Skip to main content
Log in

Complexity Analysis and Algorithm Design of Pooling Problem

  • Published:
Journal of the Operations Research Society of China Aims and scope Submit manuscript

Abstract

The pooling problem, also called the blending problem, is fundamental in production planning of petroleum. It can be formulated as an optimization problem similar with the minimum-cost flow problem. However, Alfaki and Haugland (J Glob Optim 56:897–916, 2013) proved the strong NP-hardness of the pooling problem in general case. They also pointed out that it was an open problem to determine the computational complexity of the pooling problem with a fixed number of qualities. In this paper, we prove that the pooling problem is still strongly NP-hard even with only one quality. This means the quality is an essential difference between minimum-cost flow problem and the pooling problem. For solving large-scale pooling problems in real applications, we adopt the non-monotone strategy to improve the traditional successive linear programming method. Global convergence of the algorithm is established. The numerical experiments show that the non-monotone strategy is effective to push the algorithm to explore the global minimizer or provide a good local minimizer. Our results for real problems from factories show that the proposed algorithm is competitive to the one embedded in the famous commercial software Aspen PIMS.

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

Similar content being viewed by others

References

  1. Xia, X.-M.: The research on Beijing Yanshan chemical corporation’s PIMS optimizing resource. Master thesis, Beijing University of Chemical Technology, Beijing (2007)

  2. Alfaki, M., Haugland, D.: Strong formulations for the pooling problem. J. Glob. Optim. 56, 897–916 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  3. Haverly, C.A.: Studies of the behavior of recursion for the pooling problem. Acm Sigmap Bull. 25, 19–28 (1978)

    Article  Google Scholar 

  4. Haverly, C.A.: Behavior of recursion model: more studies. Acm Sigmap Bull. 26, 22–28 (1979)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  6. Lasdon, L.S., Waren, A.D., Sarkar, S., et al.: Solving the pooling problem using generalized reduced gradient and successive linear programming algorithms. Acm Sigmap Bull. 27, 9–15 (1979)

    Article  Google Scholar 

  7. Diao, R. (ed.): Attacking some optimization problems in petrochemistry. Electricity and other fields. Ph.D. thesis, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing (2014)

  8. Visweswaran, V., Floudas, C.A.: New formulations and branching strategies for the GOP algorithm. In: Zhang, J., Kim, N.-H., Lasdon, L.S. (eds.) Global Optimization in Engineering Design, pp. 75–109. Springer, Berlin (1996)

  9. Androulakis, I.P., Visweswaran, V., Floudas, C.A.: Distributed decomposition-based approaches in global optimization. In: State of the Art in Global Optimization, pp. 285–301. Springer, US (1996)

  10. Foulds, L.R., Haugland, D., Jörnsten, K.: A bilinear approach to the pooling problem. Optimization 24, 165180 (1992)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  13. Gupte, A.: Mixed integer bilinear programming with applications to the pooling problem. Ph.D. thesis, Georgia Institute of Technology, Atlanta (2012)

  14. Pham, V., Laird, C., El-Halwagi, M.: Convex hull discretization approach to the global optimization of pooling problems. Ind. Eng. Chem. Res. 48, 1973–1979 (2009)

    Article  Google Scholar 

  15. Tawarmalani, M., Sahinidis, N.V.: Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications. Springer, Berlin (2002)

    Book  MATH  Google Scholar 

  16. Lee, S., Grossmann, I.E.: Global optimization of nonlinear generalized disjunctive programming with bilinear equality constraints: applications to process networks. Comput. Chem. Eng. 27, 1557–1575 (2003)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  19. Realff, M., Ahmed, S., Inacio, H., et al.: Heuristics and upper bounds for a pooling problem with cubic constraints. In: Foundations of Computer-Aided Process Operations. Savannah (2012)

  20. D’Ambrosio, C., Linderoth, J., Luedtke, J.: Valid inequalities for the pooling problem with binary variables. In: Diao, R. (ed.) Integer Programming and Combinatoral Optimization, pp. 117–129. Springer, Berlin (2011)

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Visweswaran, V. (ed.): MINLP: applications in blending and pooling problems. In: Encyclopedia of Optimization, pp. 2114–2121. Springer, Berlin (2009)

  24. Zhang, J.-Z., Kim, N.-H., Lasdon, L.(eds.): State of the Art in Global Optimization. In: An improved successive linear programming algorithm. Manag. Sci. 31, 1312–1331 (1985)

  25. Forrest, J.: COIN-OR linear programming solver. https://projects.coin-or.org/Clp

  26. Audet, C., Brimberg, J., Hansen, P., et al.: Pooling problem: alternate formulations and solution methods. Manag. Sci. 50, 761–776 (2004)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kai Fu.

Additional information

This research is supported by the National Natural Science Foundation of China (Nos. 11631013, 71331001, 11331012) and the National 973 Program of China (No. 2015CB856002).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dai, YH., Diao, R. & Fu, K. Complexity Analysis and Algorithm Design of Pooling Problem. J. Oper. Res. Soc. China 6, 249–266 (2018). https://doi.org/10.1007/s40305-018-0193-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40305-018-0193-7

Keywords

Mathematics Subject Classification

Navigation