• 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.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.2
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• pp.265-272
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• 1996

In this paper, attitude computation algorithm for a strap down ARS(Attitude Reference System)of an underwater vehicle has been studied. Attitude errors o the ARS using low-level gyroscopes tend to increase with time due to gyroscope errors. To cope with this problem, a mixing algorithm of accelerometer aided attitude computation has been developed. The algorithm can successfully bound the error increase for cruising motion, but it gives instantaneously large errors when a vehicle maneuvers. To improve the performance in case of vehicle's maneuver, a new attitude computation mixing algorithm complying state of vehicle and to manage the adjustment of the gains which are invariant in the existing algorithm. In addition, a gain scheduling method is applied to fuzzy inference composition process for real-time computation. Monte Carlo simulation results show that the proposed algorithm provides better performance than the existing algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.88-102
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• 2016

This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13~33.34 seconds depending on the size of scenarios.

• Industrial Engineering and Management Systems
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• v.10 no.2
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• pp.134-139
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• 2011

The problem of scheduling in permutation flowshops has been extensively investigated by many researchers. Recently, attempts are being made to consider more than one objective simultaneously and develop algorithms to obtain a set of Pareto-optimal solutions. Varadharajan et al. (2005) presented a multi-objective simulated-annealing algorithm (MOSA) for the problem of permutation-flowshop scheduling with the objectives of minimizing the makespan and the total flowtime of jobs. The MOSA uses two initial sequences obtained using heuristics, and seeks to obtain non-dominated solutions through the implementation of a probability function, which probabilistically selects the objective of minimizing either the makespan or the total flowtime of jobs. In this paper, the same problem of heuristically developing non-dominated sequences is considered. We propose an effective heuristics based on simulated annealing (SA), in which the weighted sum of the makespan and the total flowtime is used. The essences of the heuristics are in selecting the initial sequence, setting the weight and generating a solution in the search process. Using a benchmark problem provided by Taillard (1993), which was used in the MOSA, these conditions are extracted in a large-scale experiment. The non-dominated sets obtained from the existing algorithms and the proposed heuristics are compared. It was found that the proposed heuristics drastically improved the performance of finding the non-dominated frontier.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.199-206
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• 2008

The introduction of competition in electricity markets emphasizes the importance of sufficient transmission capacities to guarantee effective power transactions. Therefore, for the economic and stable electric power system operation, transmission security constrains should be incorporated into the dispatch scheduling problem. With the intent to solve this problem, we decompose a dispatch scheduling problem into a master problem(MP) and several subproblems(SPs) using Benders decomposition. The MP solves a general optimal power flow(OPF) problem while the SPs inspect the feasibility of OPF solution under respective transmission line contingencies. If a dispatch scheduling solution given by the MP violates transmission security constraints, then additional constraints corresponding to the violations are imposed to the MP. Through this iterative process between the MP and SPs, we derive an optimal dispatch schedule incorporating the post-contingency corrective rescheduling. In addition, we consider interruptible loads as active control variables since the interruptible loads can participate as generators in competitive electricity markets. Numerical examples demonstrate the efficiency of the proposed algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.4
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• pp.587-593
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• 2006

Development of digital information and internet technology causes the changes of technology environments and companies, and the variety of customers needs has been dynamically changed in terms of integrating information system with customers satisfaction. Moreover a new logistics concept is needed through the sharing information between suppliers and consumers, which maximizes the customers service and its flexibility by changing functional-oriented to process-oriented. Many research papers on transportation studies have focused on the Vehicle Routing Problem (VRP) and Vehicle Scheduling Problem (VSP). However in the real world, it is known that it takes long time to build vehicle scheduling in the process of transporting the amount of orders from the logistics center to the vendors due to the realistic constraints. This paper presents a framework design for each process enabling delivery planning automatically using heuristic algorithm. In addition, an interactive delivery planning system is implemented utilizing the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11A
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• pp.859-866
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• 2003

In this paper, we designed the bandwidth allocation scheme with priority consideration for upstream channel access of EthernetPON. The objective of our scheme is to control the multi services in more effective way according to their CoS(Class of Service) or QoS(Quality of Service). The designed scheme considers transmission priority in the both side of OLT and ONU. In the OLT's view, the Two-step scheduling algorithm is applied with which we can support multiple bandwidth allocation policies simultaneously, i.e. SBA for the time-sensitive, constant rate transmission services and DBA for the best-effort services. This Two-step scheduling algorithm reduces the scheduling complexity by separating the process of transmission start time decision from the process of grant generation. In the ONU's view, the proposed scheme controls 8 priority queues of the 802.1d recommended 8 service classes. Higher priority queue is serviced in prior during the allowed GATE time from OLT. The OPNET modeling and simulation result compares the performance of each bandwidth allocation policy with SBA or DBA only approach.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.728-736
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• 2009

We propose a method using the genetic algorithm to solve the maximum set cover problem. It is needed for scheduling the power of sensor nodes in extending the lifetime of the wireless sensor network with variable sensing range. The existing Greedy Heuristic method calculates the power scheduling of sensor nodes repeatedly in the process of operation, and so the communication traffic of sensor nodes is increased. The proposed method reduces the amount of communication traffic of sensor nodes, and so the energies of nodes are saved, and the lifetime of network can be extended. The effectiveness of this method was verified through computer simulation, and considering the energy losses of communication operations about 10% in the network lifetime is improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3126-3145
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• 2014

Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. A spot instance in cloud computing helps a user to obtain resources at a lower cost. However, a crucial weakness of spot instances is that the resources can be unreliable anytime due to the fluctuation of instance prices, resulting in increasing the failure time of users' job. In this paper, we propose a Genetic Algorithm (GA)-based workflow scheduling scheme that can find the optimal task size of each instance in a spot instance-based cloud computing environment without increasing users' budgets. Our scheme reduces total task execution time even if an out-of-bid situation occurs in an instance. The simulation results, based on a before-and-after GA comparison, reveal that our scheme achieves performance improvements in terms of reducing the task execution time on average by 7.06%. Additionally, the cost in our scheme is similar to that when GA is not applied. Therefore, our scheme can achieve better performance than the existing scheme, by optimizing the task size allocated to each available instance throughout the evolutionary process of GA.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.213-224
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• 2009

This paper describes a real-time scheduling system re-construction project for automated manufacturing at a 300mm wafer fab of Korean semiconductor manufacturing company. During executing this project, for each main operation such as clean, diffusion, deposition, photolithography, and metallization, each adopted scheduling algorithm was developed, and then those were implemented in a real-time scheduling system. In this paper, we focus on the scheduling algorithms and real-time scheduling system for clean and diffusion operations, that is, a serial-process block with the constraint of limited queue time and batch processors. After this project was completed, the automated manufacturing utilizations of clean and diffusion operations became around 91% and 83% respectively, which were about 50% and 10% at the beginning of this project. The automated manufacturing system reduces direct operating costs, increased throughput on the equipments, and suggests continuous and uninterrupted processings.