• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_2
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• pp.1071-1081
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• 2022

As the demand for high-rise buildings increases, the demand for high-speed elevators is also increasing. In order to make a high-speed elevator, a method is needed to reduce the weight of the elevator's components, which is a constraint on the increase in speed. As a measure to reduce the weight, it is possible to remove the traveling cable for power and signal supply. Since the weight of the traveling cable varies depending on the position of the carriage, it is difficult to compensate the weight using the counter weight. The power supply is a structure in which a brush-rail type power input terminal is installed in the elevator hoistway to receive power in a contact-type manner while the carriage is moving. If a small-capacity ESS is installed in a passenger car, power can be supplied uninterruptedly inside the passenger car. A small-capacity ESS charging system to be applied to such an elevator system is required to perform several functions. First, the passenger Car must be able to charge as much as possible even during high-speed operation. A control algorithm with high responsiveness is required because charging starts and ends repeatedly by the partially installed input power stage. In addition, if the input-side line impedance is large due to the structure of the system and the response characteristic is increased, the stability of the system may be lowered. Accordingly, in this paper, we propose a control algorithm that has a stable steady-state output while having a fast response in a transient state. To verify the proposed control algorithm, simulation was conducted using PSIM, and the performance of the controller was verified by manufacturing a prototype buck conveter charger.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.447-456
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• 2007

The ant colony optimization (ACO) is a probabilistic Meta-heuristic algorithm which has been developed in recent years. Originally ACO was used for solving the well-known Traveling Salesperson Problem. More recently, ACO has been used to solve many difficult problems. In this paper, we develop an ant colony optimization method to solve the maximum independent set problem, which is known to be NP-hard. In this paper, we suggest a new method for local information of ACO. Parameters of the ACO algorithm are tuned by evolutionary operations which have been used in forecasting and time series analysis. To show the performance of the ACO algorithm, the set of instances from discrete mathematics and computer science (DIMACS)benchmark graphs are tested, and computational results are compared with a previously developed ACO algorithm and other heuristic algorithms.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1740-1745
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• 2006

This paper is concerned with a novel heuristic algorithm for the multi-trip vehicle routing problem with time windows. The objective function is the minimization of total vehicle operating time, fixed cost of vehicle and the minimization of total lateness of customer. A mixed integer programming formulation and a heuristic algorithm for a practical use are suggested. A heuristic algorithm is constructed two phases such as clustering and routing. Clustering is progressed in order to assign appropriate vehicle to customer, and then vehicle trip and route are decided considering traveling distance and time window. It is shown that the suggested heuristic algorithm gives good solutions within a short computation time by experimental result.

• Journal of the Korea Society for Simulation
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• v.9 no.1
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• pp.21-38
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• 2000

A fuzzy dispatching algorithm with adaptable control scheme is proposed for more flexible and adaptable operation of AGV system. The basic idea of the algorithm is prioritization of all move requests based on the fuzzy urgency. The fuzzy urgency is measured by the fuzzy multi-criteria decision-making method, utilizing the relevant information such as incoming and outgoing buffer status, elapsed time of move request, and AGV traveling distance. At every dispatching decision point, the algorithm prioritizes all move requests based on the fuzzy urgency. The performance of the proposed algorithm is compared with several dispatching algorithms in terms of system throughput in a hypothetical job shop environment. Simulation experiments are carried out varying the level of criticality ratio of AGVs , the numbers of AGVs, and the buffer capacities. The rule presented in this study appears to be more effective for dispatching AGVs than the other rules.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.85-90
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• 2023

The Ant Colony System (ACS) is a variant of Ant colony optimization algorithm which is well-known in Traveling Salesman Problem. This paper proposed a hybrid method based on genetic algorithm (GA) and ant colony system (ACS), called GACS, to solve traffic routing problem. In the GACS, we use genetic algorithm to optimize the ACS parameters that aims to attain the shortest trips and time through new functions to help the ants to update global and local pheromones. Our experiments are performed by the GACS framework which is developed from VANETsim with the ability of real map loading from open street map project, and updating traffic light in real-time. The obtained results show that our framework acquired higher performance than A-Star and classical ACS algorithms in terms of length of the best global tour and the time for trip.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.8
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• pp.122-129
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• 1997

In this paper, we propose a Hopfield model for solving the part-matching in case that is the number of parts and positions are changed. The goal of this paper is to minimize part-connection in pairs and total path of part-connections. Therefore, this kind of problem is referred to as a combinatiorial optimization problem. First of all, we review the theoretical basis for Hopfield model and present two optimal algorithms of part-matching. The first algorithm is Traveling Salesman Problem(TSP) which improved the original and the second algorithm is Wdighted Matching Problem (WMP). Finally, we show demonstration through com- puter simulation and analyze the stability and feasibility of the generated solutions for the proposed con- nection methods. Therefore, we prove that the second algorithm is better than the first algorithm.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.157-164
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• 2011

This paper presents a fast and scalable re-routing algorithm that adapts to dynamically changing networks. The proposed algorithm integrates Dijkstra's shortest path algorithm with the genetic algorithm. Dijkstra's algorithm is used to define the predecessor array that facilitates the initialization process of the genetic algorithm. After that, the genetic algorithm re-searches the optimal path through appropriate genetic operators under dynamic traffic situations. Experimental results demonstrate that the proposed algorithm produces routes with less traveling time and computational overhead than pure genetic algorithm-based approaches as well as the standard Dijkstra's algorithm for large-scale networks.

• Korean Management Science Review
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• v.13 no.3
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• pp.187-198
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• 1996

The traveling salesman problem has been studied for many years since the model can be used for various applications such as vehicle routing, job sequencing, clustering a data array, and so on. In this paper one of the typical exact algorithms for TSP, Minty's, will be modified to improve the performance of the algorithm on the applications without losing simplicity. The Little's algorithm gives good results, however, the simple and plain Minty's algorithm for solving shortest-route problems has the most intuitive appeal. The suggested Minty's modification is based on the creation of penalty-values on the matrix of a TSP. Computer experiments are made to verify the effectiveness of the modification.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.3
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• pp.47-61
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• 2007

This paper presents two elimination methods of subtours, which is obtained by applying the Out-of-Kilter algorithm to the sequential ordering problem (SOP) to produce a feasible solution for the SOP. Since the SOP is a kind of asymmetric traveling salesman problem (ATSP) with precedence constraints, we can apply the Out-of-Kilter algorithm to the SOP by relaxing the precedence constraints. Instead of patching subtours, both of two elimination methods construct a feasible solution of the SOP by using arcs constructing the subtours, and they improve solution by running 3-opt and 4-opt at each iteration. We also use a perturbation method. cost relaxation to explore a global solution. Six cases from two elimination methods are presented and their experimental results are compared to each other. The proposed algorithm found 32 best known solutions out of the 34 instances from the TSPLIB in a reasonable time.

• Korean Management Science Review
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• v.30 no.2
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• pp.31-42
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• 2013

The vehicle routing problem (VRP) can be described as a problem to find the optimum traveling routes from one or several depot (s) to number of geographically scattered customers. This study executes a revised Heterogeneous Vehicle Routing Problem (HVRP) to minimize the cost that needs to conduct efficiently the snow removal operations of Air Wing under available resources and limited operations time. For this HVRP, we model the algorithm of an hybrid Ant Colony System (ACS). In the initial step for finding a solution, the modeled algorithm applies various alterations of a parameter that presents an amount of pheromone coming out from ants. This improvement of the initial solution illustrates to affect to derive better result ultimately. The purpose of this study proves that the algorithm using Hybrid heuristic incorporated in tabu and ACS develops the early studies to search best solution.