• 한국정보시스템학회지:정보시스템연구
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• 제25권1호
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• pp.159-182
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• 2016

Purpose This paper aims to develop a novel tabu search algorithm for solving the sequencing problems with precedence constraints. Due to constraints, the traditional meta heuristic methods can generate infeasible solutions during search procedure, which must be carefully dealt with. On the contrary, the candidate order based tabu search (COTS) is based on a novel neighborhood structure that guarantees the feasibility of solutions, and can dealt with a wide range of sequencing problems in flexible manner. Design/methodology/approach Candidate order scheme is a strategy for constructing a feasible sequence by iteratively appending an item at a time, and it has been successfully applied to genetic algorithm. The primary benefit of the candidate order scheme is that it can effectively deal with the additional constraints of sequencing problems and always generates the feasible solutions. In this paper, the candidate order scheme is used to design the neighborhood structure, tabu list and diversification operation of tabu search. Findings The COTS has been applied to the single machine job sequencing problems, and we can see that COTS can find the good solutions whether additional constraints exist or not. Especially, the experiment results reveal that the COTS is a promising approach for solving the sequencing problems with precedence constraints. In addition, the operations of COTS are intuitive and easy to understand, and it is expected that this paper will provide useful insights into the sequencing problems to the practitioners.

• 대한산업공학회지
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• 제35권4호
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• pp.257-265
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• 2009

This paper considers the hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. In hybrid flow shops, each job is processed through multiple production stages in series, each of which has multiple identical parallel machines. The problem is to determine the allocation of jobs to the parallel machines at each stage as well as the sequence of the jobs assigned to each machine. Due to the complexity of the problem, we suggest search heuristics, tabu search and simulated annealing algorithms with a new method to generate neighborhood solutions. In particular, to evaluate and select neighborhood solutions, three surrogate objectives are additionally suggested because not much difference in the number of tardy jobs can be found among the neighborhoods. To test the performances of the surrogate objective based search heuristics, computational experiments were performed on a number of test instances and the results show that the surrogate objective based search heuristics were better than the original ones. Also, they gave the optimal solutions for most small-size test instances.

• 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.

• 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.

• 한국산업정보학회논문지
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• 제27권1호
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• pp.37-48
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• 2022

본 연구에서는 공학설계 최적화 문제 해결을 위한 혼합 메타휴리스틱(Hybrid Meta-heuristic) 접근법을 제안된다. 공학 설계 최적화 문제는 다양한 형태의 변수를 가지며, 복잡한 제약조건들하에서 그 최적해를 구하는 문제로 이미 많은 기존 연구들을 통해 다양한 접근법들이 개발되어져 왔다. 하지만 그 효율성은 아직까지 크게 개선되지 못하고 있는 실정이다. 따라서 본 연구에서는 이러한 효율성을 개선하기 위한 새로운 접근법을 제안한다. 제안된 혼합 메타휴리스틱 접근법은 탐색 공간에 대한 전역적 탐색을 위해 유전알고리즘(Genetic Algorithm: GA) 접근법, 지역적 탐색을 위해 변동이웃탐색(Variable Neighborhood Search: VNS) 접근법과 언덕오르기(Hill Climbing: HC) 접근법을 혼합(GA-VNS-HC)하였다. 사례 연구에서는 다양한 형태의 공학설계 최적화 문제를 이용하여 본 연구에서 제안한 GA-VNS-HC 접근법의 우수성을 입증하였다.

• 경영과학
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• 제12권3호
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• pp.177-185
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• 1995

This paper presents a Tabu Search method to minimize a number of tardy jobs in the nonidentical parallel machine scheduling. The Tabu Search method employs a restricted neighborhood for the reduction of computation time. In this paper, we use two different types of method for a single machine scheduling. One is Moore's algorithm and the other is insertion method. We discuss computational experiments on more than 1000 test problems.

• 한국컴퓨터정보학회논문지
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• 제19권10호
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• pp.13-21
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• 2014

집합 커버링 문제는 대표적인 조합 최적화 문제들 중 하나로서 n개의 열로부터 일부를 선택하여 m개의 행을 커버하되 비용을 최소화하는 문제로 정의된다. 본 논문에서는 집합 커버링 문제를 해결하기 위한 정수 계획법 기반 지역 탐색의 적용 방안을 제시하고 있다. 정수계획법 기반 지역 탐색은 이웃해를 탐색하여 현재해를 반복적으로 개선하는 지역 탐색 기법의 일종으로서 이웃해를 생성하기 위한 알고리즘으로 정수계획법을 사용한다. 본 논문에서 제시한 기법의 효과를 검증하기 위해 OR-Library의 테스트 데이터를 대상으로 실험을 수행하였다. 실험 결과, 모든 테스트 데이터에 있어서 정수계획법 기반 지역 탐색을 통해 지금까지 알려진 가장 좋은 해를 탐색할 수 있었다. 특히 4개의 테스트 데이터에 대해서는 지금까지 알려진 가장 좋은 해보다 더 좋은 해를 도출할 수 있음을 확인할 수 있었다.

• 한국건설관리학회논문집
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• 제20권5호
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• pp.42-51
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• 2019

철도운영기관은 수송수요예측과 열차차량의 수송능력을 토대로 수송계획을 세우고 이에 따른 열차운행계획을 수립한다. 열차운행계획을 통해 열차 운행간격이 조정되어 열차운행시간표(열차다이아)가 작성되고, 열차운행에 사용되는 열차의 차량운영계획이 수립된다. 열차운영기관에서는 열차 운영관리의 효율성을 높이기 위해 다이아에 편성되어진 열차들의 승무인원을 판단하고 배치하는 승무일정계획을 수립해야 한다. 본 연구에서는 승무일정계획 문제인 승무다이아 작성단계에 제약조건을 만족하는 이웃해 탐색기법을 적용하여 승무 계획의 효율적 관리가 가능하도록 하는 방법론을 제시한다. 연구에서 제시한 방법론에 의해 생성된 승무 다이아는 실제 운영하는 열차시간표를 대상으로 기존 승무다이아와 결과 값을 비교하여 제시된 방법론의 효율성을 검증하였다.

• Industrial Engineering and Management Systems
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• 제6권1호
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• pp.64-71
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• 2007

J. Kennedy and R. Eberhart first introduced the concept called as Particle Swarm Optimization (PSO). They applied it to optimize continuous nonlinear functions and demonstrated the effectiveness of the algorithm. Since then a considerable number of researchers have attempted to apply this concept to a variety of optimization problems and obtained reasonable results. In PSO, individuals communicate and exchange simple information with each other. The information among individuals is communicated in the swarm and the information between individuals and their swarm is also shared. Finally, the swarm approaches the optimal behavior. It is reported that reasonable approximate solutions of various types of test functions are obtained by employing PSO. However, if more precise solutions are required, additional algorithms and/or hybrid algorithms would be necessary. For example, the heading vector of the swarm can be slightly adjusted under some conditions. In this paper, we propose a hybrid algorithm to obtain more precise solutions. In the algorithm, when a better solution in the swarm is found, the neighborhood of a certain distance from the solution is searched. Then, the algorithm returns to the original PSO search. By this hybrid method, we can obtain considerably better solutions in less iterations than by the standard PSO method.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.135-138
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• 1996

In solving the Travel Salesman Problem(TSP), we easily reach local optimal solutions with the existing methods such as TWO-OPT, THREE-OPT, and Lin-Kernighen. Tabu search, as a meta heuristic, is a good mechanism to get an optimal or a near optimal solution escaping from the local optimal. By utilizing AI concepts, tabu search continues to search for improved solutions. In this study, we focus on developing a new neighborhood structure that maintains the feasibility of the tours created by exchange operations in TSP. Intelligent methods are discussed, which keeps feasible tour routes even after exchanging several edges continuously. An extended tabu search model, performing cycle detection and diversification with memory structure, is applied to TSP. The model uses effectively the information gathered during the search process. Finally, the results of tabu search and simulated annealing are compared based on the TSP problems in the prior literatures.