• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1622-1631
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• 2007

To realize the wireless packet scheduler which efficiently considers both the effect of adaptive modulation and coding (AMC) scheme due to variable wireless communication channel information from physical layer and the QoS differentiation of multimedia services from internet protocol (IP) layer, this paper proposes a new downlink AMC-based priority queuing (APQ) scheduler which combines AMC scheme and service priority method in multimedia services at the same time. The result of numerical analysis shows that the proposed APQ algorithm plays a role in increasing the number of services satisfying the mean waiting time requirements per each service in multimedia services because the APQ scheme allows the mean waiting time of each service to be reduced much more than existing packet scheduler having only user selection processor.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.4
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• pp.390-396
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• 2002

We present a meaningful interpretation of the equations for the steady-state queue length distribution of the GI/GI/c/K queue so that the equations are better understood and become more applicable. As a byproduct, we present an exact expression of the mean queue waiting time for the M/GI/c queue.

• Quality Improvement in Health Care
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• v.5 no.2
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• pp.202-215
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• 1998

Purpose : To shorten processing time for variety of medical affairs of the patient at the outpatient clinic of a big hospital is very important to qualify medical care of the patient. Therefore, patient's waiting time for drug delivery after doctor's prescription is often utilized as a strong tool to evaluate patient satisfaction with a medical care provided. We performed this study to investigate factors influencing patient satisfaction related with waiting time for drug delivery. Methods : The data were collected from July 21 to August 12, 1998. A total 535 patients or their families who visited outpatient clinics of Inha University Hospital were subjected to evaluate the drug delivery time and the level of their satisfaction related, which were compared with those objectively evaluated by Quality Improvement Team. The reliability of the scale was tested with Cronbach's alpha, and the data were analyzed using frequency, t-test, ANOVA, correlation analysis and multiple regression. Results : The mean drug delivery time subjectively evaluated by the patient (16.1 13.0 min) was longer than that objectively evaluated (10.9 7.6 min) by 5.2 min. Drug delivery time objectively evaluated was influenced by the prescription contents, total amount or type of drug dispensed, etc, as expected. The time discrepancy between two evaluations was influenced by several causative factors. One of those proved to be a patient's late response to the information from the pharmacy which the drug is ready to deliver. Interestingly, this discrepancy was found to be more prominent especially when waiting place for drug delivery was not less crowded. Other factors, pharmaceutical counseling at the pharmacy, emotional status or behavior of a patient while he waits for the medicine, were also found to influence the time subjectively evaluated. Regarding the degree of patient satisfaction with the drug delivery, majority of patients accepted drug delivery time with less than 10 min. It was also found to be influenced by emotional status of the patient as well as kindness or activity of pharmaceutical counselor. Conclusion : The results show that, besides prescription contents, behavior pattern or emotional status of a patient, environment of the waiting place, and quality of pharmaceutical counseling at the pharmacy, may influence the patient's subjective evaluation of waiting time for drug delivery and his satisfaction related with the service in the big hospital. In order to improve patient satisfaction related with waiting time for drug delivery, it will be cost effective to qualify pharmaceutical counseling and information system at the drug delivery site or waiting place rather than to shorten the real processing time within the pharmacy.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.809-817
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• 1998

In this paper we consider a polling system where the token is passed according to a general service order table. We derive an exact and explicit formula to compute the mean waiting time for a message when the arrivals of messages are modeled by batch Poisson processes.

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.355-363
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• 1997

In queueing theory, polling systems have been widely studied as a way of serving several stations in cyclic order. In this paper we consider Markov-modulated Poisson process which is useful for approximating a superposition of heterogeneous arrivals. We derive the mean waiting time of each station in a polling system where the arrival process is modeled by a Markov-modulated Poisson process.

• IE interfaces
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• v.25 no.3
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• pp.283-289
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• 2012

M/G/1 queue with server vacations period depending on the previous vacation and customer's arrival is considered. Most existing studies on M/G/1 queue with server vacations assume that server vacations are independent of customers' arrival. However, some vacations are terminated by some class of customers' arrival in certain queueing systems. In this paper, therefore, we investigate M/G/1 queue with server vacations where each vacation period has different distribution and vacation length is influenced by customers' arrival. Laplace-Stieltjes transform of the waiting time distribution and the distribution of number of customers waiting for each class of customers are respectively derived. As performance measures, mean waiting time and average number of customers waiting for each class of customers are also derived.

• 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).

• Health Policy and Management
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• v.19 no.3
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• pp.27-44
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• 2009

The length of stay in emergency departments has been used as a quality indicator to reflect the overall efficiency of emergency care. Identifying characteristics associated with length of stay is critical to monitor overcrowding and improve efficient throughput function of emergency departments. This study examined the level of waiting time for initial assessment by physician and length of stay in emergency departments. Furthermore, we investigated the characteristics of patients' attendance associated with length of stay. An observational study was performed for a sample of 1,526 patients visiting ten nation-wide emergency departments. A structured form was designed to collect information about patients' demographics, route of admission, time and mode of arrival, triage level, cause of attendance, initial assessment time by physician, departure time, and disposition. Multiple regression analysis was performed to determine factors associated with length of stay. The average length of stay was 209.4 minutes (95% confidence interval [CI]=197.1-221.7), with a mean waiting time for initial assessment of 5.9 minutes (95% CI=5.1-6.7). After controlling for emergency department characteristics, increasing age, longer waiting times, attendance due to diseases, higher acuity, multiple diagnoses($\geq$2) and requiring admission or transfer to other health care facilities were positively associated with length of stay in emergency departments. The findings suggest that both patients' characteristics and the flow between emergency departments and parent hospitals should be taken into account in predicting length of stay in emergency departments.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.2829-2834
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• 2013

The objective was to analyze the radiotherapy (RT) practice at the cancer centre of a tertiary academic medical institution in Delhi. This audit from an Indian public institution covered patient care processes related to cancer diagnosis, integration of RT with other anti-cancer modalities, waiting time, overall treatment time, and compliance with RT. Over a period of one year, all consecutively registered patients in radiotherapy were analyzed for the audit cycle. Analysis of 1,030 patients showed median age of 49.6 years, with presentation as stage I and II in 14.2%, stage III and IV in 71.2% and unknown stage in 14.6%. A total of 974 (95%) were advised for RT appointment; 669 (68.6%) for curative intent and 31.4% for palliation. Mean times for diagnostic workup and from registration at cancer centre to radiotherapy referral were 33 and 31 days respectively. Median waiting time to start of RT course was 41 days. Overall RT compliance was 75% and overall duration for a curative RT course ranged from 50 days to 61 days. Non-completion and interruption of RT course were observed in 12% and 13% respectively. Radiotherapy machine burden in a public cancer hospital in India increases the waiting time and 25% of advised patients do not comply with the prescribed treatment. Infrastructure, machine and manpower constraints lead to more patients being treated on cobalt (74%) and by two-dimensional (78%) techniques.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.1-10
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• 2016

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.