• The Journal of Society for e-Business Studies
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• v.17 no.1
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• pp.53-74
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• 2012

This study developed a method for selection of available human resources for incomingjob allocation that considers factors affecting resource performance in the business process management (BPM) environment. For many years, resource selection has been treated as a very important issue in scheduling due to its direct influence on the speed and quality of task accomplishment. Even though traditional resource selection can work well in many situations, it might not be the best choice when dealing with human resources. Humanresource performance is easily affected by several factors such as workload, queue, working hours, inter-arrival time, and others. The resource-selection rule developed in the present study considers factors that affect human resource performance. We used a Bayesian Network (BN) to incorporate those factors into a single model, which we have called the Bayesian Selection Rule (BSR). Our simulation results show that the BSR can reduce waiting time, completion time and cycle time.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.202-213
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• 2003

Tower Palace III project is the highest residential and commercial high-rise complex building in Korea. In order to construct a high-rise building, advanced construction and engineering technology is required. Therefore, with more developed construction and engineering technology based upon accumulated knowledge, construction speed of 13.4 days per floor including finish work was achieved in this project. To achieve this project successfully, three main advanced construction technology were applied: 1) Construction methods for 3-day cycle of structural work and curtain wall, 2) Tact scheduling method for finish work, 3) Management system of material, labor, work, and information. Also, four main engineering technology were applied: 1) New material such as high -flowing concrete and high strength concrete of 800 kgf/cm2, 2) New method such as a pipe-cooling system of a cool water circulating type, 3) Mechanical system such as smart-fan controlling kitchen-ventilation system, 4) Electrical system such as false car system.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.12
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• pp.630-640
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• 2007

As the range of sensor network's applicability is getting wider, it creates new application areas which is required real-time operation, such as military and detection of radioactivity. However, existing researches are focused on effective management for resources, existing sensor network operating system cannot support to real-time areas. In this paper, we propose the ${\mu}TMO$ model which is lightweight real-time distributed object model TMO. We design the real-time sensor network operation system ${\mu}TMO-NanoQ+$ which is based on ETRI's sensor network operation system Nano-Q+. We modify the Nano-Q+'s timer module to support high resolution and apply Context Switch Threshold, Power Aware scheduling techniques to realize lightweight scheduler which is based on EDF. We also implement channel based communication way ITC-Channel and periodic thread management module WTMT.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2952-2971
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• 2016

Outsourcing jobs to a public cloud is a cost-effective way to address the problem of satisfying the peak resource demand when the local cloud has insufficient resources. In this paper, we studied the management of deadline-constrained bag-of-tasks jobs on hybrid clouds. We presented a binary nonlinear programming (BNP) problem to model the hybrid cloud management which minimizes rent cost from the public cloud while completes the jobs within their respective deadlines. To solve this BNP problem in polynomial time, we proposed a heuristic algorithm. The main idea is assigning the task closest to its deadline to current core until the core cannot finish any task within its deadline. When there is no available core, the algorithm adds an available physical machine (PM) with most capacity or rents a new virtual machine (VM) with highest cost-performance ratio. As there may be a workload imbalance between/among cores on a PM/VM after task assigning, we propose a task reassigning algorithm to balance them. Extensive experimental results show that our heuristic algorithm saves 16.2%-76% rent cost and improves 47.3%-182.8% resource utilizations satisfying deadline constraints, compared with first fit decreasing algorithm, and that our task reassigning algorithm improves the makespan of tasks up to 47.6%.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.449-455
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• 2002

This paper considers that advanced planning and scheduling (APS) in manufacturing and the efficient purchasing where each customer order has its due date and multi-suppliers exit We present a Make-To­Order Supply Chan (MTOSC) model of efficient purchasing process from multi-suppliers and APS with outsourcing in a supply chain, which requires the absolute due date and minimized total cost. Our research has included two states. One is for efficient purchasing from suppliers: (a) selection of suppliers for required parts; (b) optimum part lead­time of selected suppliers. Supplier selection process has received considerable attention in the business­management literature. Determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions usually is complex and unstructured. These influence factors can be divided into quantitative and qualitative factors. In the first level, linguistic values are used to assess the ratings for the qualitative factors such as profitability, relationship closeness and quality. In the second level a MTOSC model determines the solutions (supplier selection and order quantity) by considering quantitative factors such as part unit price, supplier's lead-time, and storage cost, etc. The other is for APS: (a) selection of the best machine for each operation; (b) deciding sequence of operations; (c) picking out the operations to be outsourcing; and (d) minimizing makespan under the due date of each customer's order. To solve the model, a genetic algorithm (GA)-based heuristic approach is developed. From the numerical experiments, GA­based approach could efficiently solve the proposed model, and show the best process plan and schedule for all customers' orders.

• Journal of Korean Physical Therapy Science
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• v.9 no.4
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• pp.63-72
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• 2002

Patient satisfaction is an important factor in evaluating the quality of care. Patient satisfaction may be used to evaluate provider services and facilities, and used to predict the patient returns to a facility. The patients d whether the patient returns to a facility or whether the patient recommends the facility to other people may be affected by a variety of factors of patient satisfaction. Low satisfaction may result in poor compliance with the potential of waste of resources and suboptimal clinical outcome. This study is to identify factors of patient satisfaction that will affect patients decision whether the patient returns or not. A self-administered questionnaire survey was conducted in Seoul, Chung-Joo and Bu-Cheon cities, Survey data was obtained from 743 patients who visited the physical therapy practice at university hospitals, general hospitals and clinics. Response rate was 94.4%. The instrument developed by Goldstein et al. (2000) was used and translated into Korean. Several items were added to the instrument. Patient's opinions of service in each domain measured using 5-point Likert-type scales that ranged from strongly disagree to strongly agree. A multiple-regression analytic approach was used to predict overall satisfaction of physical therapy. Age, kindness, scheduling, convenience of parking, privacy, and waiting time predicted the overall satisfaction of physical therapy. The older patients had higher level of satisfaction with physical therapy compared with the younger patients. Patient satisfaction were more affected by access (scheduling and waiting time), administrative technical management (convenience of parking), and interpersonal management (kindness of physical therapists and other staffs) than clinical technical management (physical therapists' skills).

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.917-926
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• 2019

At present, TICN has been developed and distributed for military command control. TICN is known as the 3.5G mobile communication technology based on WiBro, which shows technical limitation in the field operation situation. Accordingly, the drone-type base station platform is attracting attention as an alternative to overcome technical limitations such as difficulty in securing communication LoS and limiting expeditious network configuration. In this study, we performed simulation performance evaluation of drone-type base station operation in 28 GHz that is considered most suitable for cellular communication within mmWave frequency band. Specifically, we analyzed the changes in throughput and fairness performance according to radio resource management policies such as frequency reuse and scheduling in multi-cell topology. Through this, we tried to provide insights on the operation philosophy on drone-type base station.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.137-148
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• 2012

Cloud service users request dedicated virtual computing resource from the cloud service provider to process jobs in independent environment from other users. To optimize this process with automated method, in this paper we proposed a framework for workflow scheduling in the cloud environment, in which the core component is the middleware called broker mediating the interaction between users and cloud service providers. To process jobs in on-demand and virtualized resources from cloud service providers, many papers propose scheduling algorithms that allocate jobs to virtual machines which are dedicated to one machine one job. With this method, the isolation of being processed jobs is guaranteed, but we can't use each resource to its fullest computing capacity with high efficiency in resource utilization. This paper therefore proposed a cost-efficient job scheduling algorithm which maximizes the utilization of managed resources with increasing the degree of multiprogramming to reduce the number of needed virtual machines; consequently we can save the cost for processing requests. We also consider the performance degradation in proposed scheme with thrashing and context switching. By evaluating the experimental results, we have shown that the proposed scheme has better cost-performance feature compared to an existing scheme.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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• pp.129-134
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• 2013

The airport processes are restricted by some limits of performance objects as size of airport, ability of human resources, capacity of facilities and operational rules. These limitations make passenger handling difficult when passenger numbers increase. In order to solve this problem, we modeled the airport process and analyzed departure passenger arrival, scheduled security manpower under specific customer service level maintenance with mixed integer programming and validate the efficiency with simulation with adapting smart airport framework. We concluded that the airport management with information techniques can reduce waiting time within security and immigration process.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.111-119
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• 2014

The cloud environment need resource management method that to enable the big data issue and data analysis technology. Existing resource management uses the limited calculation method, therefore concentrated the resource bias problem. To solve this problem, the resource management requires the learning-based scheduling using resource history information. In this paper, we proposes the ART (Adaptive Resonance Theory)-based adaptive resource management. Our proposed method assigns the job to the suitable method with the resource monitoring and history management in cloud computing environment. The proposed method utilizes the unsupervised learning method. Our goal is to improve the data processing and service stability with the adaptive resource management. The propose method allow the systematic management, and utilize the available resource efficiently.