Development of a stochastic optimisation tool for solving the multiple container packing problems.

  1. Lookup NU author(s)
  2. Professor Christian Hicks
  3. Dr Pupong Pongcharoen
Author(s)Thapatsuwan P, Chainate W, Hicks C, Pongcharoen P
Editor(s)Grubbstrom,RW; Hinterhuber,HH;
Publication type Conference Proceedings (inc. Abstract)
Conference NameSixteenth International Working Seminar on Production Economics
Conference LocationCongress Innsbruck, Innsbruck, Austria
Year of Conference2010
Source Publication Date1st-5th March 2010
Number of Volumes4
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Marine logistics has become increasingly important as the amount of global trade has increased. Products are usually packed in various sizes of boxes, which are then arranged into containers before shipping. Shipping companies aim to optimise the use of space when packing heterogeneous boxes into containers. The container packing problem (CPP) aims to optimise the packing of a number of rectangular boxes into a set of containers. The problems may be classified as being homogeneous (identical boxes); weakly heterogeneous (a few different sizes); or strongly heterogeneous (many different boxes). The CPP is categorised as an NP hard problem, which means that the amount of computation required to find solutions increases exponentially with problem size. This work describes the development and application of an Artificial Immune System (AIS) and a Genetic Algorithm (GA) for solving the multiple container packing problems (MCPP). The stochastic optimisation tool was written in Microsoft Visual basic. A sequential series of experiments was designed to identify the best parameter configuration of the algorithms for solving MCPP problems. The work optimised the packing a standard marine container (8ft x 8ft x 20ft) for a strongly heterogeneous problem. The experimental results were analysed using the general linear model form of analysis of variance to identify the appropriate parameter configurations of the algorithms. It was found that each algorithm’s parameters were statistically significant with a 95% confidence interval. The best configurations were then used in the sequential experiment aiming to compare the performance of both algorithms for solving twelve heterogeneous MCPP problems. It was found that the best-so-far solutions obtained from the AIS were marginally lower than those produced by the GA for all problem sizes but taken longer computational time.