• Journal of the Korea Society of Computer and Information
• /
• v.11 no.1 s.39
• /
• pp.129-138
• /
• 2006

Various techniques have been applied to solve the maximal covering problem. Tabu search is also one of them. But, existing researches were lacking of the synthetic analysis and the effort for performance improvement about neighborhood search techniques such as hill-climbing search and simulated annealing including tabu search. In this paper, I introduce the way to improve performance of neighborhood search techniques through various experiments and analyses. Basically, all neighborhood search algorithms use the k-exchange neighborhood generation method. And I analyzed how the performance of each algorithm changes according to various parameter settings. Experimental results have shown that simple hill-climbing search and simulated annealing can produce better results than any other techniques. And I confirmed that simple hill-climbing search can produce similar results as simulated annealing unlike general case.

• IE interfaces
• /
• v.15 no.4
• /
• pp.338-348
• /
• 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.

• Journal of KIISE:Software and Applications
• /
• v.31 no.6
• /
• pp.829-839
• /
• 2004

Methods based on integer programming have been shown to be very effective in solving various crew pairing optimization problems. However, their applicability is limited to problems with linear constraints and objective functions. Also, those methods often require an unacceptable amount of time and/or memory resources given problems of larger scale. Heuristic methods such as neighborhood search, on the other hand, can handle large-scaled problems without too much difficulty and can be applied to problems having any form of objective functions and constraints. However, neighborhood search often gets stuck at local optima when faced with complex search spaces. This paper presents ,i hybrid algorithm of neighborhood search and integer programming, which nicely combines the advantages of both methods. The hybrid algorithm has been successfully tested on a large-scaled crew pairing optimization problem for a real subway line.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.2
• /
• pp.27-35
• /
• 2009

Integer programming is a very effective technique for searching optimal solution of combinatorial optimization problems. However, its applicability is limited to linear models. In this paper, I propose an effective method for solving a nonlinear optimization problem by integrating the powerful search performance of integer programming and the flexibility of neighborhood search algorithms. In the first phase, integer programming is executed with subproblem which can be represented as a linear form from the given problem. In the second phase, a neighborhood search algorithm is executed with the whole problem by taking the result of the first phase as the initial solution. Through the experimental results using a nonlinear maximal covering problem, I confirmed that such a simple integration method can produce far better solutions than a neighborhood search algorithm alone. It is estimated that the success is primarily due to the powerful performance of integer programming.

• Industrial Engineering and Management Systems
• /
• v.8 no.1
• /
• pp.29-36
• /
• 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.

• Proceedings of the IEEK Conference
• /
• 2002.07a
• /
• pp.595-598
• /
• 2002

This paper proposes neighborhood reduction techniques in local search of the customer decomposition subproblem in the Multi Depots Vehicle Routing Problem with Time Windows (MDVRPTW) by using geospatial relation among depots and customers. The neighborhood of the customer decomposition subproblem can be simply and well defined but it should include excessively bad solution candidates. Our techniques find such candidates by using information of the problem domain, geographical relation. We use our techniques in Tabu Search and evaluate the effectiveness in computer experiment.

• Journal of Korean Institute of Industrial Engineers
• /
• v.43 no.1
• /
• pp.1-11
• /
• 2017

Stochastic resource-constrained project scheduling problem is an extension of resource-constrained project scheduling problem such that activity duration has stochastic nature. In real situation where activity duration is not known until the activity is finished, open-loop based static policies such as activity-based policy and priority-based policy will not well cope with duration variability. Then, a dynamic policy based on closed-loop decision making will be regarded as an alternative toward achievement of minimal makespan. In this study, a dynamic policy designed to select activities to start at each decision time point is illustrated. The performance of static and dynamic policies based on variable neighborhood search is evaluated under the discrete-event simulation environment. Experiments with J120 sets in PSPLIB and several probability distributions of activity duration show that the dynamic policy is superior to static policies. Even when the variability is high, the dynamic policy provides stable and good solutions.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.41 no.1
• /
• pp.41-53
• /
• 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.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.18 no.35
• /
• pp.107-114
• /
• 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.

• Management Science and Financial Engineering
• /
• v.15 no.1
• /
• pp.81-96
• /
• 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.