Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

A multi-commodity flow formulation for the generalized pooling problem

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

The pooling problem is an extension of the minimum cost network flow problem where the composition of the flow depends on the sources from which it originates. At each source, the composition is known. In all other nodes, the proportion of any component is given as a weighted average of its proportions in entering flow streams. The weights in this average are simply the arc flow. At the terminals of the network, there are bounds on the relative content of the various components. Such problems have strong relevance in e.g. planning models for oil refining, and in gas transportation models with quality constraints at the reception side. Although the pooling problem has bilinear constraints, much progress in solving a class of instances to global optimality has recently been made. Most of the approaches are however restricted to networks where all directed paths have length at most three, which means that there is no connection between pools. In this work, we generalize one of the most successful formulations of the pooling problem, and propose a multi-commodity flow formulation that makes no assumptions on the network topology. We prove that our formulation has stronger linear relaxation than previously suggested formulations, and demonstrate experimentally that it enables faster computation of the global optimum.

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.

Similar content being viewed by others

References

  • Adhya N., Sahinidis N.V., Tawarmalani M.: A Lagrangian approach to the pooling problem. Ind. Eng. Chem. Res. 38(5), 1956–1972 (1999)

    Article  Google Scholar 

  • Al-Khayyal F.A., Falk J.E.: Jointly constrained biconvex programming. Math. Oper. Res. 8(2), 273–286 (1983)

    Article  Google Scholar 

  • Alfaki, M., Haugland, D.: Strong formulations for the pooling problem. J. Glob. Optim. (The current issue). doi:10.1007/s10898-012-9875-6 (2012)

  • Audet C., Brimberg J., Hansen P., Le Digabel S., Mladenović N.: Pooling problem: alternate formulations and solution methods. Manag. Sci. 50(6), 761–776 (2004)

    Article  Google Scholar 

  • Ben-Tal A., Eiger G., Gershovitz V.: Global minimization by reducing the duality gap. Math. Program. 63(1), 193–212 (1994)

    Article  Google Scholar 

  • Foulds L.R., Haugland D., Jörnsten K.: A bilinear approach to the pooling problem. Optimization 24(1), 165–180 (1992)

    Article  Google Scholar 

  • Gounaris C.E., Misener R., Floudas C.A.: Computational comparison of piecewise linear relaxation for pooling problems. Ind. Eng. Chem. Res. 48(12), 5742–5766 (2009)

    Article  Google Scholar 

  • Guisewite G.M.: Network problems. In: Horst, R., Pardalos, P.M. (eds.) Handbook of Global Optimization, pp. 609–648. Kluwer, Dordrecht (1995)

    Google Scholar 

  • Haverly C.A.: Studies of the behavior of recursion for the pooling problem. ACM SIGMAP Bull. 25, 19–28 (1978)

    Article  Google Scholar 

  • Haverly C.A.: Behavior of recursion model-more studies. ACM SIGMAP Bull. 26, 22–28 (1979)

    Article  Google Scholar 

  • Horst R., Pardalos P.M., Thoai N.V.: Introduction to Global Optimization. Kluwer, Dordrecht, The Netherlands (2000)

    Google Scholar 

  • Karuppiah R., Grossmann I.E.: Global optimization for the synthesis of integrated water systems in chemical processes. J. Comput. Chem. Eng. 30(4), 650–673 (2006)

    Article  Google Scholar 

  • Li X., Armagan E., Tomasgard A., Barton P.I.: Stochastic pooling problem for natural gas production network design and operation under uncertainty. AIChE J. 57(8), 2120–2135 (2011)

    Article  Google Scholar 

  • Main R.A.: Large recursion models: practical aspects of recursion techniques. In: Ciriani, T., Leachman, R. (eds.) Optimization in Industry: Mathematical Programming and Modeling Techniques in Practice, pp. 241–249. Wiley, New York (1993)

    Google Scholar 

  • McCormick G.P.: Computability of global solutions to factorable nonconvex programs: part I - convex underestimating problems. Math. Program. 10(1), 147–175 (1976)

    Article  Google Scholar 

  • Meyer C.A., Floudas C.A.: Global optimization of a combinatorially complex generalized pooling problem. AIChE J. 52(3), 1027–1037 (2006)

    Article  Google Scholar 

  • Misener R., Floudas C.A.: Advances for the pooling problem: modeling, global optimization, and computational studies. Appl. Comput. Math. 8(1), 3–22 (2009)

    Google Scholar 

  • Misener R., Floudas C.A.: Global optimization of large-scale generalized pooling problems: quadratically constrained MINLP models. Ind. Eng. Chem. Res. 49(11), 5424–5438 (2010)

    Article  Google Scholar 

  • Misener R., Gounaris C.E., Floudas C.A.: Mathematical modeling and global optimization of large-scale extended pooling problems with the (EPA) complex emissions constraints. J. Comput. Chem. Eng. 34, 1432–1456 (2010)

    Article  Google Scholar 

  • Sahinidis N.V.: BARON: a general purpose global optimization software package. J. Glob. Optim. 8(2), 201–205 (1996)

    Article  Google Scholar 

  • Sahinidis N.V., Tawarmalani M.: Accelerating branch-and-bound through a modeling language construct for relaxation-specific constraints. J. Glob. Optim. 32(2), 259–280 (2005)

    Article  Google Scholar 

  • Sherali H.D.: Tight relaxations for nonconvex optimization problems using the reformulation-linearization/convexification technique (RLT). In: Pardalos, P.M., Romeijn, E. (eds.) Handbook of Global Optimization, vol. 2, pp. 1–164. Kluwer, Dordrecht, The Netherlands (2002)

    Chapter  Google Scholar 

  • Sherali H.D., Adams W.P.: A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems. Kluwer, Dordrecht, The Netherlands (1999)

    Book  Google Scholar 

  • Sherali H.D., Adams W.P., Driscoll P.J.: Exploiting special structures in constructing a hierarchy of relaxations for 0–1 mixed integer problems. Oper. Res. 46(3), 396–405 (1998)

    Article  Google Scholar 

  • Tawarmalani M., Sahinidis N.V.: Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications. Kluwer, Dordrecht, The Netherlands (2002)

    Google Scholar 

  • Wicaksono D.S., Karimi I.A.: Piecewise MILP under- and overestimators for global optimization of bilinear programs. AIChE J. 54(4), 991–1008 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dag Haugland.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Alfaki, M., Haugland, D. A multi-commodity flow formulation for the generalized pooling problem. J Glob Optim 56, 917–937 (2013). https://doi.org/10.1007/s10898-012-9890-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-012-9890-7

Keywords

Navigation