• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.31-47
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• 2007

The main purpose of this study is to find out the best solution of the vehicle routing problem with hard time window by using both genetic algorithm and heuristic. A mathematical programming model was also suggested in the study. The suggested mathematical programming model gives an optimal solution by using ILOG-CPLEX. This study also suggests a hybrid genetic algorithm which considers the improvement of generation for an initial solution by savings heuristic and two heuristic processes. Two heuristic processes consists of 2-opt and Or-opt. Hybrid genetic algorithm is also compared with existing problems suggested by Solomon. We found better solutions rather than the existing genetic algorithm.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.1
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• pp.31-40
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• 2020

In this study, we developed an algorithm for automatic travel itinerary planning based on expert recommendation. The proposed algorithm generates an itinerary by patterning a number of travel routes based on the automatic itinerary generation method based on the routes recommended by travel experts. To evaluate the proposed algorithm, we generated 30 itinerary for Singapore, Bankok, and Da Nang using both algorithms and analyzed the mean difference of trip distances with t-test and interater reliability of those itineraries. The result shows that the itineraries based on the proposed algorithm is not different from that of VRP(Vehicle routing problem) algorithm and interater reliability is high enough to show that the proposed algorithm is effective enough for real-world usage.

• 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.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.5
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• pp.609-617
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• 2013

This paper presents a near-minimum time path planning algorithm for autonomous driving. The problem of near-minimum time path planning is an optimization problem in which it is necessary to take into account not only the geometry of the circuit but also the dynamics of the vehicle. The path planning algorithm consists of a candidate path generation and a velocity optimization algorithm. The candidate path generation algorithm calculates the compromises between the shortest path and the path that allows the highest speeds to be achieved. The velocity optimization algorithm calculates the lap time of each candidate considering the vehicle driving performance and tire friction limit. By using the calculated path and velocity of each candidate, we calculate the lap times and search for a near-minimum time path. The proposed algorithm was evaluated via computer simulation using CarSim and Matlab/Simulink.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1164-1169
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• 2006

본 논문은 차량운행경로문제(Vehicle Routing Problem)의 기본적인 정보가 되는 Origin-Destination (OD) Matrix 의 생성에 관한 문제를 다룬다. OD Matrix 를 만들기 위해서는 몇 개의 지점으로부터 몇 개의 지점(some-to-some)까지의 최단 거리를 계산해야 한다. 이를 위해 Dijkstra 알고리듬과 같은 one-to-all 알고리듬을 반복하여 사용할 수 있고 Floyd-Washall 알고리듬과 같은 all-to-all 알고리듬을 사용할 수 있으며 some-to-some 을 위해 고안된 알고리듬을 사용할 수 있다. 본 연구에서는 이러한 알고리듬들이 실제 도로 네트워크의 OD Matrix 를 생성할 때 어떤 성능을 보이는지 비교 분석한다.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.1-14
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• 2009

The Capacitated Vehicle Routing Problem (CVRP) is the problem that the vehicles stationed at central depot are to be optimally routed to supply customers with demands, satisfying vehicle capacity constraints. The CVRP is the NP-hard as it is a natural generalization of the Traveling Salesman Problem (TSP). In this article, we propose the heuristic algorithm, called the bisection seed detection method, to solve the CVRP. The algorithm is composed of 3-phases. In the first phase, we work out the initial cluster using the improved sweep algorithm. In the next phase, we choose a seed node in each initial cluster by using the bisection seed detection method, and we compose the rout with the nearest node from each seed. At this phase, we compute the regret value to decide the list of priorities for the node assignment. In the final phase, we improve the route result by using the tabu search and exchange algorithm. We compared our heuristic with different heuristics such as the Clark-Wright heuristic and the genetic algorithm. The result of proposed heuristic show that our algorithm can get the nearest optimal value within the shortest execution time comparatively.

• Management & Information Systems Review
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• v.29 no.4
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• pp.109-121
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• 2010

In this study, we propose a solution procedure to solve travelling salesman problem(TSP) with special cost function, route dependent travelling salesman problem(RDTSP). First, we develop an integer programming model to describe the problem. In the model, a variable means a possible route. And, the number of variables in this model are extremely large. So, we develop a LP relaxation problem of the IP model and solve the relaxation problem by a column generation technique. The relaxation problem does not guarantee the optimal solution. If we get an integer solution in the ralaxation problem, then the solution is an optimal one. But, if not, we cannot get an optimal solution. So, we approach a branch and price technique. The overall solution procedure can be applied a printed circuit board(PCB) assembly process.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.5
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• pp.168-185
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• 2024

Autonomous driving technologies have entered the initial stage of commercialization. Recently, mobility services that combine autonomous driving technologies and information and communication technologies based intelligent transportation systems are being actively developed. This study develops a global path planning system that considers traffic information and user demands to generate the shortest time paths for autonomous delivery services using Mixed Integer Programming. While providing the autonomous delivery services, the generated paths are updated recursively according to traffic information updates or additional demands. The developed global path planning system was verified by simulations with traffic information in the Sangam autonomous driving test-bed, and comparative analysis with existing manned delivery services shows that operating costs, product delivery time, and empty driving time were reduced.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.137-150
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• 2009

This paper proposes the hybrid heuristic method to apply the opportunity time to solve the vehicle routing and scheduling problem with time constraints(VRSPTW). The opportunity time indicates the idle time which remains after the vehicle performs the unloading service required by each customer's node. In this proposed heuristic, we add the constraints to VRSPTW model for the opportunity time. We also obtain the initial solution by applying the cost evaluation function to the insertion strategy considering the opportunity time. In addition, we improve the former result by applying the opportunity time to the tabu search strategy by swapping the customer's node. Finally, we suggest the construction strategies of initial routing which can efficiently acquire the nearest optimal solution from various types of data in terms of geographical condition, scheduling horizon and vehicle capacity. Our experiment show that our heuristic can get the nearest optimal solution more efficiently than the Solomon's I1 heuristic.