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DINS, a MIP Improvement Heuristic

  • Conference paper
Integer Programming and Combinatorial Optimization (IPCO 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4513))

Abstract

We introduce DISTANCE INDUCED NEIGHBOURHOOD SEARCH (DINS), aMIP improvement heuristic that tries to find improved MIP feasible solutions from a givenMIP feasible solution. DINS is based on a variation of local search that is embedded in an exact MIP solver, namely a branch-and-bound or a branch-and-cut MIP solver. The key idea is to use a distancemetric between the linear programming relaxation optimal solution and the currentMIP feasible solution to define search neighbourhoods at different nodes of the search tree generated by the exact solver. DINS considers each defined search neighbourhood as a new MIP problem and explores it by an exact MIP solver with a certain node limit. On a set of standard benchmark problems, DINS outperforms the MIP improvement heuristics Local Branching due to Fischetti and Lodi and Relaxation Induced Neighbourhood Search due to Danna, Rothberg, and Pape, as well as the generic commercial MIP solver Cplex.

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Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Canada

    Shubhashis Ghosh

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  1. Shubhashis Ghosh
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Matteo Fischetti David P. Williamson

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© 2007 Springer Berlin Heidelberg

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Ghosh, S. (2007). DINS, a MIP Improvement Heuristic. In: Fischetti, M., Williamson, D.P. (eds) Integer Programming and Combinatorial Optimization. IPCO 2007. Lecture Notes in Computer Science, vol 4513. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72792-7_24

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  • DOI: https://doi.org/10.1007/978-3-540-72792-7_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72791-0

  • Online ISBN: 978-3-540-72792-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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