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Bilinear Assignment Problem: Large Neighborhoods and Experimental Analysis of Algorithms

Author

Listed:
  • Vladyslav Sokol

    (School of Computing Science, Simon Fraser University, Surrey, British Columbia V3T 0A3, Canada)

  • Ante Ćustić

    (Department of Mathematics, Simon Fraser University, Surrey, British Columbia V3T 0A3, Canada)

  • Abraham P. Punnen

    (School of Management, Northwestern Polytechnical University, 710072 Xi’an, China; Department of Mathematics, Simon Fraser University, Surrey, British Columbia V3T 0A3, Canada)

  • Binay Bhattacharya

    (School of Computing Science, Simon Fraser University, Surrey, British Columbia V3T 0A3, Canada)

Abstract

The bilinear assignment problem (BAP) is a generalization of the well-known quadratic assignment problem . In this paper, we study the problem from the computational analysis point of view. Several classes of neighborhood structures are introduced for the problem along with some theoretical analysis. These neighborhoods are then explored within a local search and variable neighborhood search frameworks with multistart to generate robust heuristic algorithms. In addition, we present several very fast construction heuristics. Our systematic experimental analysis disclosed some interesting properties of the BAP, different from those of comparable models. We have also introduced benchmark test instances that can be used for future experiments on exact and heuristic algorithms for the problem.

Suggested Citation

  • Vladyslav Sokol & Ante Ćustić & Abraham P. Punnen & Binay Bhattacharya, 2020. "Bilinear Assignment Problem: Large Neighborhoods and Experimental Analysis of Algorithms," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 730-746, July.
    • Handle: RePEc:inm:orijoc:v:32:y:3:i:2020:p:730-746
    DOI: 10.1287/ijoc.2019.0893
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    References listed on IDEAS

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    6. Torki, Abdolhamid & Yajima, Yatsutoshi & Enkawa, Takao, 1996. "A low-rank bilinear programming approach for sub-optimal solution of the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 94(2), pages 384-391, October.
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