• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.3-3
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• 2018

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.99-104
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• 2002

This paper addresses a problem of machining route selection and determination of input quantity with yield in multi-stage flexible flow system. The problem is formulated as nonlinear programing and the proposed model is solved by genetic algorithm(GA) approach. The effectiveness of the proposed GA approach is evaluated through comparisons with the optimal solution obtained from the branch and bound for the same problem.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.255-255
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• 2000

Technical advance of electrical chart and cruising equipment make it possible to sail without a man. It is important to decide the cruising route in view of effectiveness and stability of a ship. So we need to study on the optimal trajectory planning. Genetic algorithm is a strong optimization algorithm with adaptational random search. It is a good choice to apply genetic algorithm to the trajectory planning of a ship. We modify a genetic algorithm to solve this problem. The effectiveness of the revised genetic algorithm is assured through computer simulations.

• CDE review
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• v.21 no.2
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• pp.34-38
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• 2015

In case of the big city, choosing the adequate root of which we can reach the destination can affect the driver's condition and driving time. so it is quite important to find the optimal routes for arriving the destination as considering the factors, such as driving conditions or travel time and so on. In this paper, we develop route choice model with considering driving conditions and travel time, and it can search the optimal path which make drivers reduce their fatigues using genetic algorithm.

• Journal of the military operations research society of Korea
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• v.8 no.2
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• pp.17-30
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• 1982

This paper offers an optimal artillery deployment scheme for the defending unit when two forces are confronted at a military front line. When proposed gun sites, types and number of guns as well as targets are given, the solutions of the two models in this paper direct each (unit of) guns to a certain location. The aim of the models is to maximize the number of guns which can hit important targets. Unlike widely used target assignment models, these models are formulated using the set covering problem concept. These models do not contain probabilities and time. Thus they are simple as models, easy in implementation, and yield tractable solutions. The dynamic and probabilistic feature of battle situations is implicitly reflected on the models. The first model is for the case that enemies' approaching route is clearly predictable, while the second model is for the unpredictable approaching route case.

• Korean Management Science Review
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• v.17 no.2
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• pp.55-65
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• 2000

This paper addresses a problem of machining route selection in multi-stage process with machine group. This problem is considered the subcontracting and the production in-house such as regular and overtime work. the proposed model is formulated as a 0-1 integer programming constraining the avaliable time of each machine for planning period and total overtimes. The objective of the model is to minimize the sum of processing cost, overtime cost, and subcontracting cost. To solve this model, a genetic algorithm(GA) approach is developed. The effectiveness of the proposed GA approach is evaluated through comparisons with the optimal solution obtained from the branch and bound. In results, the same optimal solution is obtained from two methods at small size problem, and the consistent solution is provided by the GA approach at large size problem. The advantage of the GA approach is the flexibility into decision-making process because of providing multiple machining routes.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.63-71
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• 2012

Recently, because most of buildings in the city become bigger and higher, so various convenient facilities and high-tech management skills are required to prevent potential disasters. Especially, because fires in huge buildings can cause serious damage, technologies and methods that help people quickly and safely to escape from the building is very important to extinguish the fire in initial state and to minimize the damage. This paper suggests a context-aware service system that can provide optimal escape routes to tenants with their smart-phones. The proposed system collects contexts with real time through USN(Ubiquitous Sensor Network) and provides an optimum escape route based on the collected contexts through service users' smart phones. In addition, The system provides not only safe routes from the fire but also valuable information for rescue operations with real time.

• Journal of the military operations research society of Korea
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• v.30 no.2
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• pp.81-95
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• 2004

This paper presents optimal solutions for the following two problems ; (1) The vehicle routing problem with deadline considering homeward-route transport requirement, (2) The problem of (1) with relaxed constraint that the vehicle cannot visit the depot during the trip. That is, for the second problem, vehicle can visit the depot several times during the trip. The formulation and optimal solution we obtained outperform the existing ones proposed by other researchers in the sense of time and total distance traveled and the results can be applicable to short-haul local transport.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.97-105
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• 2022

Human casualties from fires are increasing worldwide. The majority of human deaths occur during the evacuation process, as occupants panic and are unaware of the location of the fire and evacuation routes. Using an Internet of Things (IoT) sensor and reinforcement learning, we propose a method to find the safest evacuation route by considering the fire location, flame speed, occupant position, and walking conditions. The first step is detecting the fire with IoT-based devices. The second step is identifying the occupant's position via a beacon connected to the occupant's mobile phone. In the third step, the collected information, flame speed, and walking conditions are input into the reinforcement learning model to derive the optimal evacuation route. This study makes it possible to provide the safest evacuation route for individual occupants in real time. This study is expected to reduce human casualties caused by fires.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2001.10a
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• pp.97-102
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• 2001