• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.531-537
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• 2010

In this paper, some vacation policies are considered, which can be related to the past behavior of the system. The server, after serving all customers, stays idle or to wait for some time before a vacation is taken. General formulas for the waiting time and the amount of work in the system are derived for a vacation policy. Using the analysis on the vacation system, we derived the waiting time in the sequential bottleneck station.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.460-467
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• 2020

The medical service, accompanied by various disease and disease of modern people, must collect various information about patients through various tests, and demand continued to increase. As a result, interest in waiting time for medical services is rising, and various studies are underway to reduce waiting time. However, medical procedure is complex and diverse in the procedure of the services need to be conducted for each procedure. In this study, we are going to propose ways to improve the waiting time of the test procedures that are basically experienced by outpatients through the simulation experimental method. Based on the actual measurement data, the Arena Simulation program was used to implement the hospital's ophthalmic examination model and alternative model. Through this analysis, the waiting time of the examination procedure, the size of the queue, and density of the waiting room were analyzed.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.1
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• pp.53-63
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• 2018

In this paper, we analyze the waiting time of a queueing system with D-BMAP (discrete-time batch Markovian arrival process) and D-policy. Customer group or packets arrives at the system according to discrete-time Markovian arrival process, and an idle single server becomes busy when the total service time of waiting customer group exceeds the predetermined workload threshold D. Once the server starts busy period, the server provides service until there is no customer in the system. The steady-state waiting time distribution is derived in the form of a generating function. Mean waiting time is derived as a performance measure. Simulation is also performed for the purpose of verification and validation. Two simple numerical examples are shown.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.489-497
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• 2000

The M/M/1 queue with impatient customers is studied. Impatient customers wait for service only for limited time K/0 and leave the system if their services do not start during that time. Notice that in the analysis of virtual waiting time, the impatient customer can be considered as the customer who enters the system only when his/her waiting time does not exceed K. In this paper, we apply martingale methods to the virtual waiting time and obtain the expected period from origin to the point where the virtual waiting time crosses over K or reaches 0, and the variance of this period. With this results, we obtain the expected busy period of the queue, the distribution, expectation and variance of the number of times the virtual waiting time exceeding level K during a busy period, and the probability of there being no impatient customers in a busy period.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.2
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• pp.167-181
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• 1995

We develop an analytical model to estimate the time a workpiece spends in both input and output queues in trip-based material handling systems. The waiting times in the input queues are approximated by M/G/1 queueing system and the waiting times in the output queues are estimated using the method discussed in Bozer, Cho, and Srinivasan [2]. The analytical results are tested via simulation experiment. The result indicates that the analytical model estimates the expected waiting times in both the input and output queues fairly accurately. Furthermore, we observe that a workpiece spends more time waiting for a processor than waiting for a device even if the processors and the devices are equally utilized. It is also noted that the expected waiting time in the output queue with fewer faster devices is shorter than that obtained with multiple slower devices.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.270-286
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• 2017

Managing waiting experience is critical for providing service because waiting is an inevitable experience. We explored the factors which influence the waiting experience of the customers and the experience they face, in order to find out the meaning of waiting experience of the customer. As a result, we investigated a holistic sequence divided into four stages. When the customers wait for the service, perceived time would be increased by Zeigarnik effect. How long the customers feel when waiting is largely affected by a gap between waiting time and the perceived time, rather than the perceived time itself. Furthermore, the customers showed a tendency to have hard time immersing in the media due to considering others. Additionally, we could find that waiting time after giving an order is relatively more important than the waiting time before the order. Based on the results, the service provider should convey a feeling of completion by fitting the end point of the media to the point when the customer receives the service. In addition, high predictability needs to be offered for the customers in order to predict the exact waiting time. Finally, the customers should feel as if they take the initiative during waiting time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.18-29
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• 2022

Since the operation of an reft-turn actuated signal driven mainly by vehicles may increase the waiting time for walking, this signal causes inconvenience or stress to pedestrians. Therefore, in this study, the change in waiting time for walking before and after the application of an reft-turn actuated signal and the stress on the pedestrians were investigated through a questionnaire. The investigation showed that the waiting time for walking increased by 37% during non-peak time. Also the waiting time for walking of 62.1% of pedestrians became longer and 78% of them were stressed because of it. Meanwhile, simulation(VISSIM) showed that the vehicle travel speed slightly decreased to 1.07km/h(a 2.5% decrease), and the average waiting time for walking decreased by 15.51sec(a 28% decrease) with a pedestrian priority actuated signal. Therefore, it is expected that the pedestrian priority actuated signal can reduce the waiting time for walking and relieve pedestrian stress.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.161-165
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• 2005

In order to produce an optimal traffic cycle. We must first check how many waiting cars are at the lower intersection, because waiting queue is bigger than the length of upper traffic intersection. Start up delay time and vehicle waiting time occurs. To reduce vehicle waiting time, in this paper, we present an optimal green time algorithm using fuzzy neural network. Through computer simulation has been proven to be improved average vehicle speed than fixed traffic signal light which do not consider different intersection conditions.

• Quality Improvement in Health Care
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• v.3 no.1
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• pp.144-152
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• 1996

Background : Because of introduction of nationwide health care system in 1989 and the improvement of socioeconomic status of population the number of outpatient visiting university hospital has good facilities and manpower has increased. So the waiting time for medical service at university hospital are lengthened. Particularly outpatients complain that waiting for prescribed drugs at pharmacy depart are long. Reducing waiting time at pharmacy depart door-to-door delivery system that the patients applying for door-to-door delivery receive prescribed drug at home without waiting at pharmacy depart were studied. The objective of this study is to analysis the opinion of outpatients for door-to-door delivery system, to study the appropriateness of adopting the system and to produce ideal model for the system. Method : Outpatients waiting drug at pharmacy depart were questioned about door-to-door delivery system. to find the factors affect utilizing the system the logistic regression was used. Result : 83.3% of the patients want to utilize the system without charging, and 72.9% of the patients want to utilized system with charging. 68.3% of patients with charging want to use this system because of long waiting time at pharmacy depart. 50% of patients who do not want to use door-to-door do not use this system because of incorrect delivery. The affecting factors to utilize the system were sex, waiting time, fee. Conclusion : The model for door to door delivery system. 1. door-to-door personnel reside in hospital and the patient want to utilize the system apply for the delivery with charging. 2. The applied drugs dispense at spare time. 3. Delivery company gathers drug at appointed time and delivers. 4. The delivery fee is 2,000-3,000 won. 5. To prevent from loss and changing the drug the name of patient on packet are printed and drug packet are sealed. 6. The company submit the confirm sheet which are written that the patient received drug correctly to hospital. 7. The delivery time of drug is reserved for the convenience of receiving.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.523-533
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• 1999

We consider the M/G/1 queueing model with D-policy. The server is turned off at the end of each busy period and is activated again only when the sum of the service times of all waiting customers exceeds a fixed value D. We obtain the distribution of unfinished work and show that the unfinished work decomposes into two random variables, one of which is the unfinished work of ordinary M/G/1 queue. We also derive the distribution of queue waiting time.