• Proceedings of the Korean Operations and Management Science Society Conference
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• 한국경영과학회 2006년도 추계학술대회
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• pp.493-505
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• 2006

Call center staffing problems have often relied upon queueing models, which are traditionally used to compute average call waiting time. However, the relationship between the in-bound call volume and call abandon rate is not directly explained even with the complex queueing formula while that relationship is a major interest to the hospital due to profitability. In this paper we provide a novel approach for the call center staffing problem by incorporating the relationship between the in-bound call volume and call abandon rate with a nonlinear integer programming, rather than using the traditional queueing model. We perform numerical analyses with actual data obtained from a reservation call center in a hospital.

• Proceedings of the Korea Society for Simulation Conference
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• 한국시뮬레이션학회 2003년도 추계학술대회 및 정기총회
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• pp.75-85
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• 2003

With recent advances in technology and the changing nature of business, call center management has become a rapidly growing industry. However theoretical analysis about the call center system is very difficult, and the forecasting of call volume also. In the situation, it is significant that we study call-flow system, design system model and perform simulation. If these are possible, it is able to control the staff schedule and the resource management efficiently. This study introduces the process of applying the call center to simulation. So, it is feasible to break from the intuitive management by a minority manager and analyze it scientifically. The enterprise can reduce unnecessary expense, make an offer high quality to user in a keen competition

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.808-813
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• 2011

There are hundreds of thousands call center workers wearing acoustic device. However, researches and noise exposure measurements on the noise transmitted from acoustic devices have seldom been performed due to the difficulty of measurement and to the absence of the measuring method in Korea. The aim of this study is to set up management measures to protect hearing loss on the call operator by acquiring measurement data of noise transmitted from the headset Noise exposure measurements of 17 operators were performed in 7 call centers and Head and Torso Simulator method in compliance with the ISO Standard 11904-2 was used for the measurement of noise transmitted from the headset Sound pressure levels(SPL) transmitted from the headset were 73.2~86 dB(A). The operator exposed to the highest SPL set up his volume control at 9 which was the highest volume level. The volume control level, adjustable from 1 to 9, could be identified 12 out of 17 operators and the range of volume levels was 4.5~9. As a result of Pearson Correlation Analysis, the correlation between volume level and SPL transmitted from the headset showed high relation as significance at the 0.672 level(p<0.05). To protect hearing loss of call center operators, it is more practical and effective measure to limit the volume level below the noise exposure level, i.e. 85 dB(A), rather than to carry out noise monitoring considering cost-effective aspect.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• 제21권7호
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• pp.615-621
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• 2011

There are hundreds of thousands call center workers wearing acoustic device. However, researches and noise exposure measurements on the noise transmitted from acoustic devices have seldom been performed due to the difficulty of measurement and to the absence of the measuring method in Korea. The aim of this study is to set up management measures to protect hearing loss on the call operator by acquiring measurement data of noise transmitted from the headset. Noise exposure measurements of 17 operators were performed in 7 call centers and head and Torso simulator method in compliance with the ISO standard 11904-2 was used for the measurement of noise transmitted from the headset. Sound pressure levels(SPL) transmitted from the headset were 73.2~86 dB(A). The operator exposed to the highest SPL set up his volume control at 9 which was the highest volume level. The volume control level, adjustable from 1 to 9, could be identified 12 out of 17 operators and the range of volume levels was 4.5~9. As a result of pearson correlation analysis, the correlation between volume level and SPL transmitted from the headset showed high relation as significance at the 0.672 level(p<0.05). To protect hearing loss of call center operators, it is more practical and effective measure to limit the volume level below the noise exposure level, i.e. 85 dB(A), rather than to carry out noise monitoring considering cost-effective aspect.

• Journal of Advanced Information Technology and Convergence
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• 제10권2호
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• pp.49-59
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• 2020

As technologies have been developing, our lives are getting easier. Today we are surrounded by the new technologies such as AI and IoT. Moreover, the word, "smart" is a very broad one because we are trying to change our daily environment into smart one by using those technologies. For example, the traditional workplaces have changed into smart offices. Since the 3rd industrial revolution, we have used the touch interface to operate the machines. In the 4th industrial revolution, however, we are trying adding the speech recognition module to the machines to operate them by giving voice commands. Today many of the things are communicated with human by voice commands. Many of them are called AI things and they do tasks which users request and do tasks more than what users request. In the 4th industrial revolution, we use smartphones all the time every day from the morning to the night. For this reason, the privacy using phone is not guaranteed sometimes. For example, the caller's voice can be heard through the phone speaker when accepting a call. So, it is needed to protect privacy on smartphone and it should work automatically according to the user context. In this aspect, this paper proposes a method to adjust the voice volume for call to protect privacy on smartphone according to the user context.

• The Korean Journal of Emergency Medical Services
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• 제6권1호
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• pp.77-86
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• 2002

The purposes of this research which was conducted by surveying lost/added unit hours reports, unit hour demand analysis worksheets from prehospital care reports of two squads in Kyonggi Provincial Fire and Disaster Headquarters for 20 weeks (January 1, 2002 - May 20, 2002) are to get Unit Hour Utilizations. Call Demands such as Unit Hour Demand, Simple Average Demand, High Average Demand, Peak Average Demand, the High Actual Demand. The conclusions from this analysis were summarized as follows: (1) By revealing Unit Hour Produced 3223.9, Call Volume 964, Unit Hour Utilization 0.299 at the Squad A and Unit Hour Produced 3328.4, Call Volume 901, Unit Hour Utilization 0.271 at the Squad B induced Korean Squads to chance identification, definition, direction of Unit Hour Utilization. (2) By revealing Simple Average Demand 7.4 on Monday Tuesday, High Average Demand 9.6 on Tuesday Friday. Peak Average Demand 11.5 on Tuesday, the High Actual Demand 12 on Tuesday Wednesday at the Squad A and Simple Average Demand 6.8 on Sunday, High Average Demand 10.4 on Monday, Peak Average Demand 11.5 on Monday, the High Actual Demand 13 on Monday at the Squad B enabled Korean Squads to utilize System Status Management. (3) The Maximum Calls per Unit Hour were 115 for 23:00~23:59, the Minimum Calls per Unit Hour were 46 for 05:00~05:49 in two squads. The Maximum Calls per Unit Hour were 7.4 on Tuesday Saturday, the Minimum Calls per Unit Hour were 6.1 on Thursday at the Squad A. The Maximum Calls per Unit Hour were 7.3 on Monday Saturday, the Minimum Calls per Unit Hour were 5.6 on Thursday at the Squad B. (4) Analyzing demand for EMTs in the optimum emergency medical service of Korea, we have been able to utilize this Unit Hour Utilization in company with the established estimation methods such as international comparisons or the number of ambulances for scientific reasonable estimation. (5) These Call Demands which were limited to the demand time in this study will make us expect some following studies including demand time, demand time, demand map for Strategic Deployment.

• IE interfaces
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• 제24권1호
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• pp.40-50
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• 2011

We develop a nonlinear integer programming model which minimizes the total cost with the optimal number of operators to hire and their optimal allocation to the tasks under the diverse constraints such as the weekly, daily, and hourly maximum allowable abandonment rates for the time-varying inbound call volume. We present a case study based on actual data at a call center, in order to prove the validity of applying the optimization method proposed. By the one-sample two-tailed t-test, we confirm that the expected abandonment rates resulting from the optimization method are identical with the ones from the discrete-event simulation within specified confidence intervals.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제23권7호
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• pp.1732-1743
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• 1998

In this paper, we have proposed a channel assignment shceme for the multiple traffic class environment where the traffic volume varies with time. Our basic idea is to assign radio channels considering the characteristics of each traffic class and mobile speed and change the number of allocated channels for each traffic class according to vaiation of traffic volume. To evalute the perfomance of our proposed algorithms, we conducted analyical and simulation studies. We derived new call blocking probability and hand-off failure probability of the proposed scheme analytically. The simulatioh results show that our proposed algorithms can grantee GOS of each traffic class and provide a fairness between the new call and the hand-off call.

• Journal of Intelligence and Information Systems
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• 제16권3호
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• The Korean Journal of Applied Statistics
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• 제24권2호
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• pp.217-225
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• 2011

In this paper, a seasonal variable selection method using the spectral analysis accompanied with seasonal linear model is suggested. The suggested method is applied to the prediction of intra-day call arrivals at a large North American commercial bank call center and a signi cant intra-month seasonal variable I detected. This newly detected seasonal factor is included in the seasonal linear model and is compared with the seasonal linear models without this variable to see whether the new variable helps to improve the forecasting performance. The seasonal linear model with the new variable outperformed the models without it in one-day-ahead forecasting.