• Title/Summary/Keyword: Waiting Time

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Using Simulation to Predict the Number of Recovery Bed and Waiting Time as Increasing Client for Sleep Endoscopy Check in Health Service Center (건강검진센터에서 위장 및 대장 수면 내시경 검사 증가에 따른 필요 회복실 침상 수 및 대기 시간 예측 시뮬레이션)

  • Lee, Hee-Joo
    • The Journal of the Korea Contents Association
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    • v.10 no.2
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    • pp.35-42
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    • 2010
  • The increasing regular medical check up rate for early diagnosis in disease has increasing sleep endoscopy rate because of reduction with discomfort. The purpose of this study was to determine the number of recovery bed as increasing sleep endoscopy rate using check up time, waiting time & recovery time at a general hospital in Seoul. This study was analyzed using ARENA 10.0 program. At present and as increasing of sleep endoscopy rate 10%, 20% was increased recovery time, waiting time & the rate of bed inflection. So at present, the number of recovery bed has to increase for client's safety and as increasing of sleep endoscopy rate 10%, 20% has to increase 3 and then waiting time decreased in 2 minutes.

Stationary Waiting Times in m-node Tandem Queues with Communication Blocking

  • Seo, Dong-Won;Lee, Ho-Chang;Ko, Sung-Seok
    • Management Science and Financial Engineering
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    • v.14 no.1
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    • pp.23-34
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    • 2008
  • In this study, we consider stationary waiting times in a Poisson driven single-server m-node queues in series. We assume that service times at nodes are independent, and are either deterministic or non-overlapped. Each node excluding the first node has a finite waiting line and every node is operated under a FIFO service discipline and a communication blocking policy (blocking before service). By applying (max, +)-algebra to a corresponding stochastic event graph, a special case of timed Petri nets, we derive the explicit expressions for stationary waiting times at all areas, which are functions of finite buffer capacities. These expressions allow us to compute the performance measures of interest such as mean, higher moments, or tail probability of waiting time. Moreover, as applications of these results, we introduce optimization problems which determine either the biggest arrival rate or the smallest buffer capacities satisfying probabilistic constraints on waiting times. These results can be also applied to bounds of waiting times in more general systems. Numerical examples are also provided.

NFC based Smart Waiting Number Ticket Issuing System (NFC를 이용한 스마트 대기표발권시스템 설계)

  • Kang, Su-kyoung;Kim, Chang-jae;Lee, Nam-young
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2014.10a
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    • pp.940-943
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    • 2014
  • Currently in many places, such as a business or government office, customers are spending many time after issuing waiting number ticket. To improve this, this paper proposes new design of waiting number ticket issuing system which provides real-time notification service with smartphone. This system add the NFC tag to the issuing machine in order to input the information of waiting and increase the convenience of users and provides an integrated management of corporate and government information using a waiting number ticket. Therefore, it is possible to efficiently use the waiting time than when using the existing ticketing system by taking advantage of this system, and appears to be to reduce costs and environmental protection, by not using the paper ticket. Also this system can be used to improve business processes through the application Waiting Number Ticket ticket cancellation feature.

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Development of an Algorithm for Minimization of Passengers' Waiting Time Using Smart Card Data (교통카드 데이터를 이용한 버스 승객 대기시간 최소화 알고리즘 개발)

  • Jeon, Sangwoo;Lee, Jeongwoo;Jun, Chulmin
    • Spatial Information Research
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    • v.22 no.5
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    • pp.65-75
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    • 2014
  • Bus headway plays an important role not only in determining the passenger waiting time and bus service quality, but also in influencing the bus operation cost and passenger demand. Previous research on headway control has considered only an hourly difference in the distribution of ridership between peak and non-peak hours. However, this approach is too simple to help manage ridership demand fluctuations in a short time scale; thus passengers' waiting cost will be generated when ridership demand exceeds the supply of bus services. Moreover, bus ridership demand varies by station location and traffic situation. To address this concern, we propose a headway control algorithm for minimizing the waiting time cost by using Smart Card data. We also provide proof of the convergence of the algorithm to the desired headway allocation using a set of preconditions of political waiting time guarantees and available fleet constraints. For model verification, the data from the No. 143 bus line in Seoul were used. The results show that the total savings in cost totaled approximately 600,000 won per day when we apply the time-value cost of waiting time. Thus, we can expect that cost savings will be more pronounced when the algorithm is applied to larger systems.

A studying example on simulation of the Queuing system of the window Box (窓口Queuing System의 Simulation에 관한 事例 硏究)

  • 양해술
    • Communications of the Korean Institute of Information Scientists and Engineers
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    • v.5 no.2
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    • pp.49-60
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    • 1987
  • To decide on the queuing system of the optimum-sized bank window, data by means of simulation was reckoned. That is, by linking the average arrival rate and the average service rate with the exponential random number, customers' arrival time and service time was reckoned and simulation size optionally decided. By so doing, this paper is aimed at predicting the conditions of a bank, average arrival time, average waiting time, aveerage service time, average queuing length, servers' idle time, etd, and at preparing for a simulation model of the queuing system that can apply not only to the bank window box but also to all system under which queuing phenomena may arise.

Non-periodic Subway Scheduling that Minimizes Operational Cost and Passenger Waiting Time

  • Hong, YunWoo;Chung, Yerim;Min, YunHong
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.8
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    • pp.133-142
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    • 2018
  • Subway metro scheduling is one of the most important problems impacting passenger convenience today. To operate efficiently, the Seoul metro uses regular, periodic schedules for all lanes, both north and southbound. However, many past studies suggest that non-periodic scheduling would better optimize costs. Since the Seoul metro is continuously facing a deficit, adopting a non-periodic schedule may be necessary. Two objectives are presented; the first, to minimize the average passengers' waiting time, and the second, to minimize total costs, the sum of the passenger waiting time, and the operational costs. In this paper, we use passenger smart card data and a precise estimation of transfer times. To find the optimal time-table, a genetic algorithm is used to find the best solution for both objectives. Using Python 3.5 for the analysis, for the first objective, we are able to reduce the average waiting time, even when there are fewer trains. For the second objective, we are able to save about 4.5 thousand USD with six fewer trains.

Performance analysis of mini MAP architecture in real time application by simulation method (실시간 응용시 Mini MAP의 시뮬레이션에 의한 성능해석에 관한 연구)

  • 김덕우;정범진;권욱현
    • 제어로봇시스템학회:학술대회논문집
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    • 1987.10b
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    • pp.382-388
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    • 1987
  • In this paper, the performance of Mini MAP architecture is analyzed by simulation method. Token rotation time and waiting time are obtained by simulation. The results of the simulation are compared with these of the analytic model. From these comparisons, it is shown that simulation results are approximately identical to analytic results. Mini MAP architecture has good real time performances in token rotation time and waiting time and can be used to many real time applications.

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A Study on the Analysis of Container Physical Distribution System -Pusan Port Oriented- (물류시스템 분석에 관한 연구 - 부산항을 중심으로 -)

  • Park, C.H.;Lee, C.Y.
    • 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).

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Test of Independence in a Markov Dependent Waiting-time Distribution

  • Bai, Do-Sun
    • Journal of Korean Institute of Industrial Engineers
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    • v.1 no.1
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    • pp.99-103
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    • 1975
  • A procedure for the test of independence of the observations and the null distribution are studied for a waiting-time distribution of the number of Bernoulli trials required to obtain a preassigned number of successes under Markov dependence. Selected critical values for the test statistic are tabulated.

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A RECENT PROGRESS IN ALGORITHMIC ANALYSIS OF FIFO QUEUES WITH MARKOVIAN ARRIVAL STEAMS

  • Takine, Tetsuya
    • Journal of the Korean Mathematical Society
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    • v.38 no.4
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    • pp.807-842
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    • 2001
  • This paper summarizes recent development of analytical and algorithmical results for stationary FIFO queues with multiple Markovian arrival streams, where service time distributions are general and they may differ for different arrival streams. While this kind of queues naturally arises in considering queues with a superposition of independent phase-type arrivals, the conventional approach based on the queue length dynamics (i.e., M/G/1 pradigm) is not applicable to this kind of queues. On the contrary, the workload process has a Markovian property, so that it is analytically tractable. This paper first reviews the results for the stationary distributions of the amount of work-in-system, actual waiting time and sojourn time, all of which were obtained in the last six years by the author. Further this paper shows an alternative approach, recently developed by the author, to analyze the joint queue length distribution based on the waiting time distribution. An emphasis is placed on how to construct a numerically feasible recursion to compute the stationary queue length mass function.

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