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Valid Inequalities for the Pooling Problem with Binary Variables

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Integer Programming and Combinatoral Optimization (IPCO 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6655))

Abstract

The pooling problem consists of finding the optimal quantity of final products to obtain by blending different compositions of raw materials in pools. Bilinear terms are required to model the quality of products in the pools, making the pooling problem a non-convex continuous optimization problem. In this paper we study a generalization of the standard pooling problem where binary variables are used to model fixed costs associated with using a raw material in a pool. We derive four classes of strong valid inequalities for the problem and demonstrate that the inequalities dominate classic flow cover inequalities. The inequalities can be separated in polynomial time. Computational results are reported that demonstrate the utility of the inequalities when used in a global optimization solver.

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References

  1. DeWitt, C., Lasdon, L., Waren, A., Brenner, D., Melham, S.: OMEGA: An improved gasoline blending system for Texaco. Interfaces 19, 85–101 (1989)

    Article  Google Scholar 

  2. Rigby, B., Lasdon, L., Waren, A.: The evolution of Texaco’s blending systems: From OMEGA to StarBlend. Interfaces 25, 64–83 (1995)

    Article  Google Scholar 

  3. Bagajewicz, M.: A review of recent design procedures for water networks in refineries and process plants. Computers & Chemical Engineering 24, 2093–2113 (2000)

    Article  Google Scholar 

  4. Kallrath, J.: Mixed integer optimization in the chemical process industry: Experience, potential and future perspectives. Chemical Engineering Research and Design 78, 809–822 (2000)

    Article  Google Scholar 

  5. Misener, R., Floudas, C.: Advances for the pooling problem: Modeling, global optimization, & computational studies. Applied and Computational Mathematics 8, 3–22 (2009)

    MathSciNet  MATH  Google Scholar 

  6. Lee, H., Pinto, J., Grossmann, I., Park, S.: Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management. Industrial & Engineering Chemistry Research 35, 1630–1641 (1996)

    Article  Google Scholar 

  7. Shah, N.: Mathematical programming techniques for crude oil scheduling. Computers & Chemical Engineering 20, S1227–S1232 (1996)

    Article  Google Scholar 

  8. Padberg, M., Van Roy, T.J., Wolsey, L.: Valid linear inequalities for fixed charge problems. Operations Research 33, 842–861 (1985)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  10. Karuppiah, R., Grossmann, I.: Global optimization for the synthesis of integrated water systems in chemical processes. Computers & Chemical Engineering 30, 650–673 (2006)

    Article  Google Scholar 

  11. Papageorgiou, D.J., Toriello, A., Nemhauser, G.L., Savelsbergh, M.: Fixed-charge transportation with product blending (2010) (unpublished manuscript)

    Google Scholar 

  12. Quesada, I., Grossmann, I.: Global optimization of bilinear process networks with multicomponent flows. Computers and Chemical Engineering 19, 1219–1242 (1995)

    Article  Google Scholar 

  13. Tawarmalani, M., Sahinidis, N.: Convexification and global optimization in continuous and mixed-integer nonlinear programming: Theory, Algorithms, Software, and Applications. Kluwer Academic Publishers, Dordrecht (2002)

    Book  MATH  Google Scholar 

  14. McCormick, G.: Computability of global solutions to factorable nonconvex programs: Part 1 - convex underestimating problems. Mathematical Programming 10, 147–175 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  15. Wolsey, L., Nemhauser, G.: Integer and Combinatorial Optimization. Wiley, New York (1988)

    MATH  Google Scholar 

  16. Sahinidis, N., Tawarmalani, M.: Accelerating branch-and-bound through a modeling language construct for relaxation-specific constraints. Journal of Global Optimization 32, 259–280 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  17. Dolan, E., Moré, J.: Benchmarking optimization software with performance profiles. Mathematical Programming 91, 201–213 (2002)

    Article  MathSciNet  MATH  Google Scholar 

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D’Ambrosio, C., Linderoth, J., Luedtke, J. (2011). Valid Inequalities for the Pooling Problem with Binary Variables. In: Günlük, O., Woeginger, G.J. (eds) Integer Programming and Combinatoral Optimization. IPCO 2011. Lecture Notes in Computer Science, vol 6655. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20807-2_10

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  • DOI: https://doi.org/10.1007/978-3-642-20807-2_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20806-5

  • Online ISBN: 978-3-642-20807-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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