A compromised large-scale neighborhood search heuristic for cargo loading planning

  • Authors:
  • Yanzhi Li;Yi Tao;Fan Wang

  • Affiliations:
  • Department of Management Sciences, City University of Hong Kong, Hong Kong;Department of Management Sciences, City University of Hong Kong, Hong Kong and Department of Management Sciences, Sun Yat-Sen University, P.R. China;Department of Management Sciences, Sun Yat-Sen University, P.R. China

  • Venue:
  • AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
  • Year:
  • 2007

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Abstract

In this work, we propose a compromised large-scale neighborhood, which is embedded in simulated annealing to solve a cargo loading planning problem arising in logistics industry. It is "compromised" because it makes a tradeoff between the extensive backward checking work incurred in traditional subset-disjoint restriction and the possible infeasibility resulting from the relaxing the restriction. Extensive experiments have shown the competitive advantages of the heuristic approach. The proposed neighborhood search method is generally applicable.