the exact cycle time. The results also can be applied to a queueing model with batch arrivals. Our results can be employed to test the accuracy of existing or newly developed approximation methods. Furthermore, we provide intuitive interpretations to the results regarding the expected waiting time. The intuitive interpretations can be used to understand logically the characteristics of systems with low loading.

Different from general operating policies to be applied for controllable queueing models, two of three well-known simple N, T and D operating policies are applied alternatingly to the single server controllable queueing models, so called alternating (NT), (ND) and (TD) policies. For example, the alternating (ND) operating policy is defined as the busy period is initiated by the simple N operating policy first, then the next busy period is initiated by the simple D operating policy and repeats the same sequence after that continuously. Because of newly designed operating policies, important system characteristic such as the expected busy and idle periods, the expected busy cycle, the expected number of customers in the system and so on should be redefined. That is, the expected busy and idle periods are redefined as the sum of the corresponding expected busy periods and idle periods initiated by both one of the two simple operating policies and the remaining simple operating policy, respectively. The expected number of customers in the system is represented by the weighted or pooled average of both expected number of customers in the system when the predetermined two simple operating policies are applied in sequence repeatedly. In particular, the expected number of customers in the system could be used to derive the expected waiting time in the queue or system by applying the famous Little’s formulas. Most of such system characteristics derived would play important roles to construct the total cost functions per unit time for determination of the optimal operating policies by defining appropriate cost elements to operate the desired queueing systems.

A steady-state controllable M/G/1 queueing model operating under the {T:Min(T,N)} policy is considered where the {T:Min(T,N)} policy is defined as the next busy period will be initiated either after T time units elapsed from the end of the previous busy period if at least one customer arrives at the system during that time period, or after T time units elapsed without a customer’ arrival, the time instant when Nth customer arrives at the system or T time units elapsed with at least one customer arrives at the system whichever comes first. After deriving the necessary system characteristics including the expected number of customers in the system, the expected length of busy period and so on, the total expected cost function per unit time for the system operation is constructed to determine the optimal operating policy. To do so, the cost elements associated with such system characteristics including the customers’ waiting cost in the system and the server’s removal and activating cost are defined. Then, procedures to determine the optimal values of the decision variables included in the operating policy are provided based on minimizing the total expected cost function per unit time to operate the queueing system under considerations.

A steady-state controllable M/G/1 queueing model operating under the (TN) policy is considered where the (TN) policy is defined as the next busy period will be initiated either after T time units elapsed from the end of the previous busy period if at least one customer arrives at the system during that time period, or the time instant when Nth customer arrives at the system after T time units elapsed without customers’ arrivals during that time period. After deriving the necessary system characteristics such as the expected number of customers in the system, the expected length of busy period and so on, the total expected cost function per unit time in the system operation is constructed to determine the optimal operating policy. To do so, the cost elements associated with such system characteristics including the customers’ waiting cost in the system and the server’s removal and activating cost are defined. Then, the optimal values of the decision variables included in the operating policies are determined by minimizing the total expected cost function per unit time to operate the system under consideration.

Using the known result of the expected busy period for a controllable M/G/1 queueing model operating under the triadic Max (N, T, D) policy, its upper and lower bounds are derived to approximate its corresponding actual value. Both bounds are represented

Us ing the known result of the expected bllsy period for the triadic Min (N, T, 0) operating po licy applied to a controllable M/GI1 queueing model, its upper and lower bounds are derived to approximate its corresponding ac tual value. 80th bounds are rep

Using the results of the expected busy periods for the dyadic Min(N, D) and Max(N, D) operating policies in a controllable M/G/1 queueing model, an important relation between them is derived. The derived relation represents the complementary property betw

The expected busy period for the controllable M/G/1 queueing model operating under the triadic Max (N, T, D) policy is derived by using a new concept so called “the pseudo probability density function.” In order to justify the proposed approaches for the

  The BMAP/M/N/0 queueing system operating in Markovian random environment is investigated. The stationary distribution of the system is derived. Loss probability and other performance measures of the system also are calculated. Numerical experiments whic

본 논문에서는 통신망의 트래픽 제어를 위한 무한버퍼, 단일 서버와 배치도착과정을 갖는 대기행렬 모형을 고려하였다. 또한 고객 도착 형태와 서비스의 분포는 지수분포를 배치 흐름은 포아송 정상과정 및 배치크기는 기하분포를 따른다고 가정하였다. 서비스를 받기 위해 시스템으로 들어오는 배치의 크기는 시스템의 상태에 따라서 트래픽 제어가 가능하다. 이와 같은 조건에서 시스템에 있는 고객의 수와 배치크기에 대한 결합 확률분포를 분석하였고, 행렬기법을 적용하여 시스

  The expected busy period for the controllable M/G/1 queueing model operating under the triadic policy is derived by using the pseudo probability density function which is totally different from the actual probability density function. In order to justif

Two-stage multi-server tandem queue with two types of processed customers is analyzed. Priority customers always are admitted into the system. Non-priority customers are admitted into the system only if the number of busy servers at the second stage does not exceed some pre-assigned threshold. Economic criterion of the system operation is optimized with respect to the threshold.

  A multi-server queueing system with finite buffer is considered. The input flow is the BMAP (Batch Markovian Arrival Process). The service time has the PH (Phase) type distribution. Customers from the BMAP enter the system according to the discipline of

  대기행렬 모형은 통신시스템이나 통신망 구현에 가장 적합한 수리모형으로 알려져 있고, 이에 대한 연구가 상당히 많이 진행되고 있다. 본 논문에서는 재해가 발생될 수 있는 BMAP/SM/1 대기시스템으로, 재해가 발생했을 경우 시스템 복구가 즉시 이루어지지 않고 임의 시간 후 복구 되는 시스템을 고려대상으로 하고 있다. 시스템의 정보입력흐름은 상호종속 또는 그룹 입력이 허용되는 배치마코프 도착과정으로 가정하였고, 또한 서비스분포는 세미 마코프 프로세스를

  본 논문에서는 재시도와 완전입력 규칙을 갖는 BMAP/PH/0 대기시스템에 대한 주요 성능평가척도와 시스템의 정상상태 조건을 제시한다. 고려되는 시스템은 모든 서버가 서비스를 하고 있을 경우 도착이 이루어지는 배치도착은 모두 손실되며, 반대의 경우 도착하는 배치는 서비스를 받기 위해 시스템에 들어가게 된다. 만약 쉬고 있는 서버의 수가 불충분하여 배치의 일부가 즉각 서비스를 받을 수 없다면, 일단 오빗으로 이동하고 표준 재시도 대기시스템의 규칙에 따

This paper studies procedures for bottleneck detection in closed queueing networks(CQN's) with multiple job classes. Bottlenecks refer to servers operating at 100% utilization. For CQN's, this can occur as the population sizes approach infinity. Bottlenec