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Solving the quadratic assignment problem by means of general purpose mixed integer linear programming solvers

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  • Huizhen Zhang
  • Cesar Beltran-Royo
  • Liang Ma

Abstract

The Quadratic Assignment Problem (QAP) can be solved by linearization, where one formulates the QAP as a mixed integer linear programming (MILP) problem. On the one hand, most of these linearizations are tight, but rarely exploited within a reasonable computing time because of their size. On the other hand, Kaufman and Broeckx formulation (Eur. J. Oper. Res. 2(3):204–211, 1978 ) is the smallest of these linearizations, but very weak. In this paper, we analyze how the Kaufman and Broeckx formulation can be tightened to obtain better QAP-MILP formulations. As shown in our numerical experiments, these tightened formulations remain small but computationally effective to solve the QAP by means of general purpose MILP solvers. Copyright Springer Science+Business Media, LLC 2013

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  • Huizhen Zhang & Cesar Beltran-Royo & Liang Ma, 2013. "Solving the quadratic assignment problem by means of general purpose mixed integer linear programming solvers," Annals of Operations Research, Springer, vol. 207(1), pages 261-278, August.
    • Handle: RePEc:spr:annopr:v:207:y:2013:i:1:p:261-278:10.1007/s10479-012-1079-4
    DOI: 10.1007/s10479-012-1079-4
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    References listed on IDEAS

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    1. Loiola, Eliane Maria & de Abreu, Nair Maria Maia & Boaventura-Netto, Paulo Oswaldo & Hahn, Peter & Querido, Tania, 2007. "A survey for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 176(2), pages 657-690, January.
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    6. Adams, Warren P. & Guignard, Monique & Hahn, Peter M. & Hightower, William L., 2007. "A level-2 reformulation-linearization technique bound for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 180(3), pages 983-996, August.
    7. Kaufman, L. & Broeckx, F., 1978. "An algorithm for the quadratic assignment problem using Bender's decomposition," European Journal of Operational Research, Elsevier, vol. 2(3), pages 207-211, May.
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    Cited by:

    1. Mohammad Javad Feizollahi & Igor Averbakh, 2014. "The Robust (Minmax Regret) Quadratic Assignment Problem with Interval Flows," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 321-335, May.
    2. Zakir Hussain Ahmed, 2016. "Experimental analysis of crossover and mutation operators on the quadratic assignment problem," Annals of Operations Research, Springer, vol. 247(2), pages 833-851, December.
    3. Feizollahi, Mohammad Javad & Feyzollahi, Hadi, 2015. "Robust quadratic assignment problem with budgeted uncertain flows," Operations Research Perspectives, Elsevier, vol. 2(C), pages 114-123.
    4. Silva, Allyson & Coelho, Leandro C. & Darvish, Maryam, 2021. "Quadratic assignment problem variants: A survey and an effective parallel memetic iterated tabu search," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1066-1084.
    5. Prabhjot Kaur & Kalpana Dahiya & Vanita Verma, 2021. "Time-cost trade-off analysis of a priority based assignment problem," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 448-482, June.
    6. Fatma Zohra Ouaïl & Mohamed El-Amine Chergui, 2018. "A branch-and-cut technique to solve multiobjective integer quadratic programming problems," Annals of Operations Research, Springer, vol. 267(1), pages 431-446, August.

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