• 산업공학
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• 제15권4호
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• pp.338-348
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• 2002

In this paper, we present a restricted tabu search(RTS) algorithm that schedules jobs on identical parallel machines in order to minimize the maximum lateness of jobs. Jobs have release times and due dates. Also, sequence-dependent setup times exist between jobs. The RTS algorithm consists of two main parts. The first part is the MATCS(Modified Apparent Tardiness Cost with Setups) rule that provides an efficient initial schedule for the RTS. The second part is a search heuristic that employs a restricted neighborhood generation scheme with the elimination of non-efficient job moves in finding the best neighborhood schedule. The search heuristic reduces the tabu search effort greatly while obtaining the final schedules of good quality. The experimental results show that the proposed algorithm gives better solutions quickly than the existing heuristic algorithms such as the RHP(Rolling Horizon Procedure) heuristic, the basic tabu search, and simulated annealing.

• 한국컴퓨터정보학회논문지
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• 제11권1호
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• pp.129-138
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• 2006

지금까지 maximal covering문제를 해결하기 위해 다양한 기법들이 적용되어 왔다. 타부 탐색 역시 그 중의 하나이다. 그러나 기존 연구에서는 타부 탐색을 비롯한 언덕오르기 탐색이나 시뮬레이티드 어닐링과 같은 이웃해 탐색 기법들에 대한 종합적인 분석과 성능 향상을 위한 노력이 부족하였다. 본 논문에서는 다양한 실험과 분석을 통해 이웃해 탐색 기법들의 성능을 향상시키기 위한 방안을 소개한다. 기본적으로 모든 이웃해 탐색 기법들은 k-exchange 이웃해 생성 방법을 사용하고 있으며 다양한 파라미터 설정에 따라 각 기법의 성능이 어떻게 달라지는가를 분석하였다. 실험 결과 단순 언덕오르기 탐색과 시뮬레이티드 어닐링이 다른 기법들에 비해 훨씬 우수한 탐색 성능을 보였으며, 일반적인 경우와는 달리 단순 언덕오르기 탐색이 시뮬레이티드 어닐링과 비슷한 성능을 보임을 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제14권2호
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• pp.27-35
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• 2009

정수계획법은 조합 최적화 문제의 최적해를 매우 효과적으로 탐색할 수 있는 기법인 반면에 대상 문제가 선형적으로 표현되어야만 적용이 가능하다는 단점이 있다. 본 논문에서는 정수계획 법의 뛰어난 탐색 능력과 이웃해 탐색 기법의 유연성을 결합함으로써 비선형 최적화 문제를 효과적으로 해결하는 방안을 제시하고 있다. 먼저 1단계에서는 주어진 문제로부터 선형적으로 표현 가능한 부문제만을 대상으로 정수계획 법을 적용한다. 2단계에서는 전체 문제를 대상으로 이웃해 탐색 기법을 적용하되 1단계의 결과를 초기해로 설정한 후 탐색을 수행한다. 비선형 최대 커버링 문제를 대상으로 한 실험 결과, 이와 같은 간단한 결합만으로도 이웃해 탐색 기법만을 적용했을 때보다 훨씬 좋은 해를 도출할 수 있음을 확인하였다. 이는 기본적으로 정수계획법의 탁월한 성능에 기인한 것으로 판단된다.

• Management Science and Financial Engineering
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• 제15권1호
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• pp.81-96
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• 2009

Data mining, specially clustering is one of exciting research areas for ant based algorithms. Ant clustering algorithm, however, has many difficulties for resolving practical situations in clustering. We propose a new grid-based ant colony algorithm for clustering of data. The previous ant based clustering algorithms usually tried to find the clusters during picking up or dropping down process of the items of ants using some stigmergy information. In our ant clustering algorithm we try to make the ants reflect neighborhood information within the storage nests. We use two ant classes, search ants and labor ants. In the initial step of the proposed algorithm, the search ants try to guide the characteristics of the storage nests. Then the labor ants try to classify the items using the guide in-formation that has set by the search ants and the stigmergy information that has set by other labor ants. In this procedure the clustering decision of ants is quickly guided and keeping out of from the stagnated process. We experimented and compared our algorithm with other known algorithms for the known and statistically-made data. From these experiments we prove that the suggested ant mining algorithm found the clusters quickly and effectively comparing with a known ant clustering algorithm.

• Advances in aircraft and spacecraft science
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• 제10권5호
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• pp.393-418
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• 2023

Drones are increasingly used in logistics delivery due to their low cost, high-speed and straight-line flight. Considering the small cargo capacity, limited endurance and other factors, this paper optimized the pickup and delivery vehicle routing problem with time windows in the mode of "truck+drone". A mixed integer programming model with the objective of minimizing transportation cost was proposed and an improved adaptive large neighborhood search algorithm is designed to solve the problem. In this algorithm, the performance of the algorithm is improved by designing various efficient destroy operators and repair operators based on the characteristics of the model and introducing a simulated annealing strategy to avoid falling into local optimum solutions. The effectiveness of the model and the algorithm is verified through the numerical experiments, and the impact of the "truck+drone" on the route cost is analyzed, the result of this study provides a decision basis for the route planning of "truck+drone" mode delivery.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권6호
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• pp.829-839
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• 2004

정수계획법에 기반 한 기법들은 다양한 승무일정계획 최적화 문제를 해결하는 데 매우 효과적인 것으로 알려져 있다. 그러나 정수계획법은 대상 문제의 제약조건 및 목적함수가 모두 선형적으로 표현되어야만 적용이 가능하다는 단점이 있으며 문제의 규모가 클 경우 과도한 수행 시간과 메모리 자원을 요구하게 된다. 반면 이웃해 탐색 기법과 같은 휴리스틱 탐색 기법은 대상 문제의 제약조건이나 목적함수의 형태에 관계없이 쉽게 적응이 가능하다. 그러나 이웃해 탐색 기법은 복잡한 탐색 공간을 탐색할 경우 국소 최적해에 도달한 후 국소 최적해로부터 쉽게 빠져나오지 못하는 경우가 많다. 본 논문에서는 이웃해 탐색 기법과 정수계획법의 장점을 효과적으로 결합하기 위한 방안을 제시하고 있으며 실제 운행중인 지하철 승무일정계획 문제에 적용해 봄으로써 대규모 승무일정계획 최적화 문제에 성공적으로 적용될 수 있음을 확인하였다.

• 한국경영과학회지
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• 제41권1호
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• pp.41-53
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• 2016

Static repositioning is a well-known and commonly used strategy to maximize customer satisfaction in public bike-sharing systems. Repositioning is performed by trucks at night when no customers are in the system. In models that represent the static repositioning problem, the decision variables are truck routes and the number of bikes to pick up and deliver at each rental station. To simplify the problem, the decision on the number of bikes to pick up and deliver is implicitly included in the truck routes. Two relocation-based local search algorithms (1-relocate and 2-relocate) with the best-accept strategy are incorporated into a variable neighborhood search (VNS) to obtain high-quality solutions for the problem. The performances of the VNS algorithm with the effect of local search algorithms and shaking strength are evaluated with data on Tashu public bike-sharing system operating in Daejeon, Korea. Experiments show that VNS based on the sequential execution of two local search algorithms generates good, reliable solutions.

• 산업경영시스템학회지
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• 제18권35호
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• pp.107-114
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• 1995

This paper proposes the Tabu Search algorithm to minimize mean tardiness in the parallel machine scheduling problem. The algorithm reduces the computation time by employing restricted neighborhood and produces an efficient solution in this problem.

• Industrial Engineering and Management Systems
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• 제8권1호
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• pp.22-28
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• 2009

We consider facility layout problems, where mn facility units are assigned into mn cells. These cells are arranged into a rectangular pattern with m rows and n columns. In order to solve this cell type facility layout problem, many approximation algorithms with improved local search methods were studied because it was quite difficult to find exact optimum of such problem in case of large size problem. In this paper, new algorithms based on Simulated Annealing (SA) method with two neighborhood generation methods are proposed. The new neighborhood generation method adopts the exchanging operation of facility units in accordance with adjacent preference. For evaluating the performance of the neighborhood generation method, three algorithms, previous SA algorithm with random 2-opt neighborhood generation method, the SA-based algorithm with the new neighborhood generation method (SA1) and the SA-based algorithm with probabilistic selection of random 2-opt and the new neighborhood generation method (SA2), are developed and compared by experiment of solving same example problem. In case of numeric examples with problem type 1 (the optimum layout is given), SA1 algorithm could find excellent layout than other algorithms. However, in case of problem type 2 (random-prepared and optimum-unknown problem), SA2 was excellent more than other algorithms.

• Industrial Engineering and Management Systems
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• 제8권1호
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• pp.29-36
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• 2009

We present two local-search based metaheuristics for the multi-source capacitated facility location problem. In such a problem, each customer's demand can be supplied by one or more facilities. The problem is NP-hard and the number of locations in the optimal solution is unknown. To keep the search process effective, the proposed methods adopt the following features: (1) a multi-exchange neighborhood structure, (2) a tabu list that keeps track of recently visited solutions, and (3) a multi-start to enhance the diversified search paths. The transportation simplex method is applied in an efficient manner to obtain the optimal solutions to neighbors of the current solution under the algorithm framework. Two in-and-out selection rules are also proposed in the algorithms with the purpose of finding promising solutions in a short computational time. Our computational results for some of the benchmark instances, as well as some instances generated using a method in the literature, have demonstrated the effectiveness of this approach.