• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제1권1호
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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.

• 대한산업공학회지
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• 제29권3호
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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.

• 한국정밀공학회지
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• 제18권7호
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• pp.27-33
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• 2001

• Management Science and Financial Engineering
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• 제11권2호
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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.

• 한국정보처리학회논문지
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• 제2권4호
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• pp.535-542
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• 1995

이 논문에서 우리는 CM-5와 같은 초대형병렬컴퓨터에서 대형 이산선형체제를 풀기 위한 준비행렬로써 두 가지를 소개한다. 대다수의 초대형병렬컴퓨터들은 프로세서간의 통신을 메세지패씽(messagepassing)에 의존하는데 현재의 기술수준하에서는 이 통신속 도가 실수계산속도에 비해 매우 느리므로 종래의 메모리공유컴퓨터에서와는 달리 데이 터통신량을 최소화하는 알고리듬이 요구된다. 블록 SOR에 다중색채기법을 가미한 알고 리듬이 그 한 예로써 우리는 이를 CM-5에서 구현한 결과 N=512x512 행렬에서 프로세서 의 수가 16에서 512의 범위 하에서 50%의 효율을 실현하였다. 반면 종래의 효율적인 병렬 준비행렬로 알려진 AKI알고리듬은 방대한 량의 데이터통신 때문에 매우 열등한 결과를 보여준다.

• 경영과학
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• 제30권3호
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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.

• 정보처리학회논문지A
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• 제8A권1호
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• pp.36-41
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• 2001

본 논문에서는 첫째로 큰 정수의 소인수 분해를 위한 병렬 이차선별법(parallel quadratic sieve) 알고리즘을 제시한다. 이 알고리즘을 반복적으로 사용하여, 분산 메모리 모델(DMM)을 갖는 SIMD구조의 병렬 컴퓨터 상에서 분할정복기법을 사용하는 병력 소인수 분해(parallel factoring) 알고리즘을 제시한다. 또한 이러한 알고리즘이 시간과 프로세서의 곱의 관점에서 최적화 알고리즘임을 보인다.

• 대한산업공학회지
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• 제25권3호
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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.

• 대한산업공학회지
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• 제24권1호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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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