Adaptive Kernel Search: A heuristic for solving Mixed Integer linear Programs

G. Guastaroba, M. Savelsbergh and M.G. Speranza

European Journal of Operational Research, 2017, vol. 263, issue 3, 789-804

Abstract: We introduce Adaptive Kernel Search (AKS), a heuristic framework for the solution of (general) Mixed Integer linear Programs (MIPs). AKS extends and enhances Kernel Search, a heuristic framework that has been shown to produce high-quality solutions for a number of specific (combinatorial) optimization problems in a short amount of time. AKS solves a sequence of carefully constructed restricted MIPs (using a commercial MIP solver). The computational effort required to solve the first restricted MIP guides the construction of the subsequent MIPs. The restricted MIPs are constructed around a kernel, which contains the variables that are presumably non-zero in an optimal solution. Computational results, for a set of 137 instances, show that AKS significantly outperforms other state-of-the-art heuristics for solving MIPs. AKS also compares favorably to CPLEX and offers more flexibility to trade-off solution quality and computing time.

Keywords: Mixed integer linear programming; General-purpose heuristic; Kernel Search; Adaptive heuristic (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:263:y:2017:i:3:p:789-804

DOI: 10.1016/j.ejor.2017.06.005

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