• 보건행정학회지
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• 제7권2호
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• pp.19-45
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• 1997

This study was performed to evaluate the appropriateness of resource allocation based on the ranking of health center function. Through the Delphi processes, health center functions were ranked in order of importance as follows; planning and research, followed by health education, health promotion, management of chronic diseases, health screening tests, welfare activities, mental health services, medical personnel management, medical services, prevention of communicable diseases, maternal and infant health services, housekeeping, management of oral hygiene, nutrition services, surveillance for community health services, family planning, and administration of the health center. In relation to the above priorities, the allocation of manpower was not appropriate. Even though the expert groups emphasized on functions such as planning and research, health education, and health promotion, they inputted more personnel for administration of a health center, maternal and infant health services, and medical services which were evaluated with lower importance. The budget allocation showed the same trends as the above. Although the functions such as planning and research, health education, and health promotion, and management of chronic diseases were evaluated highly, the budget was allocated accordingo to the the results of the former fiscal year rather than on the importance of function. However the budget for nutrition services, surveillance for the community health services, family planning, and administration of a health center was allocated according to priority. Based upon the above findings, community health center should be given the opportunity to make their own ranking of health center function and to allocate their resources including personnel and budget in order to improve the responsibilities and roles of the community health center.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.222-229
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• 2022

Although stochastic programming and feedback control approaches could efficiently mitigate the overdue risks caused by inherent uncertainties in ground conditions, the lack of formal representations of planners' rationales for resource allocation still prevents planners from applying these approaches due to the inability to consider comprehensive resource allocation policies for hard rock tunnel projects. To overcome the limitations, the authors developed an ontology that represents the project duration estimation rationales, considering the impacts of ground conditions, excavation methods, project states, resources (i.e., given equipment fleet), and resource allocation policies (RAPs). This ontology consists of 5 main classes with 22 subclasses. It enables planners to explicitly and comprehensively represent the necessary information to rapidly and consistently estimate the excavation durations during construction. 10 rule sets (i.e., policies) are considered and categorized into two types: non-progress-related and progress-related policies. In order to provide simplified information about the remaining durations of phases for progress-related policies, the ontology also represents encoding principles. The estimation of excavation schedules is carried out based on a hypothetical example considering two types of policies. The estimation results reveal the feasibility, potential for flexibility, and comprehensiveness of the developed ontology. Further research to improve the duration estimation methodology is warranted.

• 한국경영과학회지
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• 제16권2호
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• pp.61-61
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• 1991

We propose a strategic R&D resource allocation and project selection model based on national R&D policy and objectives. First, contributions to R&D policy and objectives for each R&D area are evaluated by using analytical hierarchy process (AHP). Second, fuzzy Delphi are proposed to estimate R&D budget for each R&D area. Then, a project selection grid is also introduced to implement two-phased evaluation for R&D project selection. We also discuss how to improve the consistency in AHP and how to reduce the pairwise comparison in AHP. The proposed model enables the decision makers to allocate R&D budget, and to evaluate and select the R&D proposals based on both the contribution to national R&D policy and objectives, and the size of each R&D area concurrently

• 경영과학
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• 제16권2호
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• pp.61-77
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• 1999

We propose a strategic R&D resource allocation and project selection model based on national R&D policy and objectives. First, contributions to R&D policy and objectives for each R&D area are evaluated by using analytical hierarchy process (AHP). Second, fuzzy Delphi are proposed to estimate R&D budget for each R&D area. Then, a project selection grid is also introduced to implement two-phased evaluation for R&D project selection. We also discuss how to improve the consistency in AHP and how to reduce the pairwise comparison in AHP. The proposed model enables the decision makers to allocate R&D budget, and to evaluate and select the R&D proposals based on both the contribution to national R&D policy and objectives, and the size of each R&D area concurrently.

• Journal of Preventive Medicine and Public Health
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• 제54권5호
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• pp.360-369
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• 2021

Objectives: The purpose of this study was to investigate public preferences regarding allocation principles for scarce medical resources in the coronavirus disease 2019 (COVID-19) pandemic, particularly in comparison with the recommendations of ethicists. Methods: An online survey was conducted with a nationally representative sample of 1509 adults residing in Korea, from November 2 to 5, 2020. The degree of agreement with resource allocation principles in the context of the medical resource constraints precipitated by the COVID-19 pandemic was examined. The results were then compared with ethicists' recommendations. We also examined whether the perceived severity of COVID-19 explained differences in individual preferences, and by doing so, whether perceived severity helps explain discrepancies between public preferences and ethicists' recommendations. Results: Overall, the public of Korea agreed strongly with the principles of "save the most lives," "Koreans first," and "sickest first," but less with "random selection," in contrast to the recommendations of ethicists. "Save the most lives" was given the highest priority by both the public and ethicists. Higher perceived severity of the pandemic was associated with a greater likelihood of agreeing with allocation principles based on utilitarianism, as well as those promoting and rewarding social usefulness, in line with the opinions of expert ethicists. Conclusions: The general public of Korea preferred rationing scarce medical resources in the COVID-19 pandemic predominantly based on utilitarianism, identity and prioritarianism, rather than egalitarianism. Further research is needed to explore the reasons for discrepancies between public preferences and ethicists' recommendations.

• 디지털융복합연구
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• 제14권10호
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• pp.303-310
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• 2016

인터넷의 경직성으로 인하여 다양한 서비스를 제공하기 어려운 문제를 해결하기 위한 가장 효과적인 방법이 네트워크 가상화 방안이다. 공통의 물리 네트워크 자원을 공유하는 가상 네트워크를 효율적으로 생성하기 위해서는 자원을 효율적으로 사용할 수 있는 방안이 필요하다. 이러한 한정된 물리 자원을 효율적으로 사용하기 위하여 본 논문에서는 사용중인 자원이라도 더 높은 서비스 우선순위를 갖는 가상 네트워크에 할당하는 방법을 제안한다. 높은 우선순위의 네트워크 생성 요청을 위하여 운용중인 낮은 우선순위의 가상 네트워크에서 효율적으로 대역폭을 회수하는 알고리즘을 제안한다. 제안하는 대역폭 할당 알고리즘은 낮은 우선순위를 갖는 가상 네트워크에서 서비스 지속성을 유지하면서 할당된 대역폭을 회수할 수 있다. 제안한 알고리즘의 실험을 통하여 약 8%정도의 가상 네트워크 생성 수락률의 향상을 확인하였다.

• ETRI Journal
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• 제33권2호
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• pp.184-193
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• 2011

Tailored for wireless local area networks, the present paper proposes a cross-layer resource allocation scheme for multiple-input multiple-output orthogonal frequency-division multiplexing systems. Our cross-layer resource allocation scheme consists of three stages. Firstly, the condition of sharing the subchannel by more than one user is studied. Secondly, the subchannel allocation policy which depends on the data packets' lengths and the admissible combination of users per subchannel is proposed. Finally, the bits and corresponding power are allocated to users based on a greedy algorithm and the data packets' lengths. The analysis and simulation results demonstrate that our proposed scheme not only achieves significant improvement in system throughput and average packet delay compared with conventional schemes but also has low computational complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권11호
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• pp.5357-5381
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• 2018

Cloud computing offers a wide range of on-demand resources over the internet. Utility-based resource allocation in cloud data centers significantly increases the number of cloud users. Heavy usage of cloud data center encounters many problems such as sacrificing system performance, increasing operational cost and high-energy consumption. Therefore, the result of the system damages the environment extremely due to heavy carbon (CO2) emission. However, dynamic allocation of energy-efficient resources in cloud data centers overcomes these problems. In this paper, we have proposed Energy and Service Level Agreement (SLA) Aware Resource Allocation Heuristic Algorithms. These algorithms are essential for reducing power consumption and SLA violation without diminishing the performance and Quality-of-Service (QoS) in cloud data centers. Our proposed model is organized as follows: a) SLA violation detection model is used to prevent Virtual Machines (VMs) from overloaded and underloaded host usage; b) for reducing power consumption of VMs, we have introduced Enhanced minPower and maxUtilization (EMPMU) VM migration policy; and c) efficient utilization of cloud resources and VM placement are achieved using SLA-aware Modified Best Fit Decreasing (MBFD) algorithm. We have validated our test results using CloudSim toolkit 3.0.3. Finally, experimental results have shown better resource utilization, reduced energy consumption and SLA violation in heterogeneous dynamic cloud environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1415-1435
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• 2018

Resource allocation in device-to-device (D2D) aided cellular systems, in which the proximity users are allowed to communicate directly with each other without relying on the intervention of base stations (BSs), is investigated in this paper. A new uplink resource allocation policy is proposed by exploiting the relationship between D2D-access probability and channel gain among variant devices, such as cellular user equipments (CUEs), D2D user equipments (DUEs) and BSs, etc., under the constraints of their minimum signal to interference-plus-noise ratio (SINR) requirements. Furthermore, the proposed resource-allocation problem can be formulated as the cost function of "maximizing the number of simultaneously activated D2D pairs subject to the SINR constraints at both CUEs and DUEs". Numerical results relying on system-level simulations show that the proposed scheme is capable of substantially improving both the D2D-access probability and the network throughput without sacrificing the performance of conventional CUEs.