• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.3
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• pp.113-121
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• 2014

This paper considers a scheduling problem in a two-machine flowshop with outsourcing strategy incorporated. The jobs can be either processed in the first machine or outsourced to outside subcontractors. This paper wants to determine which jobs to be processed in-house and which jobs to be outsourced. If any job is decided to be outsourced, then an additional outsourcing cost is charged The objective of this paper is to minimize the sum of scheduling cost and outsourcing cost under a budget constraint. At first this paper characterizes some solution properties, and then it derives solution procedure including DP (Dynamic Programming) and B&B (Branch-and-Bound) algorithms and a greedy-type heuristic. Finally the performance of the algorithms are evaluated with some numerical tests.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.889-900
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• 2008

The problem of scheduling n jobs on a single machine is considered when the machine is subject to stochastic breakdowns. The objective is to minimize the weighted squared deviation of job completion times from a common due date. Two versions of the problem are addressed. In the first one the common due date is a given constant, whereas in the second one the common due date is a decision variable. In each case, a general form of deterministic equivalent of the stochastic scheduling problem is obtained when the counting process N(t) related to the machine uptimes is a Poisson process. It is proved that an optimal schedule must be V-shaped in terms of weighted processing time when the agreeable weight condition is satisfied. Based on the V-shape property, two dynamic programming algorithms are proposed to solve both versions of the problem.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.624-627
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• 2014

In vehicular network, the basic goal is to provide vehicle safety service and commercial service such as ITS(Intelligent Transportation System) or video, etc on the road. And most research concentrated on transportation of safety message in congestion situation. It is important to allocate channel for safety message in congestion situation, but providing suitable service is also important problem in vehicular network. For this reason, IEEE 1609.4 allocate 4 multiple service channels (SCHs) for non-safety data transfer. But, in congestion situation with many vehicles, the contention for channel acquisition between services becomes more severe. So services are provided improperly because of lack of service channel. This paper suggests dynamic channel allocation algorithm. The proposed algorithm is that RSU(RaodSide Unit) maintain and manage the information about service and status of channels. On based of the SJF(Shortest Job First) scheduling using those information, RSU selects the most appropriate channel among the 4 SCHs allocated by IEEE 1609.4 in network congestion situation.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.3
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• pp.431-446
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• 1998

In this paper, 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, we first propose a dynamic programming(DP) algorithm for sequencing jobs processed on a single machine. The algorithm is then extended to handle jobs on parallel-identical machines. Finally, we developed an improved version of the algorithm which generates optimal solutions using much smaller amount of memory space and computing time. Computational results are provided to illustrate the performance of the DP algorithms.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.205-216
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• 2022

One of the many tools used for distributed deep learning training is Kubeflow, which runs on Kubernetes, a container orchestration tool. TensorFlow jobs can be managed using the existing operator provided by Kubeflow. However, when considering the distributed deep learning training jobs based on the parameter server architecture, the scheduling policy used by the existing operator does not consider the task affinity of the distributed training job and does not provide the ability to dynamically allocate or release resources. This can lead to long job completion time and low resource utilization rate. Therefore, in this paper we proposes a new operator that efficiently schedules distributed deep learning training jobs to minimize the job completion time and increase resource utilization rate. We implemented the new operator by modifying the existing operator and conducted experiments to evaluate its performance. The experiment results showed that our scheduling policy improved the average job completion time reduction rate of up to 84% and average CPU utilization increase rate of up to 92%.

• Management Science and Financial Engineering
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• v.3 no.2
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• pp.29-43
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• 1997

Many heuristics have been developed in order to overcome the computational complexity of job shop problems. In this research, we develop a new heuristic by selecting four simple dispatching rules, i.e., SPT, LPT, SR and LR, dynamically as scheduling proceeds. The selection is accomplished by using artificial neural networks. As a result of testing on 50 problems, the makespan obtained by our heuristic is, on the average, 13.0% shorter than the longest makespan, and 0.4% shorter than the shortest makespan obtained by existing dispatching rules.

• Korean Management Science Review
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• v.17 no.2
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• pp.125-134
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• 2000

Keeping the promised delivery date for a customer order is crucial for a company to promote customer satisfaction and generate further businesses. For this, a company should be able to quote the delivery date that can be achieved with the capacity available on the shop floor. In a dynamic make-to-order manufacturing environment, the problem of determining a delivery date for an incoming order with consideration of resource capacity, workload, and finished-product inventory can hardly be solved by an analytical solution procedure. This paper considers a situation in which a delivery date for a customer order is determined based on a job schedule, and presents the SimTriD algorithm that provides the best scheduling for determining a delivery date of customer order through the job schedule that efficiently utilizes manufacturing resources with consideration of interacting factors such as resource utilization, finished-product inventory, and due date.

• International Journal of Precision Engineering and Manufacturing
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• v.1 no.2
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• pp.124-131
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• 2000

A shop floor can be considered as and importand level to develop a Computer Integrated Manufacturing system (CIMs). The shop foor is a dynamic environment where unexpected events contrinuously occur, and impose changes to planned activities. The shop floor should adopt an appropriate control system that is responsible for scheduling coordination and moving the manufacturing material and information flow. In this paper, the architecture of the hybrid control model identifies three levels; i.e., the shop floor controller (SFC), the cell controller(CC) and the equipment controller (EC). The methodology for developing these controller is employ an object-oriented approach for static models and IDEF0 for function models for dispatching a job. SFC and CC are coordinated by employing a multi-factors bidding and an adapted Analytic Hierarchy Process(AHP) prove applicability of the suggested method. Test experiment has been conducted by with the shopfloor, consisting of six manufacturing cells.

• The KIPS Transactions:PartA
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• v.14A no.6
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• pp.357-362
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• 2007

In distributed systems like cluster systems, in order to get more performance improvement, the initial task placement system precisely estimates and correctly assigns the resource requirement by the process. The resource-based initial job placement scheme needs the prediction of resource usage of a task in order to fit it to the most suitable hosts. However, the wrong prediction of resource usage causes serious performance degradation in dynamic load balancing systems. Therefore, in this paper, to resolve the problem due to the wrong prediction, we propose a new load metric. By the new load metric, the resource-based initial job placement scheme can work without priori knowledge about the type of process. Simulation results show that the dynamic load balancing system using the proposed approach achieves shorter execution times than the conventional approaches.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.493-496
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• 2007

그리드 컴퓨팅은 방대한 데이터 저장 공간과 고성능 연산능력을 요구하는 작업에 적합하다. 동적계획법(dynamic programming)은 방대한 크기의 동적 테이블(dynamic table)을 구성하여 최적해(optimal solution)을 찾기 때문에 그리드에서 수행하기에 적합한 작업이다. 본 논문에서는 동적 테이블을 구성하고 분산 배치하는 할당 정책을 제안한다. 그리고 동적계획법 기반의 어플리케이션을 그리드에서 효율적으로 수행할 수 있는 그리드 시스템 구조를 제안한다.