• Journal of Korean Institute of Industrial Engineers
• /
• v.23 no.1
• /
• pp.129-146
• /
• 1997

In traditional approaches to scheduling problems, a single dispatching rule was used by all machines in a system. However, since the situation of each machine generally differs from those of other machines, it is reasonable to apply a different dispatching rule to each machine responding to its given situation. In this regard, we introduce the concept of spatial adaptation and examine its effectiveness by simulation. In the spatial adaptation, each machine in a system selects an appropriate dispatching rule in order to improve productivity while it strives to be in harmony with other machines. This study proposes an adaptive procedure which produces a reliable dispatching rule for each machine beginning with the bottleneck machine. The dispatching rule is composed of several criteria of which priorities are adaptively weighted. The weights are learned for each machine through systematic simulations. The simulations are conducted according to a Taguchi experimental design in order to find appropriate sets of criteria weights in an efficient and robust way in the context of environmental variations. The proposed method was evaluated in an application to a semiconductor wafer fabrication system. The method achieved reliable performance compared to traditional dispatching rules, and the performance quickly approached the peak after learning for only a few bottleneck machines.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.42 no.5
• /
• pp.361-367
• /
• 2014

In this paper, task scheduling and load balancing methods of multifrontal solution methods of finite element structural analysis in a modern multicore machine are introduced. Many structural analysis problems have generally irregular grid and many kinds of properties and materials. These irregularities and heterogeneities lead to bottleneck of parallelization and cause idle time to analysis. Therefore, task scheduling and load balancing are desired to reduce inefficiency. Several kinds of multithreaded parallelization methods are presented and comparison between static and dynamic task scheduling are shown. To reduce the idle time caused by irregular partitioned subdomains, computational load balancing methods, Balancing all tasks and minmax task pairing balancing, are invented. Theoretical and actual elapsed time are shown and the reason of their performance gap are discussed.

• Journal of Korea Game Society
• /
• v.16 no.4
• /
• pp.57-68
• /
• 2016

As the size of the data used in the game application increase gradually, the physical resources of game server grow. Accordingly, it is necessary to/it is required to introduce I/O virtualization in game server to improve I/O performance of the server. But it is difficult to ensure high responsiveness in game server where I/O delay change from time to time. To maximize the benefit of I/O virtualization, guaranteeing I/O response time is very important and it is necessary to have I/O scheduling techniques to manage the I/O latency in the order of priority of virtual machines. In this paper, we propose an efficient delay-constrained scheduling algorithm in a virtualization environment to ensure maximum I/O latency. In addition, a reduced amount of loss of the packet was found to increase the fairness of scheduling in the experiments with the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.3
• /
• pp.1164-1183
• /
• 2019

The rapid development of cloud computing and high requirements of operators requires strong support from the underlying Data Center Networks. Therefore, the effectiveness of using resources in the data center networks becomes a point of concern for operators and material for research. In this paper, we discuss the online virtual-cluster provision problem for multiple tenants with an aim to decide when and where the virtual cluster should be placed in a data center network. Our objective is maximizing the total revenue for the data center networks under the constraints. In order to solve this problem, this paper divides it into two parts: online multi-tenancy scheduling and virtual cluster placement. The first part aims to determine the scheduling orders for the multiple tenants, and the second part aims to determine the locations of virtual machines. We first approach the problem by using the variational inequality model and discuss the existence of the optimal solution. After that, we prove that provisioning virtual clusters for a multi-tenant data center network that maximizes revenue is NP-hard. Due to the complexity of this problem, an efficient heuristic algorithm OMS (Online Multi-tenancy Scheduling) is proposed to solve the online multi-tenancy scheduling problem. We further explore the virtual cluster placement problem based on the OMS and propose a novel algorithm during the virtual machine placement. We evaluate our algorithms through a series of simulations, and the simulations results demonstrate that OMS can significantly increase the efficiency and total revenue for the data centers.

• CDE review
• /
• v.11 no.2
• /
• pp.15-22
• /
• 2005

Presented in the paper is an approach to developing an intelligent mold shop as a means to overcome the difficulties faced by mold-makers due to skill shortages and increased global competition. A machine shop where as much as of the human skills are replaced by a set of intelligent systems is called an intelligent machine shop, and an intelligent mold-making machine shop is called an intelligent mold shop(MS). By analyzing the contents of operator's skill. three intelligent S/W stations have been designed: Technical Data Processing(TDP) Station, Loading Schedule Station, and Real-time Monitoring Station. A detailed architecture of the TDP station is described, and measures of effectiveness of IMS are elaborated.

• Industrial Engineering and Management Systems
• /
• v.3 no.2
• /
• pp.100-115
• /
• 2004

The network representation and branch and bound algorithm with efficient lower and upper bounding procedures are developed to determine a global optimal production schedule on a machine that minimizes sequence-dependent setup cost and earliness/tardiness penalties. Lower bounds are obtained based on heuristic and Lagrangian relaxation. Priority dispatching rule with local improvement procedure is used to derive an initial upper bound. Two dominance criteria are incorporated in a branch and bound procedure to reduce the search space and enhance computational efficiency. The computational results indicate that the proposed procedure could optimally solve the problem with up to 40 jobs in a reasonable time using a personal computer.

• Journal of Korean Institute of Industrial Engineers
• /
• v.34 no.3
• /
• pp.296-307
• /
• 2008

A semiconductor production system has sophisticated manufacturing operations and needs high capital investment for its expensive equipment, which warrants efficient real-time flow control for wafers. In the bay, we consider material handling equipment that can handle multiple carriers of wafers. The dispatching logic first determines the transportation time of each carrier to its destination by each unit of transportation equipment and uses this information to determine the destination machine and target carrier. When there is no available buffer space at the machine tool, the logic allows carriers to stay at the buffer of a machine tool and determine the delay time, which is used to determine the destination of carriers in URL. A simulation study shows this dispatching logic performs better than the procedure currently in use to reduce the mean flow time and average WIP of wafers and increase efficiency of material handling equipment.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.54
• /
• pp.13-26
• /
• 2000

Machining route selection to produce parts should be based on shop flow information because of input data at scheduling tasks and is one of the main problem in process planning. This paper addresses the problem of machining route selection in multi-stage process with machine group included a similar function. The model proposed is formulated as 0-1 integer programing considering the relation of parts and machine table size, avaliable time of each machine for planning period, and delivery date. The objective of the model is to minimize the sum of processing, transportation, and setup time for all parts. Genetic algorithm approach is developed to solve this model. The efficiency of the approach is examined in comparison with the method of branch and bound technique for the same problem. Also, this paper is to solve large problem scale and provide it if the multiple machining routes are existed an optimal solution.

• KIPS Transactions on Computer and Communication Systems
• /
• v.3 no.5
• /
• pp.151-154
• /
• 2014

An energy of applications had consumed through the complexed inter-action with operating systems, run-time environments, compiler, and applications on various mobile devices. In these days, challenged researches are studying to reduce of energy consumptions that uses energy-oriented high-level and low-level compiler techniques on mobile devices. In this paper, we intented to reduce an energy consumption of Java mobile applications that applied a list instruction scheduling for energy dissipation from dalvik bytecode which extracted Android dex files. Through this works, we can construct the optimized power and energy environment on mobile devices with the limited power supply.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.11a
• /
• pp.506-512
• /
• 2006

In this paper, we propose a lot-sizing and scheduling problem that seeks to minimize the sum of production cost and inventory cost over a given planning horizon while considering idle state of a machine in a batch production system. For this problem, we develop an integer programming model. And, we develop an efficient 2-phase heuristic algorithm to find a high quality feasible solution within reasonable time bounds. In the first phase, we seek to minimize the production cost by assigning batches to machines. Then, in the second phase, we find a production sequence of batches that reduces the inventory cost, while considering adding or deleting idle states between batches. Computational results show that the developed heuristic algorithm finds excellent feasible solutions within reasonable time bounds. Also, we could significantly reduce the total cost consisting of production cost and inventory cost by using the developed heuristic algorithm at a real manufacturing system that produces zinc alloys.