Randomized algorithm with tabu search for multi-objective optimization of large containership stowage plans

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. © 2011 Springer-Verlag.