Skip to main content

Advertisement

Log in

Convex reformulations for solving a nonlinear network design problem

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

Abstract

We consider a nonlinear nonconvex network design problem that arises, for example, in natural gas or water transmission networks. Given is such a network with active and passive components, that is, valves, compressors, control valves (active) and pipelines (passive), and a desired amount of flow at certain specified entry and exit nodes in the network. The active elements are associated with costs when used. Besides flow conservation constraints in the nodes, the flow must fulfill nonlinear nonconvex pressure loss constraints on the arcs subject to potential values (i.e., pressure levels) in both end nodes of each arc. The problem is to compute a cost minimal setting of the active components and numerical values for the flow and node potentials. We examine different (convex) relaxations for a subproblem of the design problem and benefit from them within a branch-and-bound approach. We compare different approaches based on nonlinear optimization numerically on a set of test instances.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Achterberg, T.: SCIP: solving constraint integer programs. Math. Program. Comput. 1(1), 1–41 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  2. Babonneau, F., Nesterov, Y., Vial, J.-P.: Design and operations of gas transmission networks. Oper. Res. 60(1), 34–47 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  3. Collins, M., Cooper, L., Helgason, R., Kennington, J., LeBlanc, L.: Solving the pipe network analysis problem using optimization techniques. Manag. Sci. 24(7), 747–760 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  4. Conn, A.R., Gould, N.I.M., Toint, P.L.: Trust-Region Methods. SIAM, Philadelphia (2000)

    Book  MATH  Google Scholar 

  5. CPLEX: User’s Manual for CPLEX. IBM Corporation, 12.1 edition, Armonk, USA (2011)

  6. De Wolf, D.: Mathematical properties of formulations of the gas transmission problem. Submitted to RAIRO Oper. Res. (2004). http://www-heb.univ-littoral.fr/dewolf

  7. De Wolf, D., Bakhouya, B.: The gas transmission problem when the merchant and the transport functions are disconnected. Technical Report 01/01, Ieseg, Université catholique de Lille, HEC Ecole de Gestion de l’ULG (2007)

  8. De Wolf, D., Bakhouya, B.: Optimal dimensioning of pipe networks: the new situation when the distribution and the transportation functions are disconnected. Technical Report 07/02, Ieseg, Université catholique de Lille, HEC Ecole de Gestion de l’ULG (2008)

  9. De Wolf, D., Bakhouya, B.: Solving gas transmission problems by taking compressors into account. http://www-heb.univ-littoral.fr/dewolf, September 2008. Submitted to 4OR

  10. De Wolf, D., Smeers, Y.: Optimal dimensioning of pipe networks with application to gas transmission networks. Oper. Res. 44(4), 596–608 (1996)

    Article  MATH  Google Scholar 

  11. De Wolf, D., Smeers, Y.: The gas transmission problem solved by an extension of the simplex algorithm. Manag. Sci. 46(11), 1454–1465 (2000)

    Article  MATH  Google Scholar 

  12. Dembo, R.S., Mulvey, J.M., Zenios, S.A.: Large-scale nonlinear network models and their application. Oper. Res. 37(3), 353–372 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  13. Fügenschuh, A., Homfeld, H., Schülldorf, H., Vigerske, S.: Mixed-integer nonlinear problems in transportation applications. In: Rodrigues, H., et al. (eds.) Proceedings of the 2nd International Conference on Engineering Optimization (CD-ROM) (2010)

  14. Geißler, B., Martin, A., Morsi, A.: LaMaTTO++. Information available at http://www.mso.math.fau.de/edom/projects/lamatto.html, February 2015

  15. Humpola, J., Fügenschuh, A., Koch, T.: A New Class of Valid Inequalities for Nonlinear Network Design Problems. OR Spectrum, online available (2015)

  16. Humpola, J., Fügenschuh, A., Lehmann, T.: A primal heuristic for optimizing the topology of gas networks based on dual information. EURO J. Comput. Optim. 3(1), 53–78 (2015)

    Article  MATH  Google Scholar 

  17. Karush, W.: Minima of functions of several variables with inequalities as side constraints. Master’s thesis (1939)

  18. Korte, B., Vygen, J.: Combinatorial Optimization: Theory and Algorithms. Springer, Berlin (2007)

    Google Scholar 

  19. Kuhn, H.W., Tucker, A.W.: Nonlinear programming. In: Neyman, J. (ed.) Proceedings of the 2nd Berkley Symposium on Mathematical Statistics and Probability, pp. 481–493. University Press, Berkley, California (1951)

  20. Maugis, J.J.: Etude de réseaux de transport et de distribution de fluide. RAIRO Oper. Res. 11(2), 243–248 (1977)

    Google Scholar 

  21. Nemhauser, G.L., Wolsey, L.A.: Integer programming, Chap. 6. In: Nemhauser, G.L., Rinnooy Kan, A.H.G., Todd, M.J. (eds.) Optimization, pp. 447–527. Elsevier, Amsterdam (1989)

    Chapter  Google Scholar 

  22. Oldham, J.: Combinatorial approximation algorithms for generalized flow problems. In: Proceedings of the Tenth Annual ACM-SIAM Symposium on Discrete Algorithms SODA’99, pp. 704–714 (1999)

  23. Pfetsch, M., Fügenschuh, A., Geißler, B., Geißler, N., Gollmer, R., Hiller, B., Humpola, J., Koch, T., Lehmann, T., Martin, A., Morsi, A., Rövekamp, J., Schewe, L., Schmidt, M., Schultz, R., Schwarz, R., Schweiger, J., Stangl, C., Steinbach, M., Vigerske, S., Willert, B.: Validation of nominations in gas network optimization: models, methods, and solutions. Optim. Methods Softw. 30(1), 15–53 (2015)

    Article  MathSciNet  Google Scholar 

  24. Raghunathan, A.U.: Global optimization of nonlinear network design. SIAM J. Optim. 23(1), 268–295 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  25. Sherali, H.D., Smith, E.P.: An optimal replacement-design model for a reliable water distribution network system. In: Coulbeck, Bryan (ed.) Integrated Computer Applications in Water Supply, vol. 1, pp. 61–75. Wiley, New York (1994)

    Google Scholar 

  26. Smith, E.M.B., Pantelides, C.C.: A symbolic reformulation/spatial branch-and-bound algorithm for the global optimization of nonconvex MINLPs. Comput. Chem. Eng. 23, 457–478 (1999)

    Article  Google Scholar 

  27. Tawarmalani, M., Sahinidis, N.V.: Global optimization of mixed-integer nonlinear programs: a theoretical and computational study. Math. Program. 99(3), 563–591 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  28. Tawarmalani, M., Sahinidis, N.V.: A polyhedral branch-and-cut approach to global optimization. Math. Program. 103(2), 225–249 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  29. Vigerske, S.: Decomposition in Multistage Stochastic Programming and a Constraint Integer Programming Approach to Mixed-Integer Nonlinear Programming. PhD thesis, Humboldt-Universität zu Berlin (2012)

  30. Wächter, A., Biegler, L.T.: On the implementation of a primal–dual interior point filter line search algorithm for large-scale nonlinear programming. Math. Program. 106(1), 25–57 (2006)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgments

We are grateful to Open Grid Europe GmbH (OGE, Essen/Germany) for supporting our work. The second coauthor conducted parts of this research under a Konrad-Zuse-Fellowship. We thank two anonymous referees for their various helpful comments on our manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Armin Fügenschuh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Humpola, J., Fügenschuh, A. Convex reformulations for solving a nonlinear network design problem. Comput Optim Appl 62, 717–759 (2015). https://doi.org/10.1007/s10589-015-9756-2

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10589-015-9756-2

Keywords