LEADER 05332nam 22007335 450 001 9910337569703321 005 20240112215236.0 010 $a3-030-15843-8 024 7 $a10.1007/978-3-030-15843-9 035 $a(CKB)4100000007810402 035 $a(DE-He213)978-3-030-15843-9 035 $a(MiAaPQ)EBC5922747 035 $a(PPN)235231924 035 $a(EXLCZ)994100000007810402 100 $a20190313d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aVariable Neighborhood Search $e6th International Conference, ICVNS 2018, Sithonia, Greece, October 4?7, 2018, Revised Selected Papers /$fedited by Angelo Sifaleras, Said Salhi, Jack Brimberg 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XI, 315 p. 93 illus., 26 illus. in color.) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v11328 311 0 $a3-030-15842-X 320 $aIncludes bibliographical references and index. 327 $aImproved variable neighbourhood search heuristic for quartet clustering -- On the k-medoids model for semi-supervised clustering -- Complexity and Heuristics for the Max Cut-Clique Problem -- A VNS approach to solve multi-level capacitated lotsizing problem with backlogging -- How to locate disperse obnoxious facility centers? -- Basic VNS algorithms for solving the pollution location inventory routing problem -- Less is More: The Neighborhood Guided Evolution Strategies convergence on some classic neighborhood operators -- New VNS variants for the Online Order Batching Problem -- An adaptive VNS and Skewed GVNS approaches for School Timetabling Problems -- Finding balanced bicliques in bipartite graphs using Variable Neighborhood Search -- General Variable Neighborhood Search for Scheduling Heterogeneous Vehicles in Agriculture -- Detecting weak points in networks using Variable Neighborhood Search -- A Variable neighborhood search with integer programming for the zero-one Multiple-Choice Knapsack Problem with Setup -- A VNS-based Algorithm with Adaptive Local Search for Solving the Multi-Depot Vehicle Routing Problem -- Skewed Variable Neighborhood Search Method for the Weighted Generalized Regenerator Location Problem -- Using a variable neighborhood search to solve the single processor scheduling problem with time restrictions -- An Evolutionary Variable Neighborhood Descent for addressing an electric VRP variant -- A Variable Neighborhood Descent heuristic for the multi-quay Berth Allocation and Crane Assignment Problem under availability constraints -- A Variable Neighborhood Search approach for solving the Multidimensional Multi-way Number Partitioning Problem -- A general variable neighborhood search with Mixed VND for the multi-Vehicle multi-Covering Tour Problem -- A Hybrid Firefly - VNS Algorithm for the Permutation Flowshop Scheduling Problem -- Studying the impact of perturbation methods on the efficiency of GVNS for the ATSP -- A general variable neighborhood search algorithm to solve vehicle routing problems with optional visits. 330 $aThis book constitutes the refereed post-conference proceedings of the 6th International Conference on Variable Neighborhood Search, ICVNS 2018, held in Sithonia, Greece, in October 2018. ICVNS 2018 received 49 submissions of which 23 full papers were carefully reviewed and selected. VNS is a metaheuristic based on systematic changes in the neighborhood structure within a search for solving optimization problems and related tasks. The main goal of ICVNS 2018 was to provide a stimulating environment in which researchers coming from various scientific fields could share and discuss their knowledge, expertise, and ideas related to the VNS metaheuristic and its applications. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v11328 606 $aNumerical analysis 606 $aComputer science$xMathematics 606 $aDiscrete mathematics 606 $aAlgorithms 606 $aArtificial intelligence$xData processing 606 $aMathematical optimization 606 $aNumerical Analysis 606 $aDiscrete Mathematics in Computer Science 606 $aAlgorithms 606 $aData Science 606 $aOptimization 615 0$aNumerical analysis. 615 0$aComputer science$xMathematics. 615 0$aDiscrete mathematics. 615 0$aAlgorithms. 615 0$aArtificial intelligence$xData processing. 615 0$aMathematical optimization. 615 14$aNumerical Analysis. 615 24$aDiscrete Mathematics in Computer Science. 615 24$aAlgorithms. 615 24$aData Science. 615 24$aOptimization. 676 $a511.5 676 $a511.5 702 $aSifaleras$b Angelo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSalhi$b Said$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBrimberg$b Jack$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910337569703321 996 $aVariable neighborhood search$91902308 997 $aUNINA