Tabu Search

Gendreau, Michel; Potvin, Jean-Yves

doi:10.1007/978-1-4419-1665-5_2

Cited by 103 publications

(97 citation statements)

References 50 publications

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“…The TS can be seen as a hill-climbing technique that tries to prevent local optimality by allowing non-improving moves. The principles of the TS can be briefly described as follows (Gendreau and Potvin, 2010). The algorithm starts with a feasible initial solution and tries to iteratively improve the solution by examining a set of candidate moves in the neighborhood of that solution.…”

Section: Generalmentioning

confidence: 99%

Hybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem

Sels

Coelho

Dias

et al. 2015

Computers & Operations Research

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We consider the problem of scheduling a number of jobs on a number of unrelated parallel machines in order to minimize the makespan. We develop three heuristic approaches, i.e., a genetic algorithm, a tabu search algorithm and a hybridization of these heuristics with a truncated branch-and-bound procedure. This hybridization is made in order to accelerate the search process to near-optimal solutions. The branchand-bound procedure will check whether the solutions obtained by the meta-heuristics can be scheduled within a tight upper bound. We compare the performances of these heuristics on a standard data set available in the literature. Moreover, the influence of the different heuristic parameters is examined as well. The computational experiments reveal that the hybrid heuristics are able to compete with the best known results from the literature.

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Section: Generalmentioning

confidence: 99%

Hybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem

Sels

Coelho

Dias

et al. 2015

Computers & Operations Research

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“…This value is inserted to formula (10), through which the initial temperature can be obtained. Moreover, in order to find the assumed shape of the generated distribution, in the first iteration of the FSA algorithm, the additional scaling coefficient is calculated: = − ∆ ̅̅̅̅ ln (1) .…”

Section: Procedures For Dimension Reductionmentioning

confidence: 99%

“…Apart from the classical methods -fuzzy logic and neural networks, metaheuristics such as genetic algorithms, simulated annealing, particle swarm optimization, and ants algorithms [1] are also being applied here more widely. The proper combination and exploitation of the advantages of these techniques allows in practice for the effective solution to fundamental problems in knowledge engineering, particularly those connected with exploratory data analysis.…”

Section: Introductionmentioning

confidence: 99%

“…with particular rows representing the realizations of an -dimensional random variable 1 . The aim of reducing a dimension is to transform the data matrix in order to obtain its new representation of the dimension × , where is considerablyfrom the point of view of conditioning of a problem in question -smaller than .…”

Section: Introductionmentioning

confidence: 99%

“…The latter can be treated as more generalthe selection is a particularly simple case of extraction. Noteworthy among extraction procedures are linear methods, where the resulting data set is obtained through the linear transformation of initial data set (1), therefore using the formula…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reduction of Dimension and Size of Data Set by Parallel Fast Simulated Annealing

Kulczycki

Łukasik

2014

Studies in Computational Intelligence

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Abstract.A universal method of dimension and sample size reduction, designed for exploratory data analysis procedures, constitutes the subject of this paper. The dimension is reduced by applying linear transformation, with the requirement that it has the least possible influence on the respective locations of sample elements. For this purpose an original version of the heuristic Parallel Fast Simulated Annealing method was used. In addition, those elements which change the location significantly as a result of the transformation, may be eliminated or assigned smaller weights for further analysis. As well as reducing the sample size, this also improves the quality of the applied methodology of knowledge extraction. Experimental research confirmed the usefulness of the procedure worked out in a broad range of problems of exploratory data analysis such as clustering, classification, identification of outliers and others.

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A method for solving the multi‐objective transit frequency optimization problem

Giesen¹,

Martínez

Mauttone

et al. 2016

J of Advced Transportation

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Frequency setting takes place at the strategic and tactical planning stages of public transportation systems. The problem consists in determining the time interval between subsequent vehicles for a given set of lines, taking into account interests of users and operators. The result of this stage is considered as input at the operational level. In general, the problem faced by planners is how to distribute a given fleet of buses among a set of given lines. The corresponding decisions determine the frequency of each line, which impacts directly on the waiting time of the users and operator costs. In this work, we consider frequency setting as the problem of minimizing simultaneously users' total travel time and fleet size, which represents the interest of operators. There is a trade-off between these two measures; therefore, we face a multiobjective problem. We extend an existing single-objective formulation to account explicitly for this tradeoff, and propose a Tabu Search solving method to handle efficiently this multi-objective variant of the problem. The proposed methodology is then applied to a real medium-sized problem instance, using data of Puerto Montt, Chile. We consider two data sets corresponding to morning-peak and off-peak periods. The results obtained show that the proposed methodology is able to improve the current solution in terms of total travel time and fleet size. In addition, the proposed method is able to efficiently suggest (in computational terms) different trade-off solutions regarding the conflicting objectives of users and operators.to the users and minimize the level of required resources delivered by the operators. Problem data is given by the itinerary of each line and origin-destination (OD) demand within a specific time horizon. An important component of the model is the assignment sub-model, which represents the behavior of the users with respect to a set of lines and frequencies. This sub-model is needed to measure the performance of the system with respect to the users, that is, the level of service.The literature concerning transit frequency optimization can be classified into (i) analytical models that admit closed-form solutions and (ii) mathematical programming formulations either explicit or not, with associated solution algorithms. In the first group, there are formulations that characterize the system in terms of few variables and allow getting a full description of the optimal solution. Although these models make considerable simplifications of the real system, they allow obtaining practical guidelines that are theoretically well founded [3,4], for example, the well-known rule of the square root [5,6]. The other important stream of work is based on a detailed characterization of the transit system, in terms of the route network and the demand that should be transported over it. These studies formulate the optimization problem in terms of a graph model, where decision variables are the capacities of the arcs (represented by the frequencies) and the flows that represent the...

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Tabu Search

Cited by 103 publications

References 50 publications

Hybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem

Hybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem

Reduction of Dimension and Size of Data Set by Parallel Fast Simulated Annealing

A method for solving the multi‐objective transit frequency optimization problem

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