• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.2
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• pp.39-51
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• 2018

Long-term unexecuted urban parks will be released from urban planning facilities after 2020, this may result in development of those parks. However, little research have been focused on how to develop those parks considering conservation, development, spatial pattern, and so on. Therefore, in this study, we suggested an optimization planning model that minimizes the fragmentation while maximizing the conservation and development profit using ACO (Ant Colony Optimization). Our study area is Suwon Yeongheung Park, which is long-term unexecuted urban parks and have actual plan for private development in 2019. Using our optimization planning model, we obtained four alternatives(A, B, C, D), all of which showed continuous land use patterns and satisfied the objectives related to conservation and development. Each alternative are optimized based on different weight combinations of conservation, development, and fragmentation, and we can also generated other alternatives immediately by adjusting the weights. This is possible because the planning process in our model is very fast and quantitative. Therefore, we expected our optimization planning model can support "spatial decision making" of various issue and sites.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.13-21
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• 2017

Airline schedules are highly dependent on various factors of uncertainties such as unfavorable weather conditions, mechanical problems, natural disaster, airport congestion, and strikes. If the schedules are not properly managed to cope with such disturbances, the operational cost and performance are severely affected by the delays, cancelations, and so forth. This is described as a disruption. When the disruption occurs, the airline requires the feasible recovery plan returning to the normal operations in a timely manner so as to minimize the cost and impact of disruptions. In this research, an Ant Colony Optimization (ACO) algorithm with re-timing strategy is developed to solve the recovery problem for both aircraft and passenger. The problem consists of creating new aircraft routes and passenger itineraries to produce a feasible schedule during a recovery period. The suggested algorithm is based on an existing ACO algorithm that aims to reflect all the downstream effects by considering the passenger recovery cost as a part of the objective function value. This algorithm is complemented by re-timing strategy to effectively manage the disrupted passengers by allowing delays even on some of undisrupted flights. The delays no more than 15 minutes are accepted, which does not influence on the on-time performance of the airlines. The suggested method is tested on the real data sets from 2009 ROADEF Challenge, and the computational results are compared with the existing ones on the same data sets. The method generates the solution for most of problem set in 10 minutes, and the result generated by re-timing strategy is discussed for its impact.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.3
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• pp.147-160
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• 2010

The system virtualization shows interest in the consolidation of servers for the efficient utilization of system resources. There are many various researches to utilize a server machine more efficiently through the system virtualization technique, and improve performance of the virtualization software. These researches have studied with the activity to control the resource allocation of virtual machines dynamically focused on CPU, or to manage resources in the cross-machine using the migration. However, the researches of the memory management have been wholly lacking. In this respect, the use of memory is limited to allocate the memory statically to virtual machine in server consolidation. Unfortunately, the static allocation of the memory causes a great quantity of the idle memory and decreases the memory utilization. The underutilization of the memory makes other side effects such as the load of other system resources or the performance degradation of services in virtual machines. In this paper, we suggest the dynamic allocation of the memory in Xen to control the memory allocation of virtual machines for the utilization without the performance degradation. Using AR model for the prediction of the memory usage and ACO (Ant Colony Optimization) algorithm for optimizing the memory utilization, the system operates more virtual machines without the performance degradation of servers. Accordingly, we have obtained 1.4 times better utilization than the static allocation.