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PSO-based and SA-based metaheuristics for bilinear programming problems: an application to the pooling problem

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  • Gökalp Erbeyoğlu

    (Boğaziçi University)

  • Ümit Bilge

    (Boğaziçi University)

Abstract

Bilinear programming problems (BLP) are subsets of nonconvex quadratic programs and can be classified as strongly NP-Hard. The exact methods to solve the BLPs are inefficient for large instances and only a few heuristic methods exist. In this study, we propose two metaheuristic methods, one is based on particle swarm optimization (PSO) and the other is based on simulated annealing (SA). Both of the proposed approaches take advantage of the bilinear structure of the problem. For the PSO-based method, a search variable, which is selected among the variable sets causing bilinearity, is subjected to particle swarm optimization. The SA-based procedure incorporates a variable neighborhood scheme. The pooling problem, which has several application areas in chemical industry and formulated as a BLP, is selected as a test bed to analyze the performances. Extensive experiments are conducted and they indicate the success of the proposed solution methods.

Suggested Citation

  • Gökalp Erbeyoğlu & Ümit Bilge, 2016. "PSO-based and SA-based metaheuristics for bilinear programming problems: an application to the pooling problem," Journal of Heuristics, Springer, vol. 22(2), pages 147-179, April.
    • Handle: RePEc:spr:joheur:v:22:y:2016:i:2:d:10.1007_s10732-015-9304-3
    DOI: 10.1007/s10732-015-9304-3
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    References listed on IDEAS

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    1. David H. Evans, 1963. "Modular Design---A Special Case in Nonlinear Programming," Operations Research, INFORMS, vol. 11(4), pages 637-647, August.
    2. Harvey J. Greenberg, 1995. "Analyzing the Pooling Problem," INFORMS Journal on Computing, INFORMS, vol. 7(2), pages 205-217, May.
    3. Charles Audet & Jack Brimberg & Pierre Hansen & Sébastien Le Digabel & Nenad Mladenovi'{c}, 2004. "Pooling Problem: Alternate Formulations and Solution Methods," Management Science, INFORMS, vol. 50(6), pages 761-776, June.
    4. Jaco Schutte & Albert Groenwold, 2005. "A Study of Global Optimization Using Particle Swarms," Journal of Global Optimization, Springer, vol. 31(1), pages 93-108, January.
    5. Yiqiang Su & Joseph Geunes, 2013. "Multi-period price promotions in a single-supplier, multi-retailer supply chain under asymmetric demand information," Annals of Operations Research, Springer, vol. 211(1), pages 447-472, December.
    6. Christodoulos A. Floudas & Avanish Aggarwal, 1990. "A Decomposition Strategy for Global Optimum Search in the Pooling Problem," INFORMS Journal on Computing, INFORMS, vol. 2(3), pages 225-235, August.
    7. Al-Khayyal, Faiz A., 1992. "Generalized bilinear programming: Part I. Models, applications and linear programming relaxation," European Journal of Operational Research, Elsevier, vol. 60(3), pages 306-314, August.
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