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Three-dimensional container loading using a cooperative co-evolutionary genetic algorithm
Author(s)
Pimpawat, Chaiwat
Chaiyaratana, Nachol
Date Issued
August 1, 2004
Type
Article
Abstract
This paper presents the use of a cooperative co-evolutionary genetic algorithm (CCGA) in conjunction with a heuristic rule for solving a 3D container loading or bin packing problem. Unlike previous works, which concentrate on using either a heuristic rule or an optimization technique to find an optimal sequence of packages which must be loaded into the containers, the proposed heuristic rule is used to partition the entire loading sequence into a number of shorter sequences. Each partitioned sequence is then represented by a species member in the CCGA search. The simulation results indicate that the use of the heuristic rule and the CCGA is highly efficient in terms of the compactness of packages in comparison to the results given by a standard genetic algorithm search. In addition, this helps to confirm that the CCGA is also suitable for use in a sequence-based optimization problem.
Citation
Applied Artificial Intelligence, 18(7), 581-601, 2004