• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.33
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• pp.111-118
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• 1995

This paper suggests a hybrid genetic algorithm for asymmetric traveling salesman problem(TSP). The TSP was proved to be NP-complete, so it is difficult to find optimal solution in reasonable time. Therefore it is important to develope an algorithm satisfying robustness. The algorithm applies dynamic programming to find initial solution. The genetic operator is uniform order crossover and scramble sublist mutation. And experiment of parameterization has been performed.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.107-116
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• 2011

In this paper, we implement an algorithm of transit signal priority control that not only maximizes service quality and efficiency of bus, but also minimizes the control delay of passenger cars using UTIS currently being deployed and operated in Seoul national capital area. For this purpose, we propose an algorithm that coordinates the strength of TSP by estimating bus demand. Typically, the higher the strength of TSP is on main street, the bigger the control delay is on the cross street. Motivated by this practical difficulty, we proposes an algorithm that coordinates TSP's strength by checking the degree of saturation of cross street. Also, we verify the possibility of field implementation via simulation analysis using CORSIM RTE based HILS (Hardware In the Loop Simulation). The result shows that travel time of bus improves about 10 percent without increasing control delay of passenger cars by TSP. We expect the result of this research to contribute to increasing the overall transit ridership in this country.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.119-126
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• 2019

The partial inverse optimization problem is an interesting variant of the inverse optimization problem in which the given instance of an optimization problem need to be modified so that a prescribed partial solution can constitute a part of an optimal solution in the modified instance. In this paper, we consider the traveling salesman problem defined on the line (TSP on the line) which has many applications such as item delivery systems, the collection of objects from storage shelves, and so on. It is worth studying the partial inverse TSP on the line, defined as follows. We are given n requests on the line, and a sequence of k requests that need to be served consecutively. Each request has a specific position on the real line and should be served by the server traveling on the line. The task is to modify as little as possible the position vector associated with n requests so that the prescribed sequence can constitute a part of the optimal solution (minimum Hamiltonian cycle) of TSP on the line. In this paper, we show that the partial inverse TSP on the line and its variant can be solved in polynomial time when the sever is equiped with a specific internal algorithm Forward Trip or with a general optimal algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.601-605
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• 1991

In this paper an approximate solution method for the large-scale Traveling Salesman Problem(TSP) is presented. The method start with the subdivision of the problem domain into a number of clusters by considering their geometries. The clusters have limited number of nodes so as to get local solutions. They are linked to give the least path which covers the whole domain and become TSPs with start- and end-node. The approximate local solutions in each cluster are obtained by using geometrical property of the cluster, and combined to give an overall-approximate solution for the large-scale TSP.

• 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.

• The KIPS Transactions:PartA
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• v.10A no.4
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• pp.369-374
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• 2003

Ant Algorithm is used to find the solution of Combinatorial Optimization Problems. Real ants are capable of finding the shortest path from a food source to their nest without using visual informations. This behavior of real ants has inspired ant algorithm. There are various versions of Ant Algorithm. Ant Colony System (ACS) is introduced lately. ACS is applied to the Traveling Salesman Problem (TSP) for verifying the availability of ACS and evaluating the performance of ACS. ACS find a good solution for TSP When ACS is applied to different Combinatorial Optimization Problems, ACS uses the same parameters and strategies that were used for TSP. In this paper, ACS is applied to the Multicast Routing Problem. This Problem is to find the paths from a source to all destination nodes. This definition differs from that of TSP and differs from finding paths which are the shortest paths from source node to each destination nodes. We introduce parameters and strategies of ACS for Multicasting Routing Problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.1-7
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• 2019

It is one of the known methods to obtain the optimal solution using the Ant Colony Optimization Algorithm for the Traveling Salesman Problem (TSP), which is a combination optimization problem. In this paper, we solve the TSP problem by proposing an improved new ant colony optimization algorithm that combines genetic algorithm mutations in existing ant colony optimization algorithms to solve TSP problems in many cities. The new ant colony optimization algorithm provides the opportunity to move easily fall on the issue of developing local optimum values of the existing ant colony optimization algorithm to global optimum value through a new path through mutation. The new path will update the pheromone through an ant colony optimization algorithm. The renewed new pheromone serves to derive the global optimal value from what could have fallen to the local optimal value. Experimental results show that the existing algorithms and the new algorithms are superior to those of existing algorithms in the search for optimum values of newly improved algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.6
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• pp.506-513
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• 2000

This paper proposes a path planning algorithm for dispensers to increase the productivity in printed circuit board assembly lines. We analyze the assembly sequence of the dispenser, and formulate it as an integer programming problem. The mathematical formulation can accomodate multiple heads and different types of heads through extended cost matrix. The TSP algorithms are then applied to the formulated problem to find the near-optimal solution. Simulation results are presented to verify the usefulness of the proposed scheme.

• Nuclear Engineering and Technology
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• v.44 no.7
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• pp.799-806
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• 2012

In recent years, a new algorithm has been introduced to perform the regional overpower protection (ROP) detector layout optimization for $CANDU^{(R)}$ reactors. This algorithm is called DETPLASA. This algorithm has been shown to successfully come up with a detector layout which meets the target trip set point (TSP) value. Knowing that these ROP detectors are placed in a number of safety channels, one expects that there is an optimal placement of the candidate detectors into these channels. The objective of the present paper is to show that a slight improvement to the TSP value can be realized by optimizing the channelization of these ROP detectors. Depending on the size of the ROP system, based on numerical experiments performed in this study, the range of additional TSP improvement is from 0.16%FP (full power) to 0.56%FP.