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Strong formulations for the pooling problem

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Abstract

The pooling problem is a well-studied global optimization problem with applications in oil refining and petrochemical industry. Despite the strong NP-hardness of the problem, which is proved formally in this paper, most instances from the literature have recently been solved efficiently by use of strong formulations. The main contribution from this paper is a new formulation that proves to be stronger than other formulations based on proportion variables. Moreover, we propose a promising branching strategy for the new formulation and provide computational experiments confirming the strength of the new formulation and the effectiveness of the branching strategy.

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References

  • Adhya N., Tawarmalani M., Sahinidis N.V.: 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 

  • Almutairi H., Elhedhli S.: A new Lagrangian approach to the pooling problem. J. Glob. Optim. 45(2), 237–257 (2009)

    Article  Google Scholar 

  • 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 

  • Audet C., Hansen P., Jaumard B., Savard G.: A branch and cut algorithm for nonconvex quadratically constrained quadratic programming. Math. Program. 87(1), 131–152 (2000)

    Google Scholar 

  • Baker T.E., Lasdon L.S.: Successive linear programming at Exxon. Manag. Sci. 31(3), 264–274 (1985)

    Article  Google Scholar 

  • Ben-Tal A., Eiger G., Gershovitz V.: Global minimization by reducing the duality gap. Math. Program. 63(1–3), 193–212 (1994)

    Article  Google Scholar 

  • Floudas C.A., Aggarwal A.: A decomposition strategy for global optimum search in the pooling problem. Oper. Res. J. Comput. 2(3), 225–235 (1990)

    Google Scholar 

  • Floudas C.A., Visweswaran V.: A global optimization algorithm (GOP) for certain classes of nonconvex NLPs–I. Theory. Comput. Chem. Eng. 14(12), 1397–1417 (1990)

    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 

  • Garey M.R., Johnson D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, New York (1979)

    Google Scholar 

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

    Article  Google Scholar 

  • Griffith R.E., Stewart R.A.: A nonlinear programming technique for the optimization of continuous processing systems. Manag. Sci. 7, 379–392 (1961)

    Article  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 

  • Karuppiah R., Grossmann I.E.: Global optimization for the synthesis of integrated water systems in chemical processes. Comput. Chem. Eng. 30(4), 150–173 (2006)

    Article  Google Scholar 

  • Liberti L., Pantelides C.C.: An exact reformulation algorithm for large nonconvex NLPs involving bilinear terms. J. Glob. Optim. 36(2), 161–189 (2006)

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

    Article  Google Scholar 

  • Palacios-Gomez F. Lasdon L., Lasdon F., Engquist M.: Nonlinear optimization by successive linear programming. Manag. Sci. 28(10), 1106–1120 (1982)

    Article  Google Scholar 

  • Quesada I., Grossmann I.E.: Global optimization of bilinear process networks with multicomponent flows. Comput. Chem. Eng. 19(12), 1219–1242 (1995)

    Article  Google Scholar 

  • Ryoo H.S., Sahinidis N.V.: Global optimization of nonconvex NLPs and MINLPs with applications in process design. Comput. Chem. Eng. 19(5), 551–566 (1995)

    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 

  • Sarker R.A., Gunn E.A.: A simple SLP algorithm for solving a class of nonlinear programs. Eur. J. Oper. Res. 101(1), 140–154 (1997)

    Article  Google Scholar 

  • Shectman J.P., Sahinidis N.V.: A finite algorithm for global minimization of separable concave programs. J. Glob. Optim. 12(1), 1–35 (1998)

    Article  Google Scholar 

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

  • Visweswaran V., Floudas C.A.: A global optimization algorithm (GOP) for certain classes of nonconvex NLPs–II. Application of theory and test problems. Comput. Chem. Eng. 14(12), 1419–1434 (1990)

    Article  Google Scholar 

  • Zhang J.H., Kim N.H., Lasdon L.: An improved successive linear-programming algorithm. Manag. Sci. 31(10), 1312–1331 (1985)

    Article  Google Scholar 

Download references

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Correspondence to Dag Haugland.

Additional information

This research was sponsored by the Norwegian Research Council, Gassco, and Statoil under contract 175967/S30.

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Alfaki, M., Haugland, D. Strong formulations for the pooling problem. J Glob Optim 56, 897–916 (2013). https://doi.org/10.1007/s10898-012-9875-6

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  • DOI: https://doi.org/10.1007/s10898-012-9875-6

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