Alternating criteria search: a parallel large neighborhood search algorithm for mixed integer programs

Lluís-Miquel Munguía (), Shabbir Ahmed, David A. Bader, George L. Nemhauser and Yufen Shao
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Lluís-Miquel Munguía: Georgia Institute of Technology
Shabbir Ahmed: Georgia Institute of Technology
David A. Bader: Georgia Institute of Technology
George L. Nemhauser: Georgia Institute of Technology
Yufen Shao: ExxonMobil Upstream Research Company

Computational Optimization and Applications, 2018, vol. 69, issue 1, No 1, 24 pages

Abstract: Abstract We present a parallel large neighborhood search framework for finding high quality primal solutions for general mixed-integer programs (MIPs). The approach simultaneously solves a large number of sub-MIPs with the dual objective of reducing infeasibility and optimizing with respect to the original objective. Both goals are achieved by solving restricted versions of two auxiliary MIPs, where subsets of the variables are fixed. In contrast to prior approaches, ours does not require a feasible starting solution. We leverage parallelism to perform multiple searches simultaneously, with the objective of increasing the effectiveness of our heuristic. We computationally compare the proposed framework with a state-of-the-art MIP solver in terms of solution quality, scalability, reproducibility, and parallel efficiency. Results show the efficacy of our approach in finding high quality solutions quickly both as a standalone primal heuristic and when used in conjunction with an exact algorithm.

Keywords: MIPs; Parallel algorithms; Primal heuristics; LNS (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10589-017-9934-5

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