• Journal of the Korea Society of Computer and Information
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• v.9 no.3
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• pp.189-194
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• 2004

This paper proposes a Simulated Annealing(SA) algorithm for cluster-based Multicast Routing problems. Multicasting, the transmission of data to a group, can be solved from constructing multicast tree, that is. the whole network is partitioned to some clusters and the clusters are constructed by multicast tree. Multicast tree can be constructed by minimum-cost Steiner tree. In this paper, an SA algorithm is used in the minimum-cost Steiner tree. Especially, in SA, the cooling schedule is an important factor for the algorithm. Hence, in this paper, a cooling schedule is proposed for SA for multicast routing problems and analyzed the simulation results.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2227-2235
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• 2018

Under the current policies and compensation rules in South Korea, Photovoltaic (PV) generation supplier can maximize the profit by combining PV generation with Energy Storage System (ESS). However, the existing operational strategy of ESS is not able to maximize the profit due to the limitation of ESS capacity. In this paper, new ESS scheduling algorithm is introduced by utilizing the System Marginal Price (SMP) and PV generation forecasting to maximize the profits of PV generation supplier. The proposed algorithm determines the charging time of ESS by ranking the charging schedule from low to high SMP when PV generation is more than enough to charge ESS. The discharging time of ESS is determined by ranking the discharging schedule from high to low SMP when ESS energy is not enough to maintain the discharging. To compensate forecasting error, the algorithm is updated every hour to apply the up-to-date information. The simulation is performed to verify the effectiveness of the proposed algorithm by using actual PV generation and ESS information.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1481-1494
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• 1997

In hard real-time systems, a timing fault may yield catastrophic results. Dynamic scheduling provides the flexibility to compensate for unexpected events at runtime; however, scheduling overhead at runtime is relatively large, constraining both the accuracy of the timing and the complexity of the scheduling analysis. In contrast, static scheduling need not have any runtime overhead. Thus, it has the potential to guarantee the precise time at which each instruction implementing a control action will execute. This paper presents a new approach to the problem of analyzing high-level language code, augmented by arbitrary before and after timing constraints, to provide a valid static schedule. Our technique is based on instruction-level complier code scheduling and timing analysis, and can ensure the timing of control operations to within a single instruction clock cycle. Because the search space for a valid static schedule is very large, a novel adaptive genetic search algorithm was developed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3201-3223
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• 2020

Wireless communication has become the promising technology in the recent times because of its applications in Internet of Things( IoT) devices. The IEEE 802.15.4e has become the key technology for IoT devices which utilizes the Time-Slotted Channel Hopping (TSCH) networks for the communication between the devices. In this paper, we develop a Two Level Scheduling Algorithm (TLSA) for scheduling multiple packets with different arrival rate at the source nodes in a TSCH networks based on the link activated by a centralized scheduler. TLSA is developed by considering three types of links in a network such as link i with packets arrival type 1, link j with packets arrival type 2, link k with packets arrival type 3. For the data packets arrival, two stages in a network is considered.At the first stage, the packets are considered to be of higher priority.At the second stage, the packets are considered to be of lower priority.We introduce level 1 schedule for the packets at stage 1 and level 2 schedule for the packets at stage 2 respectively. Finally, the TLSA is validated with the two different energy functions i.e., y = e^ax - 1 and y = 0.5x² using MATLAB 2017a software for the computation of average and worst ratios of the two levels.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.111-121
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• 2007

In mobile computing environments, data broadcasting is widely used to resolve the problem of limited power and bandwidth of mobile equipments. However, the previous broadcast scheduling methods can be inefficient in the environment where the user requests change dynamically since they are based on static data requests. Moreover, a high-priority user can wait long for infrequently requested data because they never consider the priority of listeners. In this paper, we propose a new broadcast scheduling method that reflects dynamic changes of user requests using user profiles. It also reflects user priorities to reduce the access time of high-priority users. We evaluate the performance of the proposed method through simulation.

• Korea Journal of Hospital Management
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• v.16 no.1
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• pp.77-100
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• 2011

The purpose of this study is to the improvement of general hospital outpatients waiting time using Theory Of Constraints(TOC) methodology and to the development of a Reception Desk in general hospital. This study is to provide decision-making guidelines for hospital managers and to provide feedback for the efficiency of job process. The target people of the study are outpatients and Cashiers on Chungnam national university hospital in Daejeon. The methods of study are summarized as follows. First, The team managers from a Reception Desk group were appointed. This team managers have the adjustment authority to the Outpatients schedule of doctor and Cashier members. Second, The consolidation of the general Reception desk and special inspection the Reception Desk. A movement line and waiting time of patients were simple and fast to accept. As a result of study, it shows that the TOC is the method for a job process and waiting time improvement, patients' satisfaction increase and we need an objective measurement indexes in the medical treatment industry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.228-234
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• 2006

This paper describes a systematic process which can generate a complete circuit specification efficiently for a given recursive DSP algorithm based on an optimal multiprocessor scheduler. The process is composed of two states: scheduling and circuit synthesis. The scheduling part accepts a fully specified flow graph(FSFG) as an input, and generates an optimal synchronous multiprocessor schedule. Then the circuit synthesis part translates the modified schedule into a complete circuit diagram including a control specification. The circuit diagram can be applied to a silicon compiler for VLSI layout generation. This paper illustrates the whole process with an example of a second order Gray-Market lattice filter.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.117-129
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• 2008

The cost-based scheduling work has been done in both the Operations Research (OR) and Artificial Intelligence (AI) literature. To deal with more realistic problems, AI-based heuristic scheduling approach with non-regular performance measures has been studied. However, there has been little research effort to develop a full inter-job cost propagation algorithm (CPA) for different jobs having multiple downstream and upstream activities. Without such a CPA, decision-making in scheduling heuristics relies upon local, incomplete cost information, resulting in poor schedule performance from the overall cost minimizing objective. For such a purpose, we need two types of CPAs : intra-job CPA and inter-job CPA. Whenever there is a change in cost information of an activity in a job in the process of scheduling, the intra-job CPA updates cost curves of other activities connected through temporal constraints within the same job. The inter-job CPA extends cost propagation into other jobs connected through precedence relationships. By utilizing the cost information provided by CPAs, we propose cost-based scheduling heuristics that attempt to minimize the total schedule cost. This paper develops inter-job CPAs that create and update cost curves of each activity in each search state, and propagate cost information throughout a whole network of temporal constraints. Also we propose various cost-based scheduling heuristics that attempt to minimize the total schedule cost by utilizing the cost propagation algorithm.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.57-64
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• 2020

Today, with the development of information technology, hospitals are actively researching hospital information systems that are not limited by time and space to integrate mobile computing technology into the medical field to manage the bulk data of medical information. Nevertheless, most hospitals still spend a lot of time and effort creating manual schedules. In this paper, we studied an optimization model for organizing nurses' shift work and constructed an automated nurse-type job organization system. For nurses working in S hospital, information data, requirements and constraints of nurses were constructed. By applying this, we proposed an optimized scheduling method and built a web-based platform used by head nurses and a mobile app platform used by general nurses to enable real-time interchange and sharing around web servers. Therefore, through the developed nurse needs type automated system, the head nurses will increase the convenience of the nurses to organize the work every month, and general nurses will help them to work more accurately through personal schedule management. It is also expected to increase work efficiency by sharing work schedules among nurses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.287-307
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• 2018

There are two targets to schedule parallel jobs in the Cloud: (1) scheduling the jobs as many as possible, and (2) reducing the average execution time of the jobs. Most of previous work mainly focuses on the computing speed of resources without considering other attributes, such as bandwidth, memory and so on. Especially, past work does not consider the supply-demand condition from those attributes. Resources have different attributes, considering those attributes together makes the scheduling problem more difficult. This is the problem that we try to solve in this paper. First of all, we propose a new parallel job scheduling method based on a classification method of resources from different attributes, and then a scheduling method-CPLMT (Cloud parallel scheduling based on the lists of multiple attributes) is proposed for the parallel tasks. The classification method categories resources into different kinds according to the number of resources that satisfy the job from different attributes of the resource, such as the speed of the resource, memory and so on. Different kinds have different priorities in the scheduling. For the job that belongs to the same kinds, we propose CPLMT to schedule those jobs. Comparisons between our method, FIFO (First in first out), ASJS (Adaptive Scoring Job Scheduling), Fair and CMMS (Cloud-Minmin) are executed under different environments. The simulation results show that our proposed CPLMT not only reduces the number of unfinished jobs, but also reduces the average execution time.