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Titlebook: Variable Neighborhood Search; 7th International Co Rachid Benmansour,Angelo Sifaleras,Nenad Mladenovi Conference proceedings 2020 Springer

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Local Search Approach for the (,|,)-Centroid Problem Under , Metric,he Leader’s facilities maximizing her market share. We provide the results on the computational complexity of this problem and develop a local search heuristic, based on the VNS framework. Computational experiments on the randomly generated test instances show that the proposed approach performs well.
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A Reduced Variable Neighborhood Search Approach for Feature Selection in Cancer Classification,Elimination (RFE) heuristic with a RVNS algorithm. Despite the large size of the problem instances, the suggested feature selection scheme converges within reasonably short time, when compared to similar methods. Results indicate high performance for RVNS that, is further improved when the RFE method is applied as a pre-processing step.
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A Variable Neighborhood Search Algorithmic Approach for Estimating MDHMM Parameters and Application hybrid model in which VNS algorithm is coupled with Baum-Welch algorithm for parameter estimation of MDHMM, is applied in credit scoring domain, using real peer-to-peer lending data. The experiments results show the performance efficiency of our model in comparison with classical and alternative machine learning models for credit scoring.
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Daily Scheduling and Routing of Home Health Care with Multiple Availability Periods of Patients,ed using CPLEX IBM. To deal with large instances a general variable neighborhood search (GVNS) based heuristic is proposed, implemented and tested using the language C++. Computational results show that the proposed heuristic could find a good solution in a very short computational time.
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Optimization of Maintenance Planning and Routing Problems, Neighborhood Search that uses sequentially different neighborhood structures. The performance of our algorithms is evaluated using new generated instances. Results provide strong evidence of the effectiveness of our heuristic approach.
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