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Titlebook: Combinatorial Optimization; Third International Pierre Fouilhoux,Luis Eduardo Neves Gouveia,Vangel Conference proceedings 2014 Springer In

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A Hybrid Heuristic Approach Based on a Quadratic Knapsack Formulation for the Max-Mean Dispersion Pa mixed integer non linear solver and a local branching procedure is developed. Computational results, performed on literature instances, show that the proposed procedure outperforms the state-of-the-art approaches.
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The Dominating Set Polytope via Facility Location,imension. It is known from [.] that for any integer ., there exists a facet defining inequality having coefficients in .. We also show a decomposition theorem by means of 1-sums. Again this decomposition is much simpler with the extended formulation than with the node-variables formulation given in [.].
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A Constraint Generation Approach for the Two-Machine Flow Shop Problem with Jobs Selection, nearly all potential critical paths are relaxed and then only the ones violated by the relaxed solution are sequentially reinstated. This approach yields a new solution algorithm capable of solving problems with up to . jobs or more.
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Rectilinear Shortest Path and Rectilinear Minimum Spanning Tree with Neighborhoods,m spanning tree is minimum among all graphs .. We study these problems in the . metric, and show that the shortest path problem with neighborhoods is solvable in polynomial time, whereas the minimum spanning tree problem with neighborhoods is .-hard, even if the neighborhood regions are segments.
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https://doi.org/10.1007/978-3-319-01104-2 addressed by any black box-solver. An experimental evaluation shows that our approach clearly outperforms other methods for mean-variance optimization when applied to robust shortest path problems and to risk-averse capital budgeting problems arising in portfolio optimization.
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https://doi.org/10.1007/978-3-319-01104-2es a configuration based integer programming formulation for a randomized rounding procedure. In order to turn the rounded solution into a feasible solution, we define appropriate estimators that linearize the convex costs.
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