• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 춘계학술발표대회
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• pp.241-242
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• 2010

분산 Job Scheduling 문제에서 Makespan 은 항상 타 성능지표를 대표하는 단일 목표치 (Objective)가 되기 어려운 측면이 있다. 그러나 기존의 Job Scheduler 관련 제안들은 Makespan 만을 단일 목표치로 최적화 시킴으로써, 성능적 우수성을 입증하는 한계점이 있었다. 그러므로 본고에서는 Makespan 및 Throughput 을 동시에 최소화하여 개별 가중치로 정량화될 수 있는 다양한 성능 요구사항에 적합한 복수 대안 (Scheduling Alternatives)들을 제공할 수 있는 GA 기반 스케줄링 기법에 대해 제안한다.

• 한국산업융합학회 논문집
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• 제12권1호
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• pp.19-25
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• 2009

We suggest and evaluate a dynamic scheduling rule of machines and material handling systems for on-line operation in job shop type Flexible Manufacturing System. Alternating status should be able to take operation scheduling procedures and without delay in dynamic industrial environments effectively. The interaction(SPT-NS, SPT-QSNS, SPT-NUJ, EDD-NS, EDD-QSNS, EDD-NUJ, CR-NS, CR-QSNS, CR-NUJ) between machine operation scheduling and AGV dispatching rule were also studied in this research. The performance evaluation which was obtained from DSS compares the performance of Flow time, and Empty to loaded travel ratio. It is Compared with the best rules & two system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1302-1320
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• 2015

In cloud, everything can be provided as a service wherein a large number of users submit their jobs and wait for their services. hus, scheduling plays major role for providing the resources efficiently to the submitted jobs. The brainwave of the proposed ork is to improve user satisfaction, to balance the load efficiently and to bolster the resource utilization. Hence, this paper roposes an Adaptive Multilevel Scheduling System (AMSS) which will process the jobs in a multileveled fashion. The first level ontains Preprocessing Jobs with Multi-Criteria (PJMC) which will preprocess the jobs to elevate the user satisfaction and to itigate the jobs violation. In the second level, a Deadline Based Dynamic Priority Scheduler (DBDPS) is proposed which will ynamically prioritize the jobs for evading starvation. At the third level, Contest Mapping Jobs with Virtual Machine (CMJVM) is roposed that will map the job to suitable Virtual Machine (VM). In the last level, VM Scheduler is introduced in the two-tier VM rchitecture that will efficiently schedule the jobs and increase the resource utilization. These contributions will mitigate job iolations, avoid starvation, increase throughput and maximize resource utilization. Experimental results show that the performance f AMSS is better than other algorithms.

• 대한조선학회논문집
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• 제60권3호
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• pp.202-211
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• 2023

The profile shops in shipyards produce section steels required for block production of ships. Due to the limitations of shipyard's production capacity, a considerable amount of work is already outsourced. In addition, the need to improve the productivity of the profile shops is growing because the production volume is expected to increase due to the recent boom in the shipbuilding industry. In this study, a scheduling optimization was conducted for a parallel welding line of the profile process, with the aim of minimizing tardiness and the number of set-up changes as objective functions to achieve productivity improvements. In particular, this study applied a dynamic scheduling method to determine the job sequence considering variability of processing time. A Markov decision process model was proposed for the job sequence problem, considering the trade-off relationship between two objective functions. Deep reinforcement learning was also used to learn the optimal scheduling policy. The developed algorithm was evaluated by comparing its performance with priority rules (SSPT, ATCS, MDD, COVERT rule) in test scenarios constructed by the sampling data. As a result, the proposed scheduling algorithms outperformed than the priority rules in terms of set-up ratio, tardiness, and makespan.

• 대한산업공학회지
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• 제21권2호
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• pp.207-216
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• 1995

Constructing a distributed management system has its own advantages in addressing the issue of implementing a quick responsive management system in dynamically changing environment of enterprise. We suggest a basic scheduling methodology applicable to a distributed production management system. A new concept of "flexible schedule" is introduced as a tool to accommodate dynamically changing situations of job shops. Then a search technique (referred to as CSP-CBA search) is presented to obtain such a schedule for the job shop scheduling problem, which is converted into a constraint satisfaction problem(CSP), by using the constraint based analysis(CBA). This approach is tested on more than 100 test problems. The results show that the suggested approach required shorter CPU time and solved more problems in given time than another fixed schedule method.

• International Journal of Contents
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• 제6권4호
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• pp.30-34
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• 2010

Effective job scheduling scheme is a crucial part of complex heterogeneous distributed systems. Gang scheduling is a scheduling algorithm for grid systems that schedules related grid jobs to run simultaneously on servers in different local sites. In this paper, we address grid jobs (gangs) schedule modeling using Stochastic reward nets (SRNs), which is concerned for static and dynamic scheduling policies. SRN is an extension of Stochastic Petri Net (SPN) and provides compact modeling facilities for system analysis. Threshold queue is adopted to smooth the variations of performance measures. System throughput and response time are compared and analyzed by giving reward measures in SRNs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.484-503
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• 2016

Mobile cloud computing has recently become a new paradigm for the utilization of a variety of shared mobile resources via wireless network environments. However, due to the inherent characteristics of mobile devices, a limited battery life, and a network access requirement, it is necessary for mobile servers to provide a dynamic approach for managing mobile resources efficiently in mobile cloud computing environments. Since on-demand job requests occur frequently and the number of mobile devices is drastically increased in mobile cloud computing environments, a different mobile resource management method is required to maximize the computational power. In this paper, we therefore propose a cooperative, mobile resource sharing method that considers both the inherent properties and the number of mobile devices in mobile cloud environments. The proposed method is composed of four main components: mobile resource monitor, job handler, resource handler, and results consolidator. In contrast with conventional mobile cloud computing, each mobile device under the proposed method can be either a service consumer or a service provider in the cloud. Even though each device is resource-poor when a job is processed independently, the computational power is dramatically increased under the proposed method, as the devices cooperate simultaneously for a job. Therefore, the mobile computing power throughput is dynamically increased, while the computation time for a given job is reduced. We conduct case-based experiments to validate the proposed method, whereby the feasibility of the method for the purpose of cooperative computation is shown.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2008년도 한국컴퓨터종합학술대회논문집 Vol.35 No.1 (A)
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• pp.377-378
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• 2008

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.183-199
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• 2007

In this paper we research the problem in which the objective is to minimize the sum of squared deviations of job expected completion times from the due date, and the job processing times are stochastic. In the problem the machine is subject to stochastic breakdowns and all jobs are preempt-repeat. In order to show that the replacing ESSD by SSDE is reasonable, we discuss difference between ESSD function and SSDE function. We first give an express of the expected completion times for both cases without resampling and with resampling. Then we show that the optimal sequence of the problem V-shaped with respect to expected occupying time. A dynamic programming algorithm based on the V-shape property of the optimal sequence is suggested. The time complexity of the algorithm is pseudopolynomial.

• 대한산업공학회지
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• 제27권2호
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• pp.158-168
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• 2001

We present a tabu search (TS) algorithm to minimize maximum lateness on a single machine in the presence of sequence dependent setup times and dynamic job arrivals. The TS algorithm starts with a feasible schedule generated by a modified ATCS (Apparent Tardiness Cost with Setups) rule, then through a series of search steps it improves the initial schedule. Results of extensive computational experiments show that the TS algorithm significantly outperforms a well-known RHP heuristic by Ovacik and Uzsoy, both on the solutions quality and the computation time. The performance advantage is particularly pronounced when there is high competition among jobs for machine capacity.