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Randomized Algorithm with Tabu Search for Multi-Objective Optimization of Large Containership Stowage Plans

  • Fan Liu
  • Malcolm Yoke Hean Low
  • Wen Jing Hsu
  • Shell Ying Huang
  • Min Zeng
  • Cho Aye Win
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6971)

Abstract

This paper describes a randomized algorithm with Tabu Search (TS) for multi-objective optimization of large containership stowage plans. The algorithm applies a randomized block-based container allocation approach to obtain a Pareto set of stowage plans from a set of initial solutions in the first stage, and uses TS to carry out multi-objective optimization on the Pareto set of stowage plans in the second stage. Finally, a group of non-dominated solutions is generated based on objectives such as the number of re-handles, the completion time of the longest crane, the number of stacks that exceed the weight limit, the number of idle slots, horizontal moment difference and cross moment difference. Experimental results based on real data show that the proposed algorithm is able to obtain better stowage plans compared with human planners.

Keywords

Completion Time Tabu Search Tabu List Weight Limit Quay Crane 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fan Liu
    • 1
  • Malcolm Yoke Hean Low
    • 1
  • Wen Jing Hsu
    • 1
  • Shell Ying Huang
    • 1
  • Min Zeng
    • 1
  • Cho Aye Win
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

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