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A parallel multi-population genetic algorithm for a constrained two-dimensional orthogonal packing problem

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Abstract

This paper addresses a constrained two-dimensional (2D), non-guillotine restricted, packing problem, where a fixed set of small rectangles has to be placed into a larger stock rectangle so as to maximize the value of the rectangles packed. The algorithm we propose hybridizes a novel placement procedure with a genetic algorithm based on random keys. We propose also a new fitness function to drive the optimization. The approach is tested on a set of instances taken from the literature and compared with other approaches. The experimental results validate the quality of the solutions and the effectiveness of the proposed algorithm.

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Authors and Affiliations

  1. LIAAD, Faculdade de Economia do Porto, Rua Dr. Roberto Frias, s/n, 4200-464, Porto, Portugal

    José Fernando Gonçalves

  2. Algorithms and Optimization Research Department, AT&T Labs Research, 180 Park Avenue, Room C241, Florham Park, NJ, 07932, USA

    Mauricio G. C. Resende

Authors
  1. José Fernando Gonçalves
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  2. Mauricio G. C. Resende
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Correspondence to Mauricio G. C. Resende.

Additional information

Supported by Fundação para a Ciência e Tecnologia (FCT) project PTDC/GES/72244/2006.

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Gonçalves, J.F., Resende, M.G.C. A parallel multi-population genetic algorithm for a constrained two-dimensional orthogonal packing problem. J Comb Optim 22, 180–201 (2011). https://doi.org/10.1007/s10878-009-9282-1

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