2008
DOI: 10.1007/s10288-008-0089-1
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Variable neighbourhood search: methods and applications

Abstract: Variable neighbourhood search (VNS) is a metaheuristic, or a framework for building heuristics, based upon systematic changes of neighbourhoods both in descent phase, to find a local minimum, and in perturbation phase to emerge from the corresponding valley. It was first proposed in 1997 and has since then rapidly developed both in its methods and its applications. In the present paper, these two aspects are thoroughly reviewed and an extensive bibliography is provided. Moreover, one section is devoted to newc… Show more

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Cited by 258 publications
(98 citation statements)
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References 236 publications
(159 reference statements)
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“…Para resolver numericamente o problema de otimização (8)- (11) propõe-se o algoritmo heurístico VNS-Variable neighborhood search [8].…”
Section: Algoritmo Vns_aedesunclassified
“…Para resolver numericamente o problema de otimização (8)- (11) propõe-se o algoritmo heurístico VNS-Variable neighborhood search [8].…”
Section: Algoritmo Vns_aedesunclassified
“…When the neighborhood size increases, the number of changed routing decisions increases. The VNS algorithm used is an adaptation of the "Basic" VNS described in Hansen et al (2008).…”
Section: Mathematical Models 31 Optimization Of Discrete Event Dynamicsmentioning
confidence: 99%
“…2, 3 and 4 by i∈M Y ik = 1, ∀k ∈ N, (10) (12) and adding Y ik ≥ 0, ∀k ∈ N, ∀i ∈ M to constraints (9). Constraints (10) ensure that each job is assigned exactly to one machine.…”
Section: Fig 2 Graphical Representation Of a Solutionmentioning
confidence: 99%
“…In both stages, strategies borrowed from variable neighbourhood descent (VND) metaheuristic [9] have been developed which rely on the following facts: (i) a local minimum with respect to one neighbourhood structure is not necessarily so with another and (ii) a global minimum is a local minimum with respect to all possible neighbourhood structures. An initial solution is improved by exploring several neighbourhood structures N g (g = 1, .…”
Section: Improvement Proceduresmentioning
confidence: 99%