1,896 research outputs found

    Better and faster solutions for the maximum diversity problem

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    The aim of the Maximum Diversity Problem (MDP) is to extract a subset M of given cardinality from a set of elements N, in such a way that the sum of the pairwise distances between the elements of M is maximum. This problem, introduced by Glover [7], has been deeply studied using GRASP methodologies [6, 1, 17, 2, 16]. Usually, effective algorithms owe their success more to the careful exploitation of problem-specific features than to the application of general-purpose methods. A solution for MDP has a very simple structure which can not be exploited for sophisticated neighborhood search. This paper explores the performance of three alternative solution approaches, that is Tabu Search, Variable Neighborhood Search and Scatter Search, comparing them with those of best GRASP algorithms in literature. We also focus our attention on the comparison of these three methods applied in their pure form

    The crew-scheduling module in the GIST system

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    The public transportation is gaining importance every year basically due the population growth, environmental policies and, route and street congestion. Too able an efficient management of all the resources related to public transportation, several techniques from different areas are being applied and several projects in Transportation Planning Systems, in different countries, are being developed. In this work, we present the GIST Planning Transportation Systems, a Portuguese project involving two universities and six public transportation companies. We describe in detail one of the most relevant modules of this project, the crew-scheduling module. The crew-scheduling module is based on the application of meta-heuristics, in particular GRASP, tabu search and genetic algorithm to solve the bus-driver-scheduling problem. The metaheuristics have been successfully incorporated in the GIST Planning Transportation Systems and are actually used by several companies.Integrated transportation systems, crew scheduling, metaheuristics

    Randomized heuristics for the Capacitated Clustering Problem

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    In this paper, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomization and greediness on the performance of these multi-start heuristic search methods when solving this NP-hard problem. The former is a memory-less approach that constructs independent solutions, while the latter is a memory-based method that constructs linked solutions, obtained by partially rebuilding previous ones. Both are based on the combination of greediness and randomization in the constructive process, and coupled with a subsequent local search phase. We propose these two multi-start methods and their hybridization and compare their performance on the CCP. Additionally, we propose a heuristic based on the mathematical programming formulation of this problem, which constitutes a so-called matheuristic. We also implement a classical randomized method based on simulated annealing to complete the picture of randomized heuristics. Our extensive experimentation reveals that Iterated Greedy performs better than GRASP in this problem, and improved outcomes are obtained when both methods are hybridized and coupled with the matheuristic. In fact, the hybridization is able to outperform the best approaches previously published for the CCP. This study shows that memory-based construction is an effective mechanism within multi-start heuristic search techniques
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