In this study, we analyze a finite-buffer M/G/1 queueing model with randomized pushout space priority and nonpreemptive time priority. Space and time priority queueing models have been extensively studied to analyze the performance of communication systems serving different types of traffic simultaneously: one type is sensitive to packet delay, and the other is sensitive to packet loss. However, these models have limitations. Some models assume that packet transmission times follow exponential distributions, which is not always realistic. Other models use general distributions for packet transmission times, but their space priority rules are too rigid, making it difficult to fine-tune service performance for different types of traffic. Our proposed model addresses these limitations and is more suitable for analyzing communication systems that handle different types of traffic with general packet length distributions. For the proposed queueing model, we first derive the distribution of the number of packets in the system when the transmission of each packet is completed, and we then obtain packet loss probabilities and the expected number of packets for each type of traffic. We also present a numerical example to explore the effect of a system parameter, the pushout probability, on system performance for different packet transmission time distributions.

PURPOSES : The purposes of this study are to identify appropriate numbers of drivers for different time periods by analyzing the service times of the Special Transportation System and to shorten the waiting time to within 15 minutes. METHODS : In this study, the service time is divided into the call connection time (At), dispatch time after reception (Bt), vehicle arrival time after dispatch (Ct), and vehicle boarding time (Dt), and the annual average value for each time zone is calculated by analyzing the dispatch system database. Furthermore, the number of drivers working in each time period is extracted and the appropriate number of drivers for ensuring the dispatch waiting time remains within 15 minutes is determined. RESULTS : It is more accurate to interpret the decrease in dispatches during lunchtime as a decrease in the number of operational vehicles owing to the drivers' lunchtimes rather than a decrease in demand. During lunchtime (as in previous studies) the number of operations decreases, but the average dispatch time (Bt) greatly increases to 22:42; thus, it cannot be seen as a decrease in dispatch demand. The number of operations during lunchtime is proportional to the number of drivers on duty. The number of drivers on duty is inversely proportional to the average dispatch time. If the number of drivers is increased by 11.6%, the average waiting time can be reduced to within 15 minutes. CONCLUSIONS : To resolve delayed call connection issues, we will introduce an artificial intelligence (AI) call center. During the hours of 7 PM to 6 AM, calls will mainly be handled by AI and the counseling personnel will switch to daytime work. We will also increase the number of drivers by 11.6% to ensure that the dispatch time does not exceed an average of 15 minutes after receiving a call. In particular, we will generate the work schedule such that more than 131 drivers work in the 12:00 to 13:00 hours during lunch time to improve the situation where users have to wait for a long time. To do this, we will overlap the work hours for 2 hours in Jeonju and 1 hour in other cities and counties. We have to increase the number of night shift workers from seven to 15 so that all cities and counties can operate vehicles 24 hours a day, 365 days a year.

PURPOSES : The objective of this study is to investigate the spatiotemporal factors influencing the waiting time of special transportation systems for disabled people in Seoul, Korea. METHODS : A parametric survival analysis was employed to identify the primary factors influencing longer waiting times, primarily because censoring data were included in the analysis. For the analysis, one-year historical data collected from the Seoul Call-taxi for the disabled in 2019 were used. Using normal probability plots and Anderson-Darling statistics, a log-normal model was estimated to fit the data. RESULTS : In terms of time, there was a time zone in which the service level in terms of waiting time, such as late-night and dawn, decreased depending on the time of day called by call taxi users with disabilities in Seoul. Moreover, the waiting time on weekends was shorter than on weekdays. Spatially, when a user requests a service, there are many nearby vehicles when the traffic conditions nearby are good, and the waiting time tends to decrease near the depot. CONCLUSIONS : The existing system needs to reflect the increase in vulnerable times and the improvement of the dispatch method, considering the difference in the primary purpose of travel on weekdays and weekends. Additionally, to resolve the phenomenon in which vehicles are concentrated in a specific area, waiting time can be improved using operational strategies that can uniformly distribute vehicles in space, such as spatial subdivision of depots, real-time monitoring, and relocation.

The purpose of this study is to measure the effect of customers’ waiting time on their revisit intention through their emotion. Also this study assessed the effect of restaurant selection attributes on consumers’ revisit intention in Korea. This study used experimental scenario questionnaires for collecting data. Frequency analysis, Cronbach’s alpha, correlation, ttests and multiple regression analysis were assessed using SPSS. Customers preferred taste, sanitation and service when selecting a restaurant to dine out. The results of this study found that there were no significant differences between positive and negative emotions due to waiting time. Findings of this study suggested that waiting time, convenience, nutritional value, and emotion influenced consumers’ revisit intention. Therefore, reducing waiting time and providing proper service will help consumers have positive emotions to return to dine at a restaurant.

The Korean buffet restaurant, which was developed by combining the Korean food and a buffet, allows a large menu to be enjoyed at a reasonable price and has become popular with men and women, young and old, and become a new type of eating out. This study examined the effects of the waiting time management and menu quality on the customer satisfaction and re-visit intention in a Korean buffet restaurant to help quality improvement and development of Korean buffet restaurants. The customer satisfaction and re-visit intention only had a significant effect on the staff attitude toward waiting time. Hygiene, sensory factor, and variety of menu quality were significantly related to customer satisfaction and revisit intention, respectively. In addition, satisfaction with food and service had a significant effect on the re-visit intention and customer satisfaction. Therefore, it is important to establish a virtuous cycle structure that will improve the re-visit intention by satisfying customers through waiting time management and menu quality for the development of Korean buffet restaurants.

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.

본 연구의 목적은 휘발성 유기화합물(VOC)과 먼지(PM)의 배출원 프로파일로부터 화학종 분류를 할당하고, 성김 행렬 조작자 핵심 배출량 시스템(SMOKE) 내에 배출원 분류코드에 따른 배출원 프로파일의 화학종 분류와 시간분배계수를 수정하는 것이다. 기솔린, 디젤 증기, 도장, 세탁, LPG 등과 같은 VOC 배출원 프로파일로부터 화학 종 분류는 탄소 결합 IV (CBIV) 화학 메커니즘과 주 규모 대기오염연구센터 99 (SAPRC99) 화학 메커니즘을 위해 각각 12종과 34종을 포함한다. 또한 토양, 도로먼지, 가솔린, 디젤차, 산업기원, 도시 소각장, 탄 연소 발전소, 생체 연소, 해안 등과 같은 PM2.5 배출원 프로파일로부터 화학종 분류는 미세 먼지, 유기탄소, 원소 탄소, 질산염과 황산염의 5종으로 할당하였다. 게다가 점 및 선 배출원의 시간 프로파일은 2007년 수도권 지역에서의 굴뚝 원격감시시스템(TMS)과 시간별 교통 흐름 자료로부터 구하였다. 특별히 점 배출원에 있어 오존 모델링을 위한 시간분배계수는 굴뚝 원격감시시스템 자료의 NOX 배출량 인벤토리에 근거하여 추정하였다.

대기 모델링 연구에서 시간 간격을 적절하게 결정하는 것은 중요한 문제이다. 본 연구에서는 비선형 대기 모형에서 수치 해의 시간 간격에 대한 민감도를 조사하였다. 이를 위해 간단한 무차원화된 역학 모형을 사용하여 시간 간격과 비선형성 인자를 바꾸어가며 수치 실험을 수행하였다. 실험 결과, 비선형성 인자가 영향을 줄 만큼 크지 않고 절단 오차를 무시할 수 있는 경우에는 수치 해가 시간 간격에 민감하지 않았다. 그러나 비선형성 인자가 큰 경우에는 수치 해가 시간 간격에 민감한 것으로 밝혀졌다. 이 경우, 시간 간격이 감소할수록 공간 필터의 강도가 증가하여 작은 규모의 현상이 약하게 모의되었다. 이는 일반적으로 시간 간격이 감소하면 절단 오차가 감소하여 더 정확한 수치 해가 도출된다는 사실과 상충한다. 이러한 충돌은 비선형 모형의 수치 해를 안정하게 하기 위해 공간 필터가 반드시 필요하기 때문에 피할 수 없다.

The minimum latency problem, also known as the traveling repairman problem and the deliveryman problem is to minimize the overall waiting times of customers, not to minimize their routing times. In this research, a genetic algorithm, a clonal selection algorithm and a population management genetic algorithm are introduced. The computational experiment shows the objective value of the clonal selection algorithm is the best among the three algorithms and the calculating time of the population management genetic algorithm is the best among the three algorithms.