• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.511-520
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• 2004

A peer-to-peer model is very useful in solving the server link bottleneck problem of a client-server model. In this work, we discuss the problems of distributing multimedia content over peer-to-peer network. We focus on two problems in peer-to-peer media content distribution systems. The first is the transmission scheduling of the media data for a multi-source streaming session. We present a sophisticated scheduling scheme called fixed-length slotted scheduling, which results in minimum buffering delay. The second problem is on the fast distribution of media content in the peer-to-peer system that is self-growing. We propose a mechanism accelerating the speed at which the system's streaming ca-pacity increases, called FAST.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.1
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• pp.119-135
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• 1995

We consider problems of tool loading and scheduling in a flexible manufacturing system (FMS) in which tool transportation constitutes the major portion of material flows. In this type of FMSs, parts are initially assigned to machines and released to the machines according to input sequencing rules. Operations for the parts released to the machines are performed by tools initially loaded onto the machines or provided by an automatic tool transport robot when needed. For an efficient operation of such systems, therefore, we may have to consider loading and scheduling problems for tools in addition to those for parts. In this paper, we consider three problems, part loading, tool loading, and tool scheduling problems with the overall objective of minimizing the makespan. The part loading problem is solved by a method similar to that for the bin packing problem and then a heuristic based on the frequency of tool usage is applied for tool loading. Also suggested are part input sequencing and tool scheduling rules. To show the effectiveness of the overall algorithm suggested here, we compare it with an existing algorithm through a series of computational tests on randomly generated test problems.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.31-37
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• 2006

To achieve the efficient performance within a mobile grid considering the intermittent network connectivity and non-dedicated heterogeneous mobile devices, this paper suggests the scheduling algorithm of job allocation as a viable solution. The suggested scheduling algorithm has two core functions, the prediction of response time for task processing and the identification of the optimal number of mobile devices to process the mobile grid applications. This scheduling algorithm suggests the numerical formulas to calculate the network latency considering the effects of heterogeneous non-dedicated mobile system in wireless network environments. Also we evaluate the performance of mobile grid system using the processing the distributed applications in implemented mobile grid environments.

• Korean Journal of Computational Design and Engineering
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• v.17 no.1
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• pp.26-34
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• 2012

Assembly scheduling in shipbuilding is responsible for determine assembly process orders and departmental production schedule for the block, the basic composite unit of ships. It is necessary much more information to decide production scheduling as the characteristic of shipbuilding which has been more complex and more various and also, a lot of waste of time and of human power is generated in the course of data processing. The target shipbuilding manufacturer of this study use empirical techniques, based on the user's discretion, to compile and to apply data which are scattered in DB storages separately. Because of that reason, the user should not only be performed identification and screening operations but also modification and verification for vast amounts of data, so it is hard to keep the consistency of the data and also the operation time is not constant. Accordingly, the object in this study is by presenting an efficient DB framework to reduce wasting time and man-hour at experienced-oriented process, abate user's manual operations and support an efficient scheduling in assembly processes.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.119-126
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• 2006

The task scheduling in multiprocessor system is one of the key elements in the effective utilization of multiprocessor systems. The optimal assignment of tasks to multiprocessor is, in almost practical cases, an NP-hard problem. Consequently algorithms based on various modern heuristics have been proposed for practical reason. This paper proposes a new task scheduling algorithm using Genetic Algorithm which combines simulated annealing (GA+SA) in multiprocessor environment. In solution algorithms, the Genetic Algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize makespan. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the result of proposed algorithm is better than that of any other algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4988-5012
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• 2019

As a subset of the Internet of Things, the Internet of Energy (IoE) is expected to tackle the problems faced by the current smart grid framework. Notably, the conventional day-ahead power scheduling of the smart grid should be redesigned in the IoE architecture to take into consideration the intermittence of scattered renewable generations, large amounts of power consumption data, and the uncertainty of the arrival time of electric vehicles (EVs). Accordingly, a day-ahead power scheduling system for the future IoE is proposed in this research to maximize the usage of distributed renewables and reduce carbon emission caused by the traditional power generation. Meanwhile, flexible charging mechanism of EVs is employed to provide preferred charging options for moving EVs and flatten the load profile simultaneously. The simulation results revealed that the proposed power scheduling mechanism not only achieves emission reduction and balances power load and supply effectively, but also fits each individual EV user's preference.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.234-240
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• 1999

This paper presents a new approach for large-scale generator maintenance scheduling optimizations. The generator preventive maintenance scheduling problems are typical discrete dynamic n-dimensional vector optimization ones with several inequality constraints. The considered objective function to be minimized a subset of{{{{ { R}^{n } }}}} space is the variance (i.g., second-order momentum) of operating reserve margin to levelize risk or reliability during a year. By its nature of the objective function, the optimal solution can only be obtained by enumerating all combinatorial states of each variable, a task which leads to computational explosion in real-world maintenance scheduling problems. This paper proposes a new priority search mechanism based on each generator's discrete sensitivity value which was analytically developed in this study. Unlike the conventional capacity-based priority search, it can prevent the local optimal trap to some extents since it changes dynamically the search tree in each iteration. The proposed method have been applied to two test systems (i.g., one is a sample system with 10 generators and the other is a real-world lage scale power system with 280 generators), and the results anre compared with those of the conventional capacith-based search method and combinatorial optimization method to show the efficiency and effectiveness of the algorithm.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.309-319
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• 2000

The purpose of a parallel scheduling under a multiprocessor environment is to carry out the scheduling with the minimum synchronization overhead, and to perform load balance for a parallel application program. The processors calculate the chunk of iteration and are allocated to carry out the parallel iteration. At this time, it frequently accesses mutually exclusive global memory so that there are a lot of scheduling overhead and bottleneck imposed. And also, when the distribution of the parallel iteration in the allocated chunk to the processor is different, the different execution time of each chunk causes the load imbalance and badly affects the capability of the all scheduling. In the paper. we investigate the problems on the conventional algorithms in order to achieve the minimum scheduling overhead and load balance. we then present a new parallel loop scheduling algorithm, considering the locality of the data and processor affinity.

• Journal of Computing Science and Engineering
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• v.8 no.1
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• pp.43-56
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• 2014

Semi-partitioned scheduling is a new approach for allocating tasks on multiprocessor platforms. By splitting some tasks between processors, semi-partitioned scheduling is used to improve processor utilization. In this paper, a new semi-partitioned scheduling algorithm called SS-DRM is proposed for multiprocessor platforms. The scheduling policy used in SS-DRM is based on the delayed rate monotonic algorithm, which is a modified version of the rate monotonic algorithm that can achieve higher processor utilization. This algorithm can safely schedule any system composed of two tasks with total utilization less than or equal to that on a single processor. First, it is formally proven that any task which is feasible under the rate monotonic algorithm will be feasible under the delayed rate monotonic algorithm as well. Then, the existing allocation method is extended to the delayed rate monotonic algorithm. After that, two improvements are proposed to achieve more processor utilization with the SS-DRM algorithm than with the rate monotonic algorithm. According to the simulation results, SS-DRM improves the scheduling performance compared with previous work in terms of processor utilization, the number of required processors, and the number of created subtasks.