Earticle

Advanced Air Mobility (AAM) systems rely on high-frequency operations of electric vertical take-off and landing (eVTOL) aircraft, making the performance of ground energy infrastructure a critical factor for overall system efficiency and reliability. In particular, battery swapping stations function as service systems in which multiple aircraft arrivals share limited resources, inevitably leading to processing delays under stochastic demand conditions. Previous studies have primarily focused on optimization-based scheduling and simulation-driven performance evaluation. However, the probabilistic mechanisms governing delay generation and operational stability in battery swapping systems have not been sufficiently explored. This study presents a probabilistic operational analysis of battery swapping stations in AAM using queueing theory. The system is modeled as a multi-server queueing system with stochastic arrival and service processes, and key performance metrics—including system utilization, average waiting time, and delay probability are analytically derived. Furthermore, delay behavior is examined not only in terms of average values but also through probabilistic distributions and service-level-based performance criteria. To validate the proposed analytical framework, MATLAB-based discrete-event simulations are conducted under various operational scenarios, including different arrival rates and service time variability conditions. The results indicate that stochastic characteristics significantly influence delay behavior and system stability, particularly under peak demand conditions. The proposed approach provides a theoretical foundation for understanding delay dynamics in AAM battery swapping operations and offers practical insights for designing stable and efficient ground infrastructure systems.

Recently, as the 4th Industrial Revolution is rapidly progressing in the world, Self-Service Technology equipment is increasing in various industry fields. Therefore, we selected Self-Service Gas Station industry among various industry field and studied with the scope of gas station location which is in Busan, Ulsan and Gyeong-Nam in order to develop Optimal Operation Model of Self-Service Technology. As a result, we’ve got optimal economy through the main factors’s relationship that affect Self-Service Technology through queueing theory. Price discounts have a positive impact on the number of customers and the waiting time of the service has a negative impact on the numbers of customers. In addition, the number of visiting customers has a positive impact on the waiting time of the service. Next, the number of the gas stands affects adversely waiting time of the service. Also, the employee working hours have a bad influence on the waiting time of the service. Overall, it is showed that price discounts, the waiting time of the service, the number of the gas stands, the employee working hours contribute to Optimal Model by providing Self-Service Technology for Optimal Operation. The result of this study makes Optimal Operation Model through customer participation in Self-Service theoretically and it can be applied to various Self-Service industry field and is possible to operate optimally.

This study investigates a Cloud?Edge-sensors infrastructure using M/M/c/K queuing theory to analyze agricultural data systems’ performance. It focuses on optimizing data handling and evaluates the system configuration impacts on performance. The model significantly enhances efficiency and scalability, minimizing the need for extensive physical infrastructure. Analysis shows over 90% utilization in both layers, highlighting the model’s applicability to various IoT applications. The M/M/c/K queuing model addresses scalability and real-time data processing challenges in agricultural cloud?edge-sensor networks, improving over traditional methods lacking dynamic scalability. Designed for optimized resource use and reduced data handling delays, this model proves crucial in precision agriculture, where timely data is essential for decision-making. Its versatility extends to various agricultural applications requiring efficient real-time analysis and resource management.

Objectives: This research used queueing theory to analyze changes in outpatients’ waiting times before and after the introduction of Electronic Medical Record (EMR) systems. Methods: We focused on the exact drawing of two fundamental parameters for queueing analysis, arrival rate (λ) and service rate (μ), from digital data to apply queueing theory to the analysis of outpatients’ waiting times. We used outpatients’ reception times and consultation finish times to calculate the arrival and service rates, respectively. Results: Using queueing theory, we could calculate waiting time excluding distorted values from the digital data and distortion factors, such as arrival before the hospital open time, which occurs frequently in the initial stage of a queueing system. We analyzed changes in outpatients’ waiting times before and after the introduction of EMR using the methodology proposed in this paper, and found that the outpatients’ waiting time decreases after the introduction of EMR. More specifically, the outpatients’ waiting times in the target public hospitals have decreased by rates in the range between 44% and 78%. Conclusions: It is possible to analyze waiting times while minimizing input errors and limitations influencing consultation procedures if we use digital data and apply the queueing theory. Our results verify that the introduction of EMR contributes to the improvement of patient services by decreasing outpatients’ waiting time, or by increasing efficiency. It is also expected that our methodology or its expansion could contribute to the improvement of hospital service by assisting the identification and resolution of bottlenecks in the outpatient consultation process.

Cloud computing is becoming an effective and efficient way of computing resources and computing service integration. Through centralized management of resources and services, cloud computing delivers hosted services over the internet, such that access to shared hardware, software, applications, information, and all resources is elastically provided to the consumer on-demand. The main enabling technology for cloud computing is virtualization. Virtualization software creates a temporarily simulated or extended version of computing and network resources. The objectives of virtualization are as follows: first, to fully utilize the shared resources by applying partitioning and time-sharing; second, to centralize resource management; third, to enhance cloud data center agility and provide the required scalability and elasticity for on-demand capabilities; fourth, to improve testing and running software diagnostics on different operating platforms; and fifth, to improve the portability of applications and workload migration capabilities. One of the key features of cloud computing is elasticity. It enables users to create and remove virtual computing resources dynamically according to the changing demand, but it is not easy to make a decision regarding the right amount of resources. Indeed, proper provisioning of the resources to applications is an important issue in IaaS cloud computing. Most web applications encounter large and fluctuating task requests. In predictable situations, the resources can be provisioned in advance through capacity planning techniques. But in case of unplanned and spike requests, it would be desirable to automatically scale the resources, called auto-scaling, which adjusts the resources allocated to applications based on its need at any given time. This would free the user from the burden of deciding how many resources are necessary each time. In this work, we propose an analytical and efficient VM-level scaling scheme by modeling each VM in a data center as an M/M/1 processor sharing queue. Our proposed VM-level scaling scheme is validated via a numerical experiment.

건축물에 있어서 고층화 대형화의 변화는 인력과 자재의 양중부하를 증가시키는 동시에 수직양중작업의 이동거리를 증가시킨다. 이로 인해 고층으로의 운반정체는 물론 대기상태증가로 인한 생산성 감소의 문제를 발생시킨다. 이에 고층 건물 공사초기의 양중계획은 건설현장의 특성을 면밀히 고려하고 효과적으로 계획수립에 반영될 필요성이 있다. 하지만 현재 건설현장에서 가설리프트 계획수립은 사이클 주기(Cycle Time)를 바탕으로 한 단순계산식을 사용하고 있다. 이 방법은 쉽고 간단하지만, 복잡하고 유기적인 현장의 현실을 제대로 고려하지 못한다는 한계가 있다. 따라서 본 연구에서는 이러한 한계점을 극복하기 위한 방안으로, 확률적 대기행렬의 큐잉이론(Queueing Theory)을 이용한 시뮬레이션모델의 적용 가능성에 대해 알아보고자 한다.

Tall building construction has been increasing due to the need to maximize land usage. It causes the increase of vertical transportation for workers and materials, which significantly affects the productivity and lifting planning, therefore, has to be made carefully based on the characteristics of the field. However, the existing method to calculate the number of lift is too simple to consider complex and various characteristics in tall building construction. Accordingly, we developed the model for selecting the best system of vertical transportation by using Queueing theory. To find out the situation of the queue of resources such as material and workers, a simulation program will be applied.

Purpose: The purpose of this study is to develop the methods for evaluating patients' queue environment using decision tree and queueing theory. Methods: This study uses CHAID decision tree and M/G/1 queueing theory to estimate pain point and patients waiting time for medical service. This study translates hospital physical data process to logical process to adapt queueing theory. Results: This study indicates that three nodes of the system has predictable problem with patients waiting time and can be improved by relocating patients to other nodes. Conclusion: This study finds out three seek points of the hospital through decision tree analysis and substitution nodes through the queueing theory. Revealing the hospital patients' queue environment, this study has several limitations such as lack of various case and factors.

Recently, as the 4th Industrial Revolution is rapidly progressing in the world, Self-Service Technology equipment is increasing in various industry fields. Therefore, we selected Self-Service Gas Station industry among various industry field and studied with the scope of gas station location which is in Busan, Ulsan and Gyeong-Nam in order to develop Optimal Operation Model of Self-Service Technology. As a result, we’ve got optimal economy through the main factors’s relationship that affect Self-Service Technology through queueing theory. Price discounts have a positive impact on the number of customers and the waiting time of the service has a negative impact on the numbers of customers. In addition, the number of visiting customers has a positive impact on the waiting time of the service. Next, the number of the gas stands affects adversely waiting time of the service. Also, the employee working hours have a bad influence on the waiting time of the service. Overall, it is showed that price discounts, the waiting time of the service, the number of the gas stands, the employee working hours contribute to Optimal Model by providing Self-Service Technology for Optimal Operation. The result of this study makes Optimal Operation Model through customer participation in Self-Service theoretically and it can be applied to various Self-Service industry field and is possible to operate optimally.

본 논문에서는 LBT(Listen Before Talk) 방식을 사용하는 ZigBee와 FH(Frequency Hopping) 방식을 사용하는 DCP, RFID, Bluetooth 등의 소출력 무선기기가 공유주파수대역에 공존할 경우 요구되는 적정 채널수를 산출하고 전체 공유주파수대역폭 산출법을 제시하였다. 한정된 주파수 대역에서 LBT 및 FH 방식을 사용하는 총 User 수가 포용되는 공유주파수대역폭 산출은 중요한 작업이다. 소출력 무선기기 시스템의 간섭 회피 기술로 사용되는 FH 방식과 LBT 방식에 대기행렬이론(Queueing Theory)을 적용하였으며, 주위의 전파환경을 감지하여 유휴 주파수대역을 찾아 데이터전송을 시도하는 LBT 방식은 random하게 주파수채널을 이동하며 통신을 시도하는 FH 방식과는 구별된다. 채널수 별 User의 통신시도 시간간격을 통계적으로 처리하여 Throughput을 분석한 결과, Throughput 70% 조건에서 FH 방식과 LBT 방식을 사용하는 250mW 소출력 무선기기들이 공존하는 공유주파수대역의 적정 채널수는 30개를 가지며, 전체 공유주파수대역폭은 채널수에 채널당 대역폭의 곱으로 산출이 가능하다.