• 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.

• 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.

• 산업경영시스템학회지
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• 제22권52호
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• pp.297-309
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• 1999

Resource Constrained Dynamic Multi-Projects Scheduling(RCDMPS) is intended to minimize the total processing time(makespan) of two or more projects sequentially arriving at the shop under restricted resources. The aim of this paper is to develop the new Tabu Search heuristic for RCDMPS to minimize makespan. We propose the insertion method to generate the neighborhood solutions in applying the Tabu Search for the RCDMPS and the diversification strategy to search the solution space diversely. The proposed diversification strategy apply the dynamic tabu list that the tabu list size is generated and renewed at each iteration by the complexity of the project, and change the proposed tabu attribute. In this paper, We use the dynamic tabu list for the diversification strategy and intensification strategy in the tabu search, and compare with other dispatching heuristic method to verify that the new heuristic method minimize the makespan of the problem.

• 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.

• 한국통신학회논문지
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• 제38A권8호
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• pp.656-661
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• 2013

홀로그래픽 저장장치의 Guided Scrambling (GS) 인코딩에서는 소스비트에 제어비트를 추가하여 스크램블링된 코드워드에서 변조 제약식을 만족하는 최적의 코드를 선택한다. 본 연구에서는 GS 인코딩 과정을 선형식으로 수식화하여, 제어비트를 직접 계산하는 0-1 정수계획법 모형을 소개한다. 또한 정수계획법의 계산 복잡도를 줄이기 위해 제어비트를 결정하는 이웃탐색 휴리스틱 알고리즘을 개발한다. 개발된 모형은 다양한 코드 행렬 및 제어비트를 갖는 문제들에 적용하여 성능을 비교하였다.

• 한국컴퓨터정보학회논문지
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• 제21권9호
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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.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2008년도 추계학술대회 및 정기총회
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• pp.131-135
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• 2008

In this paper, we deal with a hierarchical location-allocation problem in designing the broadband convergence networks (BcN). The objective is to minimize the total cost of switch and cable while satisfying the quality of service (QoS). We formulate the problem as an integer programming model and develop the Tabu Search (TS) heuristic algorithm to find a good feasible solution within a reasonable time limit. Initial solution is obtained by using the tree structure. Three neighborhood generation mechanisms are used by local search heuristic: insertion, switch up, and switch down. In order to demonstrate the effectiveness of the proposed algorithm, we generate lower bounds from nonlinear QoS relaxation problem. We present promising computational results of the proposed solution procedures.

• 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.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2005년도 춘계 학술발표회 논문집
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• pp.359-366
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• 2005

Most engineering optimization are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithm, however, reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the problem, the result may depend on the selection of an initial point, and the obtained optimal solution may not necessarily be the global optimum. This paper describes a new harmony search(HS) meta-heuristic algorithm-based approach for structural size optimization problems with continuous design variables. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. Two classical space truss optimization problems are presented to demonstrate the effectiveness and robustness of the HS algorithm. The results indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to structural engineering problems than those obtained using current algorithms.

• 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.