• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.108-117
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• 2015

Interrupt handling is generally separated from process scheduling. This can lead to a scheduling anomaly and priority inversion. The processor can interrupt a higher priority process that is currently executing, in order to handle a network packet reception interruption on behalf of its intended lower priority receiver process. We propose a new network interrupt handling scheme that combines interrupt handling with process scheduling and the priority of the process. The proposed scheme employs techniques to identify the intended receiver process of an incoming packet at an earlier phase. We implement a prototype system of the proposed scheme on Linux 2.6, and our experiment results show that the prototype system supports the predictable real-time behavior of higher priority processes even when excessive traffic is sent to lower priority processes.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.3
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• pp.31-40
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• 1993

This paper deals with the modeling and scheduling for FMC(Flexible Manufacturing Cells). The FMC system composed of unit or multi cells is capable of improving productivity with flexibility for machine. However, the properties of multiple jobs and various alternatives results in tne dynamic states which make system management very complex. The extended Peti nets are used to represent for complex properties of FMC which performs short-term scheduling and dynamic operational scheduling. The hierarchical control structure and integlligent scheduling through expert module are adopted for efficiency of FMC operations. The computer simulation reveals that intelligent scheduling method is better than heuristics in various performance indices.

• Industrial Engineering and Management Systems
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• v.10 no.3
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• pp.203-208
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• 2011

Job shop scheduling is well-known as one of the hardest combinatorial optimization problems and has been demonstrated to be NP-hard problem. In the past decades, several researchers have devoted their effort to develop evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for job shop scheduling problem. Differential Evolution (DE) algorithm is a more recent evolutionary algorithm which has been widely applied and shown its strength in many application areas. However, the applications of DE on scheduling problems are still limited. This paper proposes a one-stage differential evolution algorithm (1ST-DE) for job shop scheduling problem. The proposed algorithm employs random key representation and permutation of m-job repetition to generate active schedules. The performance of proposed method is evaluated on a set of benchmark problems and compared with results from an existing PSO algorithm. The numerical results demonstrated that the proposed algorithm is able to provide good solutions especially for the large size problems with relatively fast computing time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3096-3117
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• 2013

To execute the performance driven Grid applications, an effective and scalable workflow scheduling is seen as an essential. To optimize cost & makespan, in this paper, we propose a Scalable Cost-Time Trade-off (SCTT) model for scheduling workflow tasks. We have developed a heuristic algorithm known as Scalable Cost-Time Trade-off Scheduling (SCTTS) with a lower runtime complexity based on the proposed SCTT model. We have compared the performance of our proposed approach with other heuristic and meta-heuristic based scheduling strategies using simulations. The results show that the proposed approach improves performance and scalability with different workflow sizes, task parallelism and heterogeneous resources. This method, therefore, outperforms other methods.

• Journal of Intelligence and Information Systems
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• v.2 no.1
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• pp.75-91
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• 1996

제철소에서의 원료 운송 작업은, 선박에 선적된 원료를 하역하여 야드에 적치한 후, 제반 원료 소요공장까지의 벨트 컨베이어를 통한 원료 수송 작업을 의미한다. 본 연구에서는 원료 운송과 관련한 선박 접안, 원료 하역 및 벨트 컨베이어 수송 일정 계획을 효과적으로 수립하기 위하여 이단계 계획적 구조를 이용한 일정계획 휴리스틱을 개발하고 이를 구현하기 위한 전문가 시스템을 개발하였다. 이단계 계층적 구조는 상위 단계의 Scheduler 와 하위 단계의 복수개의 Dispatcher 로 구성되어 있다. 하위 단계의 Dispatcher는 주어진 제약조건하에서 단위 문제를 해결하고 이를 상위 단계의 Scheduler에게 보고하게 된다. 상위 단계의 Scheduler 는 전체적인 문제 해결의 우선 순위 결정 및 Dispatcher 간의 상층을 해결하는 역할을 맡게 된다. 이러한 계층적 구조를 이용한 분산처리를 통해 문제의 복잡성을 줄이고, 시스템 설계의 모듈화 및 유연성있는 시스템 구축이 가능하게 되었다. 본 시스템은 실시간 전문가 시스템 도구인 G2를 이용하여 SUN Workstation에서 개발되었다.

• Journal of the Korea Safety Management & Science
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• v.6 no.1
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• pp.219-228
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• 2004

We can be acquire the conformation about on the due date of supplier by using the A TP(A vail able to Promise) function of management about real and concurrent access on the supply chain, also decide the affect about product availability due to forecasting or customer's orders through the A TP. Under the these environments, defines the A TP rule that can improve the customer value and data flow related the LTV(Life Time Value) and builds on a algorithm. In this paper, It consolidates the necessity on a LTV (Life Time Value) and analyzes data which is concerned of Customer Value. Under the these environments, defines the LTV rule that can improve the customer value. And then, Scheduling plays an important role in shop floor planning. Therefore, this study tries to proposed that Scheduling by customer needs group for minimizing the problem.

• Korean Management Science Review
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• v.34 no.1
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• pp.47-56
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• 2017

We focus on the Real time Fire Scheduling Problem (RFSP), the problem of determining the sequence of targets to be fired at, for the objective of minimizing threatening probability to achieve tactical goals. In this paper, we assume that there are m available weapons to fire at n targets (> m) and the weapons are already allocated to targets. One weapon or multiple weapons can fire at one target and these fire operations should start simultaneously while the finish time of them may be different. We suggest mathematical modeling for RFSP and several heuristic algorithms. Computational experiments are performed on randomly generated test problems and results show that the suggested algorithms outperform the firing method which is generally adopted in the field artillery.

• Korean Management Science Review
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• v.14 no.2
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• pp.47-61
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• 1997

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.3
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• pp.83-91
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• 2014

We consider the problem of scheduling identical jobs with sequence-independent setup times on parallel machines. The objective is to minimize total completion times. We present the pseudopolynomial-time algorithm for the case with a fixed number of machines and an efficient approximation algorithm for our problem with identical setup times, which is known to be NP-hard even for the two-machine case.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.249-252
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• 2004

The automatic determination of vehicle operation status as well as continuous tracking of vehicle location with intelligent management is one of major elements to achieve the goals. Especially, vehicle operation status can only be analyzed in terms of expert experiences with real-time location data with scheduling information. However the scheduling information of individual vehicle is very difficult to be interpreted immediately because there are hundreds of thousand vehicles are run at the same time in the national wide range workplace. In this paper, we propose the location-based knowledge management system(LKMs) using the active trajectory analysis method with routing and scheduling information to cope with the problems. This system uses an inference technology with dispatching and geographic information to generate the logistics knowledge that can be furnished to the manager in the central vehicle monitoring and controlling center.