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Rounding and Propagation Heuristics for Mixed Integer Programming

In: Operations Research Proceedings 2011

Author

Listed:
  • Tobias Achterberg

    (IBM Deutschland)

  • Timo Berthold

    (Zuse Institute Berlin)

  • Gregor Hendel

    (Zuse Institute Berlin)

Abstract

Primal heuristics are an important component of state-of-the-art codes for mixed integer programming. In this paper, we focus on primal heuristics that only employ computationally inexpensive procedures such as rounding and logical deductions (propagation). We give an overview of eight different approaches. To assess the impact of these primal heuristics on the ability to find feasible solutions, in particular early during search, we introduce a new performance measure, the primal integral. Computational experiments evaluate this and other measures on MIPLIB 2010 benchmark instances.

Suggested Citation

  • Tobias Achterberg & Timo Berthold & Gregor Hendel, 2012. "Rounding and Propagation Heuristics for Mixed Integer Programming," Operations Research Proceedings, in: Diethard Klatte & Hans-Jakob Lüthi & Karl Schmedders (ed.), Operations Research Proceedings 2011, edition 127, pages 71-76, Springer.
    • Handle: RePEc:spr:oprchp:978-3-642-29210-1_12
    DOI: 10.1007/978-3-642-29210-1_12
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    Cited by:

    1. Lluís-Miquel Munguía & Shabbir Ahmed & David A. Bader & George L. Nemhauser & Yufen Shao, 2018. "Alternating criteria search: a parallel large neighborhood search algorithm for mixed integer programs," Computational Optimization and Applications, Springer, vol. 69(1), pages 1-24, January.
    2. Schepler, Xavier & Rossi, André & Gurevsky, Evgeny & Dolgui, Alexandre, 2022. "Solving robust bin-packing problems with a branch-and-price approach," European Journal of Operational Research, Elsevier, vol. 297(3), pages 831-843.
    3. Timo Berthold, 2018. "A computational study of primal heuristics inside an MI(NL)P solver," Journal of Global Optimization, Springer, vol. 70(1), pages 189-206, January.
    4. Hussain A. Kharodawala & Ashutosh Mahajan & Jayan Moorkanat, 2022. "Multi-modal supply chain distribution problem," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 747-768, September.
    5. Natashia Boland & Matteo Fischetti & Michele Monaci & Martin Savelsbergh, 2016. "Proximity Benders: a decomposition heuristic for stochastic programs," Journal of Heuristics, Springer, vol. 22(2), pages 181-198, April.

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