• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.57-78
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• 2005

The vehicle routing problem (VRP) is to determine a set of feasible vehicle routes, one for each vehicle, such that each customer is visited exactly once and the total distance travelled by the vehicles is minimized. A feasible route is defined as a simple circuit including the depot such that the total demand of the customers in the route does not exceed the vehicle capacity. While there have been significant advances recently in exact solution methodology, the VRP is not a well solved problem. We find most approaches still relying on the branch and bound method. These approaches employ various methodologies to compute a lower bound on the optimal value. We introduce a new modelling approach, termed route-splitting, for the VRP that allows us to address problems whose size is beyond the current computational range of set-partitioning models. The route-splitting model splits each vehicle route into segments, and results in more tractable subproblems. Lifting much of the burden of solving combinatorially hard subproblems, the route-splitting approach puts more weight on the LP master problem, Recent breakthroughs in solving LP problems (Nemhauser, 1994) bode well for our approach. Lower bounds are computed on five symmetric VRPs with up to 199 customers, and eight asymmetric VRPs with up to 70 customers. while it is said that the exact methods developed for asymmetric instances have in general a poor performance when applied to symmetric ones (Toth and Vigo, 2002), the route splitting approach shows a competent performance of 93.5% on average in the symmetric VRPs. For the asymmetric ones, the approach comes up with lower bounds of 97.6% on average. The route-splitting model can deal with asymmetric cost matrices and non-identical vehicles. Given the ability of the route-splitting model to address a wider range of applications and its good performance on asymmetric instances, we find the model promising and valuable for further research.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.9
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• pp.830-835
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• 2016

With the relaxation of regulations on unmanned aerial vehicles (UAVs) in the USA, the development of related industries is expected. Hence, it is anticipated that the number the UAVs will reach approximately 600,000 in the USA in 2017. However, automated flights of commercial UAVs are restricted owing to concerns about accidents. To deal with the possibility of collisions, several studies on collision prevention and the routing of UAVs have been conducted. However, these studies do not deal with various situations dynamically or provide efficient solutions. Therefore, we propose a centralized routing method for the UAV that uses vehicular networks. In the proposed scheme, vehicular networks regard UAVs as data packets to be routed. Accordingly, the proposed method reduces UAV processing power required for route searches. In addition, the routing efficiency for UAV flight paths can be improved since congestion can be minimized by using a vehicular network.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.389-398
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• 2010

The Vehicle Routing and Scheduling Problem with Time Windows(VRSPTW) is to establish a delivery route of minimum cost satisfying the time constraints and capacity demands of many customers. The VRSPTW takes a long time to generate a solution because this is a NP-hard problem. To generate the nearest optimal solution within a reasonable time, we propose the heuristic by using an ACO(Ant Colony Optimization) with multi-cost functions. The multi-cost functions can generate a feasible initial-route by applying various weight values, such as distance, demand, angle and time window, to the cost factors when each ant evaluates the cost to move to the next customer node. Our experimental results show that our heuristic can generate the nearest optimal solution more efficiently than Solomon I1 heuristic or Hybrid heuristic applied by the opportunity time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.1
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• pp.221-227
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• 2008

A vehicle routing problem with time constraint is one of the important problems in distribution and transportation. The service of a customer must start and finish within a given time interval. Our method is based on an improved operators of genetic algorithm and the objective is to minimize the cost of servicing the set of customers without being tardy or exceeding the capacity or travel time of the vehicles. This research shows that a proposed method based on the improved genetic search can obtain good solutions to vehicle routing problems with time constrained compared with a high degree of efficiency other heuristics. For the computational purpose, we developed a GUI-type computer program according to the proposed method and the computational results show that the proposed method is very effective on a set of standard test problems, and can be potentially useful in solving the vehicle routing problems.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.4
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• pp.53-62
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• 2010

In VANETs (Vehicular Ad hoc NETwork), conditional anonymity must be provided to protect privacy of vehicles while enabling authorities to identify misbehaving vehicles. To this end, previous systems provide a mechanism to revoke the anonymity of individual messages. In VANET, if we can trace the movement path of vehicles, it can be useful in determining the liability of vehicles in car accidents and crime investigations. Although route tracing can be provided using previous message revocation techniques, they violate privacy of other vehicles. In this paper, we provide a route tracing technique that protects privacy of vehicles that are not targeted. The proposed method can be employed independently of the authentication mechanism used and includes a mechanism to prevent authorities from abusing this new function.

• Tunnel and Underground Space
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• v.24 no.1
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• pp.11-20
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• 2014

This study analyzed the optimal routes of auxiliary vehicles in an open-pit mine that need to traverse the entire mine through many working points. Unlike previous studies which usually used the Dijkstra's algorithm, this study utilized a heuristic algorithm for the Traveling Salesman Problem(TSP). Thus, the optimal routes of auxiliary vehicles could be determined by considering the visiting order of multiple working points. A case study at the Pasir open-pit coal mine, Indonesia was conducted to analyze the travel route of an auxiliary vehicle that monitors the working condition by traversing the entire mine without stopping. As a result, we could know that the heuristic TSP algorithm is more efficient than intuitive judgment in determining the optimal travel route; 20 minutes can be shortened when the auxiliary vehicle traverses the entire mine through 25 working points according to the route determined by the heuristic TSP algorithm. It is expected that the results of this study can be utilized as a basis to set the direction of future research for the system optimization of auxiliary vehicles in open-pit mines.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1036-1041
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• 2006

본 연구에서는 허브와 스포크의 입지선정과 차량 경로가 통합된 hub-and-spoke 네트워크 설계문제를 다룬다. Hub-and-spoke 네트워크는 대량화와 공동화를 통해 물류효율화를 실현하기 위한 대표적인 구조로 물류시스템에서 흔히 사용되고 있다. 이러한 물류시스템에서 물류비용의 절감과 고객서비스 향상을 위한 효율적인 수송네트워크 설계는 매우 중요하다. 전통적인 hub-and-spoke 네트워크 설계문제에서 각 스포크의 위치와 화물량이 미리 주어진 상황에서 허브의 입지를 결정하였다. 하지만 스포크 역시 스포크가 담당하는 고객들의 위치와 담당 영역에 따라 그 위치와 수, 그리고 화물량이 변할 수 있다. 또한 정확한 비용산출을 위해서는 스포크에서 고객으로의 수집과 배달을 위한 차량경로가 함께 고려되어야 한다. 다루는 수송망 설계문제는 상호 관련성 있는 여러 부분문제가 결합된 통합문제로써 이를 해결하는 방법으로 기존의 발견적 방법에 의한 순차적 기법은 한계가 있다. 본 연구에서는 공생 진화알고리듬 기반의 방법론을 채용하여 다루는 수송망 설계문제를 동시에 통합적으로 해결할 수 있는 알고리듬을 개발한다. 실험을 통해 개발한 알고리듬의 우수성과 그 적용성을 보인다.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1731-1737
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• 2007

This paper considers subway routing problem. Given a schedule of train to be routed by a railway stock, the routing problem determines a sequence of trains while satisfying turnaround time and maintenance restrictions. Generally, the solution of routing problem is generated from set partition formulation solved by column generation method, a typical integer programming approach for train-set. However, we find the characteristics of metropolitan subway which has a simple rail network, a few end stations and 13 departure-arrival patterns. We reflect a turn-around constraint due to spatial limitations has no existence in conventional railroad. Our objective is to minimize the number of daily train-sets. In this paper, we develop two basic techniques that solve the subway routing problem in a reasonable time. In first stage, we formulate the routing problem as a Min-cost-flow problem. Then, in the second stage, we attempt to normalize the distance covered to each routes and reduce the travel distance using our heuristic approach. Applied to the current daily timetable, we could find the subway routings, which is an approximately 14% improvement on the number of train-sets reducing 15% of maximum traveling distance and 8% of the standard deviation.

• Journal of the Korea Society for Simulation
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• v.22 no.1
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• pp.63-75
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• 2013

The School Bus Routing Problem(SBRP) seeks to plan an efficient schedule of a fleet of school buses that must pick up student from various bus stops and deliver them by satisfying various constraints; maximum capacity of the bus, maximum riding time of students, arrival time between a school's time window. By extending the existing SBRP, we consider a case study of SBRP with allowance of mixed-loading and dynamic arrivals reflecting the school bus operation of university in Korea. Our solution procedure is based on constructing the initial solution using sweep algorithm and then improving solution within the framework of the evolutionary approach known as efficient meta-heuristics. By comparing the various scenarios through the constraints relaxation for reflecting the real operational strategies, we assess the merit of our proposed procedure.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.279-282
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• 2000

물류시스템설계 및 개선의 일환으로 차량운송계획 문제는 매우 중요시되는 분야이다. 본 연구는 유전자 알고리즘을 이용한 통합물류 운송계획문제(Integrated Vehicle Routing Problem : IVRP)를 위한 전산화 모델의 개발로서 운송 중인 차량의 자동위치 조회시스템(Automatic Vehicle Location System : AVL)을 이용하여 차량운송의 통제기능을 높이며, 차량운송 효율을 높이는 목적으로 VRP문제와 AVL문제를 연계하는 통합운송시스템의 개념 연구이다. 본 연구에서는 다-물류센터를 고려한 운송 영역할당(Sector Clustering), 경로계획 문제(VRP) 및 유전자 알고리즘을 이용한 운송순서 계획(GA-TSP)을 고려한 통합운송계획모델의 개발을 목적으로 차량의 운송간의 통신 및 통제 등의 기능을 위하여 위치 추적을 가진 GPS와 통신시스템을 위한 TRS 및 위치설정을 위한 GIS를 고려하였다. 본 연구에서의 AVL시스템 개발을 위한 GPS, GIS 및TRS 등 AVL시스템의 구성요소에 대하여 그 개요를 조사 연구하였다. 또한 GPS, GIS을 기반으로한 물류시스템의 구성과 시스템에 필요한 기능 및 구성요소를 파악하고 서브 시스템을 구성하여 각 서브 시스템의 기본설계를 실시하기 위한 연구이다.