• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.80-87
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• 2010

As express courier market expands rapidly, companies are exposed to fierce competition. To cope with struggle for their survival, they are continuously making efforts to improve their service system. Even if most of service centers are directly linked to a consolidation terminal in courier service network, some of them with regional disadvantages are operated in milk run type from/to the consolidation terminal, which is a traditional PDP (Pick-up and Delivery Problem). This study suggests an approach to solve the PDP with the objective of maximizing the incremental profit, which belongs to PTP (Profitable Tour Problem) class. After the PTP is converted to TSP (Traveling Salesman Problem) with the same objective, a heuristic algorithm based on GA (Genetic Algorithm) is developed and examined through an example problem in practice of a courier service company in Korea.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.21-28
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• 1999

The TSP(Traveling Salesman Problem) is one of the most widely studied problems in combinatorial optimization. The most common interpretation of TSP is finding a shortest Hamiltonian tour of all cities. The objective of this paper proposes a new heuristic algorithm MCH(Multi-Convex hulls Heuristic). MCH is a algorithm for finding good approximate solutions to practical TSP. The MCH algorithm is using the characteristics of the optimal tour. The performance results of MCH algorithm are superior to others algorithms (NNH, CCA) in CPU time.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.10a
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• pp.169-169
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• 1999

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.69-76
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• 2022

The traveling salesman problem(TSP) is one of the most famous combinatorial optimization problem. So far, many metaheuristic search algorithms have been proposed to solve the problem, and one of them is local search. One of the very important factors in local search is neighbor generation method, and random-based neighbor generation methods such as inversion have been mainly used. This paper proposes 4 new greedy-based neighbor generation methods. Three of them are based on greedy insertion heuristic which insert selected cities one by one into the current best position. The other one is based on greedy rotation. The proposed methods are applied to first-choice hill-climbing search and simulated annealing which are representative local search algorithms. Through the experiment, we confirmed that the proposed greedy-based methods outperform the existing random-based methods. In addition, we confirmed that some greedy-based methods are superior to the existing local search methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.58-68
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• 2010

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.862-866
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• 2003

Traveling Salesman Problem(TSP) is a combinational optimization problem, Genetic Algorithm(GA) and Lin-Kernighan(LK) Heuristic［1］that is Local Search Heuristic are one of the most commonly used methods to resolve TSP. In this paper, we introduce ACS(Ant Colony System) Algorithm as another approach to solve TSP and propose a new pheromone updating method. ACS uses pheromone information between cities in the Process where many ants make a tour, and is a method to find a optimal solution through recursive tour creation process. At the stage of Global Updating of ACS method, it updates pheromone of edges belonging to global best tour of created all edge. But we perform once more pheromone update about created all edges before global updating rule of original ACS is applied. At this process, we use the frequency of occurrence of each edges to update pheromone. We could offer stochastic value by pheromone about each edges, giving all edges' occurrence frequency as weight about Pheromone. This finds an optimal solution faster than existing ACS algorithm and prevent a local optima using more edges in next time search.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.155-165
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• 2012

This paper suggests traveling salesman problem algorithm that have been unsolved problem with NP-Hard. The proposed algorithm is a heuristic with edge-swap method. The classical method finds the initial solution starts with first node and visits to mostly adjacent nodes then decides the traveling path. This paper selects minimum weight edge for each nodes, then perform Min-Min method that start from minimum weight edge among the selected edges and Min-Max method that starts from maximum weight edges among it. Then we decide tie initial solution to minimum path length between Min-Min and Min-Max method. To get the final optimal solution, we apply previous two-opt to initial solution. Also, we suggest extended 3-opt and 4-opt additionally. For the 7 actual experimental data, this algorithm can be get the optimal solutions of state-of-the-art with fast and correct.

• Journal of the Korea Computer Industry Society
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• v.2 no.10
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• pp.1349-1354
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• 2001

In the Traveling Salesman Problem(TSP), a set of N cities is given and the problem is to find the shortest route connecting them all, with no city visited twice and return to the city at which it started. Since TSP is a well-known combinatorial optimization problem and belongs to the class of NP-complete problems, various techniques are required for finding optimum or near optimum solution to the TSP. Especially DNA computing, which uses real bio-molecules to perform computations supported by molecular biology, has been studied by many researchers to solve NP-complete problem using massive parallelism of DNA computing. Though very promising, DNA computing technology of today is inefficiency because the effective computing models and operations reflected the characteristics of bio-molecules have not been developed yet. In this paper, I design new Polymerase Chain Reaction(PCR) operations of DNA computing to solve TSP.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.79-84
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• 2016

In this paper we consider the problem of finding the triplet (S,${\pi}$,f), where $S{\subseteq}V$, ${\pi}$ is a sequence of nodes in S and $f:V{\backslash}S{\rightarrow}S$ for a given complete graph G=(V,E). In particular, there exist two costs, $c^V_{uv}$ and $c^D_{uv}$ for $(u,v){\in}E$, and the cost of triplet (S,${\pi}$,f) is defined as $\sum_{i=1}^{{\mid}S{\mid}}c^V_{{\pi}(i){\pi}(i+1)}+2$ ${\sum_{u{\in}V{\backslash}S}c^D_{uf(u)}$. This problem is motivated by the integrated routing of the vehicle and drone for urban delivery services. Since a well-known NP-complete TSP (Traveling Salesman Problem) is a special case of our problem, we cannot expect to have any polynomial-time algorithm unless P=NP. Furthermore, for practical purposes, we may not rely on time-exhaustive enumeration method such as branch-and-bound and branch-and-cut. This paper suggests the simple heuristic which is motivated by the MST (minimum spanning tree)-based approximation algorithm and neighborhood search heuristic for TSP.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.41
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• pp.1-14
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• 1997

This study proposes a genetic algorithm using Pr(equation omitted)fer number for the traveling salesman problem(PNGATSP). Nearest neighbor nodes are mixed with randomly selected nodes at the stage of generating initial solutions. Proposed PNGATSP adopts a few ideas which are different from traditional genetic algorithms. For instance, an exponential fitness function and elitism are used and Pr(equation omitted)fer number is used for encoding TSP. Genetic operators are selected by experiments, which make a good solution among four combinations of conventional genetic operators and new genetic operators. For respective combinations, robust set of parameters is determined by the experimental designing approach. The feature of Pr(equation omitted)fer number code for TSP and the search power of GA using Pr(equation omitted)fer number is analysed. The best is a combination of OX(order crossover) and swap, which is superior to the other experimented combinations of genetic operators by 1.0%∼12.8% deviation.