• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.89-100
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• 2022

With the advancement in the Internet of things Technology (IoT) cloud computing, billions of physical devices have been interconnected for sharing and collecting data in different applications. Despite many advancements, some latency - specific application in the real world is not feasible due to existing constraints of IoT devices and distance between cloud and IoT devices. In order to address issues of latency sensitive applications, fog computing has been developed that involves the availability of computing and storage resources at the edge of the network near the IoT devices. However, fog computing suffers from many limitations such as heterogeneity, storage capabilities, processing capability, memory limitations etc. Therefore, it requires an adequate task scheduling method for utilizing computing resources optimally at the fog layer. This work presents a comprehensive review of different task scheduling methods in fog computing. It analyses different task scheduling methods developed for a fog computing environment in multiple dimensions and compares them to highlight the advantages and disadvantages of methods. Finally, it presents promising research directions for fellow researchers in the fog computing environment.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.99-102
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• 2000

전통적인 job shop 일정계획문제는 NP_hard 문제로 조합최적화 문제이다 일반적인 가정은 job이 방문하는 기계들의 경로가 고정되어 있다는 것이다. 경로 유연성을 가지는 job shop 일정계획문제는 job이 방문하는 기계들의 경로가 고정되어져 있지 않다는 것이다. 이러한 경우에 전통적인 job shop 문제를 복잡하게 만든다. 경로 유연성을 가지는 job shop 문제도 NP-hard 문제이다. 그러므로 휴레스틱이나 AI 기법들을 사용하는 하는 것이 불가피하게 되었다. 유전 알고리즘은 매우 복잡한 조합 최적화문제인 job shop 일정계획문제에 적용되어지고 있다. 이 논문은 최대완료시간(makespan)으로 경로 유연성을 가지는 job shop 일정계획문제를 풀기 위한 유전 알고리즘을 제시하고자 한다. 먼저 경로 유연성을 가지는 job shop 일정계획문제에 대한 정의를 내리고 유전 알고리즘을 구축하기 위한 첫 단계로 유전적 표현 즉, 개체 표현방법에 대해 설명하고 유전 연산자의 소개 그리고 알고리즘 재생과정을 제시하고 수치실험을 통해 알고리즘이 양질의 일정계획을 찾을 수 있다는 것을 보이고자 한다.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.10a
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• pp.137-141
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• 1998

본 연구는 TPN unfolding을 이용하여 WIP의 FMS(Flexible Manufacturing System)를 분석하는 방법을 제시한다. PN의 unfolding은 상태폭발이 발생하지 않는 concurrent system의 검증에 사용되는 순서기반방법이다. 본 연구는 일반적으로 발생하는 순환상태스케쥴문제에서 가장 그 작업과정 시간을 최적화함을 위하여 원래의 net을 동일한 비순환 net으로 바꾸어 줄 수 있는 unfolding 개념을 기반으로 한 것이다.

• Journal of Industrial Convergence
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• v.21 no.4
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• pp.13-22
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• 2023

Due to COVID-19, organizations are rapidly changing the way they work by providing telecommuting and flexible work, and by expanding Smart Work spaces. In a Smart Work situation, workers have improved their job autonomy to choose their work methods, hours, and places. However, previous studies do not reflect the Smart Work situation and there are limitations to still using the previous job autonomy concept and measurements. To overcome these problems, this study derived job autonomy types such as methods, scheduling, criteria, time, and place applicable to Smart Work environments and verified that the five types of job autonomy have a statistically significant positive effect on Smart Workers' creative behavior. This study is meaningful in that it categorized job autonomy into five types applicable to Smart Work by adding temporal and spatial flexibility to the traditional job autonomy concept such as method, scheduling, and criteria autonomy and provided the basis for subdividing and evaluating the operation performance of Smart Work through multi-dimensional job autonomy verification.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.102-102
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• 1992

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.338-338
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• 1999

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.563-570
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• 2004

Genetic algorithms have been successfully applied to various optimization problems belonging to NP-hard problems. The sequential ordering problems(SOP) and the job shop scheduling problems(JSP) are well-known NP-hard problems with strong influence on industrial applications. Both problems share some common properties in that they have some imposed precedence constraints. When genetic algorithms are applied to this kind of problems, it is desirable for genetic operators to be designed to produce chromosomes satisfying the imposed precedence constraints. Several genetic operators applicable to such problems have been proposed. We call such genetic operators precedence-preserving genetic operators. This paper presents three existing precedence-preserving genetic operators: Precedence -Preserving Crossover(PPX), Precedence-preserving Order-based Crossover (POX), and Maximum Partial Order! Arbitrary Insertion (MPO/AI). In addition, it proposes two new operators named Precedence-Preserving Edge Recombination (PPER) and Multiple Selection Precedence-preserving Order-based Crossover (MSPOX) applicable to such problems. It compares the performance of these genetic operators for SOP and JSP in the perspective of their solution quality and execution time.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.4
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• pp.201-212
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• 2002

This paper proposes a reconfigurable scheduler model that can support various real-time scheduling algorithms. The proposed model consists of two hierarchical upper and lower components, task scheduler and scheduling framework, respectively. The scheduling framework provides a job dispatcher and software timers. The task scheduler implements an appropriate scheduling algorithm, which supports a specific real-time application, based on the scheduling framework. If system developers observe internal kernel interfaces to communicate between two hierarchical components, they can implement a new scheduling algorithm independent of complex low kernel mechanism. Once a task scheduler is developed, it can be reused in a new real-time system in future. In Real-Time Linux (5), we implemented the proposed scheduling framework and several representative real-time scheduling algorithms. Throughout these implementations, we confirmed that a new scheduling algorithm could be developed independently without updates of complex low kernel modules. In order to confirm efficiency of the proposed model, we measured the performance of representative task schedulers. The results showed that the scheduling overhead of proposed model, which has two separated components, is similar to that of a classic monolithic kernel scheduler.

• Journal of Information Processing Systems
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• v.5 no.2
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• pp.87-96
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• 2009

Grid computing is a new technology which involves efforts to create a huge source of processing power by connecting computational resources throughout the world. The key issue of such environments is their resource allocation and the appropriate job scheduling strategy. Several approaches to scheduling in these environments have been proposed to date. Market driven scheduling as a decentralized solution for such complicated environments has introduced new challenges. In this paper the bidding problem with regard to resources in the reverse auction resource allocation model has been investigated and the new bidding strategies have been proposed and investigated.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.383-391
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• 2005

The paper is devoted to some flow shop scheduling problems, where job processing times are defined by functions dependent on their positions in the schedule. An example is constructed to show that the classical Johnson's rule is not the optimal solution for two different models of the two-machine flow shop scheduling to minimize makespan. In order to solve the makespan minimization problem in the two-machine flow shop scheduling, we suggest Johnson's rule as a heuristic algorithm, for which the worst-case bound is calculated. We find polynomial time solutions to some special cases of the considered problems for the following optimization criteria: the weighted sum of completion times and maximum lateness. Some furthermore extensions of the problems are also shown.