• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6004-6013
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• 2014

The research on resource sharing in the diversification field is concerned mainly with sharing, but there has been little interest in resources. This research examined resource sharing with the main variables and logics of resource-based view. Based on a survey of 263 affiliates of 35 diversified firms, the impacts of resource quantity, resource quality, and resource exploitability on inter-affiliate resource sharing were hypothesized and verified. To confirm the performance implications of resource sharing, the impacts of the combination of resource quantity and resource sharing, the combination of resource quality and resource sharing, and the combination of resource exploitability and resource sharing on the affiliate performance were also hypothesized and verified. According to the empirical results from multiple regression analyses, resource sharing increases in the order of low resource quantity, high resource quality, and high resource exploitability. The performance was advanced in resource sharing under a low resource quantity and resource sharing under high resource exploitability, but it was not advanced in resource sharing under high resource quality. The results highlight the need for a further study on why the resource quality and resource exploitability affect resource sharing in the opposite directions, as expected in the hypotheses, why resource sharing under high resource quality does not lead to high performance, even though resource quality increases resource sharing, and what they would be if resources are subdivided by the types.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.5
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• pp.275-285
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• 2006

This paper proposes an efficient market mechanism-based resource transaction model for grid computing. This model predicts the next resource demand of users and suggests reasonable resource price for both of customers and resource providers. This model increases resource transactions between customers and resource providers and reduces the average of transaction response times from resource providers. For prediction accuracy improvement of resource demands and suggestion of reasonable resource price, this model introduces a statistics-based prediction model and a price decision model of microeconomics. For performance evaluating, this paper measures resource demand prediction accuracy rate of users, response time of resource transaction, the number of resource transactions, and resource utilization. With 87.45% of reliable prediction accuracy, this model works on the less 72.39% of response time than existing resource transaction models in a grid computing environment. The number of transactions and the resource utilization increase up to 162.56% and up to 230%, respectively.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2020.10a
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• pp.190-190
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• 2020

• Journal of Family Resource Management and Policy Review
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• v.13 no.4
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• pp.39-52
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• 2009

The purpose of this research is to introduce a new method for teaching family resource management using classroom experiments. Classroom experiments are very attractive in the sense that students are placed directly into the various environments being studied, and they enhance the effectiveness of the Socratic Method, as opposed to traditional lectures. The family resource management approach focuses on the resource allocation behavior of families in the utilization of their human and non-human resources to achieve goals. Therefore, there are many family resource decision situations to which classroom experiments may be fruitfully applied. Within this context, this research provides useful implications for applying managerial concepts of family resource management behavior in practical situations. The study results might be used to analyze an interactive framework that facilitates discussions of classroom experiments, family resource management behavior and human ecology.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.107-114
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• 2018

Recently, various wearable personal devices such as a smart watch have been developed and these personal devices are being miniaturized. The user desires to receive new services from personal devices as well as services that have been received from personal computers, anytime and anywhere. However, miniaturization of devices involves constraints on resources such as limited input and output and insufficient power. In order to solve these resource constraints, this paper proposes a resource collaboration system which provides a service by composing sharable resources in the resource sharing environment like IoT. the paper also propose a method to infer and recommend user-customized resources among various sharable resources. For this purpose, the paper defines an ontology for resource inference. This paper also classifies users behavior types based on a user model and then uses them for resource recommendation. The paper implements the proposed method as a prototype system on a personal device with limited resources developed for resource collaboration and shows the effectiveness of the proposed method by evaluating user satisfaction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.79-86
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• 2020

To make a satisfactory decision regarding project scheduling, a trade-off between the resource-related cost and project duration must be considered. A beneficial method for decision makers is to provide a number of alternative schedules of diverse project duration with minimum resource cost. In view of optimization, the alternative schedules are Pareto sets under multi-objective of project duration and resource cost. Assuming that resource cost is closely related to resource leveling, a heuristic algorithm for resource capacity reduction (HRCR) is developed in this study in order to generate the Pareto sets efficiently. The heuristic is based on the fact that resource leveling can be improved by systematically reducing the resource capacity. Once the reduced resource capacity is given, a schedule with minimum project duration can be obtained by solving a resource-constrained project scheduling problem. In HRCR, VNS (Variable Neighborhood Search) is implemented to solve the resource-constrained project scheduling problem. Extensive experiments to evaluate the HRCR performance are accomplished with standard benchmarking data sets, PSPLIB. Considering 5 resource leveling objective functions, it is shown that HRCR outperforms well-known multi-objective optimization algorithm, SPEA2 (Strength Pareto Evolutionary Algorithm-2), in generating dominant Pareto sets. The number of approximate Pareto optimal also can be extended by modifying weight parameter to reduce resource capacity in HRCR.

• 대한생식의학회:학술대회논문집
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• 1999.11a
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• pp.71.2-72
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• 1999

• 대한생식의학회:학술대회논문집
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• 1999.11a
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• pp.72-73
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• 1999

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1712-1732
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• 2011

Cloud provides dynamically scalable virtualized computing resources as a service over the Internet. To achieve higher resource utilization over virtualization technology, an optimized strategy that deploys virtual machines on physical machines is needed. That is, the total number of active physical host nodes should be dynamically changed to correspond to their resource usage rate, thereby maintaining optimum utilization of physical machines. In this paper, we propose a pattern-based prediction model for resource provisioning which facilitates best possible resource preparation by analyzing the resource utilization and deriving resource usage patterns. The focus of our work is on predicting future resource requests by optimized dynamic resource management strategy that is applied to a virtualized data center in a Cloud computing environment. To this end, we build a prediction model that is based on user request patterns and make a prediction of system behavior for the near future. As a result, this model can save time for predicting the needed resource amount and reduce the possibility of resource overuse. In addition, we studied the performance of our proposed model comparing with conventional resource provisioning models under various Cloud execution conditions. The experimental results showed that our pattern-based prediction model gives significant benefits over conventional models.

• Journal of the Korea Institute of Building Construction
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• v.4 no.1
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• pp.105-110
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• 2004

Resource procurement is an important management area because cost of resource covers 40％ of total construction project cost and resource delivery has direct relationship with project performance. Integration of cost provides various potentials for effective and efficient project control. This study investigates the usefulness of time in resource procurement management focused on materials. These days, construction projects have characterized manufacture because of industrialization and component. Therefore, application of systematic resource planning has been requested in the construction. There are many companies conducting procurement of resource on the web by applying MRP, ERP etc. in the construction. However, in applying them in the construction yet, there is obstruction. MRP has the character doing its function under accurate cost prediction of resource. But prediction of resource is difficult in industry mechanism of the construction. If accurate cost prediction of resource is possible in the construction, it will be expected to reduce cost of procurement of resource substantially by applying successful resource planning model in the manufacture. On the basis of recent current, the purpose of study is to present procurement of resource system that period observance of construction and minimization of stock is possible by reflecting accurate lead-time to apply proactive thought to be able to cope with alteration of construction schedule efficiently in analyzing resource planning of the construction site.