• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.1 no.1
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• pp.35-53
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• 1997

This paper analyzes the time complexity of Genetic Algorithm based Task Scheduling (GATS) which is designed for the scheduling of parallel programs with diverse embedded parallelism types in a heterogeneous network systems. The analysis of time complexity is performed based on two representation methods (REIA, REIS) which are proposed in this paper to encode the scheduling information. And the heterogeneous network systems consist of a set of loosely coupled parallel and vector machines connected via a high-speed network. The objective of heterogeneous network computing is to solve computationally intensive problems that have several types of parallelism, on a suite of high performance and parallel machines in a manner that best utilizes the capabilities of each machine. Therefore, when scheduling in heterogeneous network systems, the matching of the parallelism characteristics between tasks and parallel machines should be carefully handled in order to obtain more speedup. This paper shows how the parallelism type matching affects the time complexity of GATS.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.3
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• pp.190-196
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• 2003

We consider the problem of scheduling n jobs on m machines with the objective of minimizing makespan. Each job cannot be eligible to all the machines but to its consecutively eligible set of machines. For this problem, a 2-approximation algorithm, MFFH, is developed. In addition, an example is presented to show the tightness of the algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.7
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• pp.27-33
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• 2001

• Management Science and Financial Engineering
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• v.11 no.2
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• pp.19-43
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• 2005

This paper considers a variation of the customer order scheduling problem, and the variation is the case where the machine-job assignment is fixed. We examine the parallel machine environment, and the objective is to minimize the sum of the completion times of the batches. While a machine can process only one job at a time, different machines can simultaneously process different jobs in a batch. The recognition version of this problem is known to be NP-complete in the strong sense even if there exist only two parallel machines. When there are an arbitrary number of parallel machines, we establish three lower bounds and develop a dynamic programming (DP) algorithm which runs in exponential time on the number of batches. We present two simple but intuitive heuristics, SB and GR, and find some special cases where SB and GR generate an optimal schedule. We also find worst case upper bounds on the relative error. For the case of the two parallel machines, we show that GR generates an optimal schedule when processing times of all batches are equal. Finally, the heuristics and the lower bounds are empirically evaluated.

• The Transactions of the Korea Information Processing Society
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• v.2 no.4
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• pp.535-542
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• 1995

In this paper we present two preconditioners for solving large sparse linear systems arising from elliptic partial differential equations on massively parallel machines, such as the CM-5. Most massively parallel machines do heavily rely on the message-passing for the interprocessor communications. but according to the current manufacturing standards the cost of communications is very high compared to that of floating point arithmetic computations. Due to this we need an algorithm which minimizes the amount of interprocessor communication on the massively parallel machines. We will show that Block SOR(Successive Over Relaxation) method coupled with the multi-coloring technique is one of such preconditioner on the massively parallel machines, by conducting experiments in the CM-5. Also, we implemented the ADI(Alternation Direction Implicit) method in the CM-5, which has been conventionally one of the most powerful parallel preconditioner. Our experiment shows that Block SOR method coupled with the multi-coloring technique could yield a speedup with 50% efficiency with the range of number of processors form 16 to 512 for a matrix with dimension 512x512. On the other hand, the ADI method shows a very poor performance.

• Korean Management Science Review
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• v.30 no.3
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• pp.81-97
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• 2013

This paper considers a parallel-machine scheduling problem with dedicated and common processing machines. Non-identical setup and processing times are assumed for each machine. A genetic algorithm is proposed to minimize the makespan objective measure. In this paper, a lowerbound and some heuristic algorithms are derived and tested through computational experiments.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.36-41
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• 2001

In this paper, we first design an parallel quadratic sieve algorithm for factoring method. We then present parallel factoring algorithm for factoring a large odd integer by repeatedly using the parallel quadratic sieve algorithm based on the divide-and-conquer strategy on SIMD machines with DMM. We show that this algorithm is optimal in view of the product of time and processor numbers.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.360-368
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• 1999

We consider the problem of scheduling n jobs with sequence-dependent processing times on a set of parallel-identical machines. The processing time of each job consists of a pure processing time and a sequence-dependent setup time. The objective is to maximize the total remaining machine available time which can be used for other tasks. For the problem, a hybrid genetic algorithm is proposed. The algorithm combines a genetic algorithm for global search and a heuristic for local optimization to improve the speed of evolution convergence. The genetic operators are developed such that parallel machines can be handled in an efficient and effective way. For local optimization, the adjacent pairwise interchange method is used. The proposed hybrid genetic algorithm is compared with two heuristics, the nearest setup time method and the maximum penalty method. Computational results for a series of randomly generated problems demonstrate that the proposed algorithm outperforms the two heuristics.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.1
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• pp.37-50
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• 1998

This paper considers the nonidentical parallel machine scheduling problem in which n jobs having different due dates are to be scheduled on m nonidentical parallel machines. For the make-to-order manufacturing environment, the objective is to minimize the number of tardy jobs. A 0-1 nonlinear programming model is formulated and a heuristic algorithm that allocates and sequences jobs to machines is developed. The proposed algorithm makes use of the concept of assignment problem based on the suitability measure as the cost coefficient. Computational experiments show that the proposed algorithm is superior to the existing one in some performance measures such as number of tardy jobs. In addition, this algorithm is appropriate for solving real industrial problems efficiently.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.880-884
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• 1995

Manufacturing environment is getting characterized by unstable market demand,short product life cycle and timebased competition. For adapting this environment,machine tools have to be further versatile functionally in order to reduce part's set-up time. Unlike existing manufacturing systems mainly to focus on part flow, it is important to control tool flow using fast tool change device and tool delivery device in parallel machines consisting of versatile machine tools, because complete operations on a part can be performed on one machine tool in a single machine set-up. In this paper, under dynamic tool allocation strategy to share tools among machine tools, we propose a real-time tool allocation and operation esequence model with an objective of minimizing flow time using autonomy and negotiation of agents in parallel machines