Skip to main content
Log in

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

  • Published:
Computational Optimization and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Achterberg, T.: Constraint integer programming. Ph.D. thesis (2007)

  2. Achterberg, T., Berthold, T.: Improving the feasibility pump. Discrete Optim. 4(1), 77–86 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  3. Achterberg, T., Berthold, T., Hendel, G.: Rounding and propagation heuristics for mixed integer programming. In: Operations Research Proceedings, 2011, pp. 71–76. Springer (2012)

  4. Bader, D.A., Hart, W.E., Phillips, C.A.: Parallel algorithm design for branch and bound. In: Tutorials on Emerging Methodologies and Applications in Operations Research, Chap. 5. Springer (2005)

  5. Baena, D., Castro, J.: Using the analytic center in the feasibility pump. Oper. Res. Lett. 39(5), 310–317 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  6. Balas, E., Ceria, S., Dawande, M., Margot, F., Pataki, G.: Octane: a new heuristic for pure 0–1 programs. Oper. Res. 49(2), 207–225 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  7. Balas, E., Schmieta, S., Wallace, C.: Pivot and shift-a mixed integer programming heuristic. Discrete Optim. 1(1), 3–12 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  8. Bertacco, L., Fischetti, M., Lodi, A.: A feasibility pump heuristic for general mixed-integer problems. Discrete Optim. 4(1), 63–76 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  9. Berthold, T.: Primal heuristics for mixed integer programs. Diploma Thesis, Technische Universitat Berlin (2006)

  10. Berthold, T.: Measuring the impact of primal heuristics. Oper. Res. Lett. 41(6), 611–614 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  11. Berthold, T.: Rens. Math. Program. Comput. 6(1), 33–54 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  12. Berthold, T., Hendel, G.: Shift-and-propagate. J. Heuristics 21(1), 73–106 (2015)

    Article  MATH  Google Scholar 

  13. Bixby, R., Gu, Z., Rothberg, E.: Gurobi optimization (2010). http://www.gurobi.com/

  14. Boland, N.L., Eberhard, A.C., Engineer, F.G., Fischetti, M., Savelsbergh, M.W.P., Tsoukalas, A.: Boosting the feasibility pump. Math. Program. Comput. 6(3), 255–279 (2014). doi:10.1007/s12532-014-0068-9

  15. Carvajal, R., Ahmed, S., Nemhauser, G., Furman, K., Goel, V., Shao, Y.: Using diversification, communication and parallelism to solve mixed-integer linear programs. Oper. Res. Lett. 42(2), 186–189 (2014)

    Article  MathSciNet  Google Scholar 

  16. Corporation, I.B.M.: Ibm cplex optimizer (2015). http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/

  17. Danna, E.: Performance variability in mixed integer programming. In: Presentation at Workshop on Mixed Integer Programming. http://coral.ie.lehigh.edu/mip-2008/talks/danna.pdf (2008)

  18. Danna, E., Rothberg, E., Le Pape, C.: Exploring relaxation induced neighborhoods to improve mip solutions. Math. Program. 102(1), 71–90 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  19. Fischetti, M., Glover, F., Lodi, A.: The feasibility pump. Math. Program. 104(1), 91–104 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  20. Fischetti, M., Lodi, A.: Local branching. Math. Program. 98(1–3), 23–47 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  21. Fischetti, M., Lodi, A.: Repairing mip infeasibility through local branching. Comput. Oper. Res. 35(5), 1436–1445 (2008). (Part Special Issue: Algorithms and Computational Methods in Feasibility and Infeasibility)

    Article  MathSciNet  MATH  Google Scholar 

  22. Fischetti, M., Lodi, A.: Heuristics in Mixed Integer Programming. Wiley, London (2010)

    MATH  Google Scholar 

  23. Fischetti, M., Lodi, A., Monaci, M., Salvagnin, D., Tramontani, A.: Improving branch-and-cut performance by random sampling. Math. Program. Comput. 8(1), 113–132 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  24. Fischetti, M., Monaci, M.: Proximity search for 0–1 mixed-integer convex programming. J. Heuristics 20(6), 709–731 (2014)

    Article  MATH  Google Scholar 

  25. Fischetti, M., Salvagnin, D.: Feasibility pump 2.0. Math. Program. Comput. 1(2–3), 201–222 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  26. Gamrath, G., Berthold, T., Heinz, S., Winkler, M.: Structure-based primal heuristics for mixed integer programming, pp. 37–53. Springer, Japan, Tokyo (2016)

  27. Ghosh, S.: Dins, a mip improvement heuristic. Integer Programming and Combinatorial Optimization. Lecture Notes in Computer Science, vol. 4513, pp. 310–323. Springer, Berlin, Heidelberg (2007)

  28. Glover, F., Laguna, M.: General purpose heuristics for integer programming–part i. J. Heuristics 2(4), 343–358 (1997)

    Article  MATH  Google Scholar 

  29. Glover, F., Laguna, M.: General purpose heuristics for integer programming–part ii. J. Heuristics 3(2), 161–179 (1997)

    Article  MATH  Google Scholar 

  30. Glover, F., LøKketangen, A., Woodruff, D.L.: Scatter Search to Generate Diverse MIP Solutions, pp. 299–317. Springer, Boston (2000)

  31. Goel, V., Furman, K.C., Song, J.H., El-Bakry, A.S.: Large neighborhood search for lng inventory routing. J. Heuristics 18(6), 821–848 (2012)

    Article  Google Scholar 

  32. Gropp, W., Lusk, E., Doss, N., Skjellum, A.: A high-performance, portable implementation of the MPI message passing interface standard. Parallel Comput. 22(6), 789–828 (1996)

    Article  MATH  Google Scholar 

  33. Hansen, P., Mladenović, N., Urošević, D.: Variable neighborhood search and local branching. Comput. Oper. Rese. 33(10), 3034–3045 (2006). (Part Special Issue: Constraint Programming)

    Article  MATH  Google Scholar 

  34. Hendel, G.: New rounding and propagation heuristics for mixed integer programming. Bachelor’s thesis, TU Berlin (2011)

  35. Hewitt, M., Nemhauser, G.L., Savelsbergh, M.W.P.: Combining exact and heuristic approaches for the capacitated fixed-charge network flow problem. INFORMS J. Comput. 22(2), 314–325 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  36. Koc, U., Mehrotra, S.: Generation of feasible integer solutions on a massively parallel computer (submitted)

  37. Koch, T., Achterberg, T., Andersen, E., Bastert, O., Berthold, T., Bixby, R.E., Danna, E., Gamrath, G., Gleixner, A.M., Heinz, S., Lodi, A., Mittelmann, H., Ralphs, T., Salvagnin, D., Steffy, D.E., Wolter, K.: MIPLIB 2010. Mathematical Programming Computation 3(2), 103–163 (2011)

    Article  MathSciNet  Google Scholar 

  38. Koch, T., Ralphs, T., Shinano, Y.: Could we use a million cores to solve an integer program? Math. Methods Oper. Res. 76(1), 67–93 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  39. Mercé, C., Fontan, G.: Mip-based heuristics for capacitated lotsizing problems. Int. J. Prod. Econ. 85(1), 97–111 (2003). (Planning and Control of Productive Systems)

    Article  Google Scholar 

  40. Munguía, L.M., Ahmed, S., Bader, D.A., Nemhauser, G.L., Goel, V., Shao, Y.: A parallel local search framework for the fixed-charge multicommodity network flow problem. Comput. Oper. Res. 77, 44–57 (2017)

    Article  MathSciNet  Google Scholar 

  41. Naoum-Sawaya, J.: Recursive central rounding for mixed integer programs. Comput. Oper. Res. 43, 191–200 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  42. Rothberg, E.: An evolutionary algorithm for polishing mixed integer programming solutions. INFORMS J. Comput. 19(4), 534–541 (2007)

    Article  MATH  Google Scholar 

  43. Shao, Y., Furman, K.C., Goel, V., Hoda, S.: A hybrid heuristic strategy for liquefied natural gas inventory routing. Transp. Res. C: Emerg. Technol. 53, 151–171 (2015)

    Article  Google Scholar 

  44. Shinano, Y., Achterberg, T., Berthold, T., Heinz, S., Koch, T.: Parascip: a parallel extension of scip. In: Competence in High Performance Computing, 2010, pp. 135–148. Springer (2012)

  45. Thakur, R., Rabenseifner, R., Gropp, W.: Optimization of collective communication operations in mpich. Int. J. High Perform. Comput. Appl. 19(1), 49–66 (2005)

    Article  Google Scholar 

  46. Wallace, C.: Zi round, a mip rounding heuristic. J. Heuristics 16(5), 715–722 (2010)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

We wish to thank the referees, whose comments led to an improved version of the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lluís-Miquel Munguía.

Additional information

This research has been supported in part by ExxonMobil Upstream Research Company, the National Science Foundation, the Office of Naval Research and the Air Force Office of Scientific Research.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Munguía, LM., Ahmed, S., Bader, D.A. et al. Alternating criteria search: a parallel large neighborhood search algorithm for mixed integer programs. Comput Optim Appl 69, 1–24 (2018). https://doi.org/10.1007/s10589-017-9934-5

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10589-017-9934-5

Keywords

Navigation