Hybrid ant colony optimization algorithm for container loading problem

In this study, a Tower Building (TB) heuristic with less complexity, inspired by the stack building heuristic, is proposed to hybridize with an Ant Colony Optimization (ACO) for solving the Container Loading Problem (CLP). This approach is called, the Hybrid Ant Colony Optimization with Tower Buildi...

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Main Author:	Yap, Ching Nei
Format:	Thesis
Language:	English
Published:	2012
Subjects:	Ant algorithms Mathematical optimization Ants - Behavior - Mathematical models
Online Access:	http://psasir.upm.edu.my/id/eprint/31439/1/IPM%202012%204R.pdf
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id	my-upm-ir.31439
record_format	uketd_dc
spelling	my-upm-ir.314392015-02-23T07:22:18Z Hybrid ant colony optimization algorithm for container loading problem 2012-10 Yap, Ching Nei In this study, a Tower Building (TB) heuristic with less complexity, inspired by the stack building heuristic, is proposed to hybridize with an Ant Colony Optimization (ACO) for solving the Container Loading Problem (CLP). This approach is called, the Hybrid Ant Colony Optimization with Tower Building Heuristic (HACO). The aim of the CLP is to pack a subset of given three-dimensional rectangular boxes of different sizes into a three-dimensional rectangular container of fixed dimensions in order to achieve optimal space utilization. The TB heuristic placed the base box on the container floor and packed the boxes on the base box by stacking them one by one until the container is full, whereas other researchers used the stack building heuristic to generate a set of box towers from all of the given boxes then only arranged them into the container. The HACO is applied with its probabilistic decision rule and pheromone feedback, together with the TB heuristic to construct towers of boxes to be arranged into the container in order to find the optimal solution. The pheromone evaporation will reduce the chances of the other ants selecting the same solution and consequently the search will be diversified. Preliminary computational experiments were conducted on a subset of benchmark data sets as to find the appropriate parameters setting for the developed HACO. The proposed algorithm is tested on two standard benchmark data sets to evaluate the performance and to determine the effectiveness of the algorithm. The results in space utilization obtained were comparable with other heuristic and metaheuristic approaches from the literature. It was showed that the proposed HACO algorithm has the capability in solving the CLP. Ant algorithms Mathematical optimization Ants - Behavior - Mathematical models 2012-10 Thesis http://psasir.upm.edu.my/id/eprint/31439/ http://psasir.upm.edu.my/id/eprint/31439/1/IPM%202012%204R.pdf application/pdf en public masters Universiti Putra Malaysia Ant algorithms Mathematical optimization Ants - Behavior - Mathematical models Institute for Mathematical Research
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Ant algorithms Mathematical optimization Ants - Behavior - Mathematical models
spellingShingle	Ant algorithms Mathematical optimization Ants - Behavior - Mathematical models Yap, Ching Nei Hybrid ant colony optimization algorithm for container loading problem
description	In this study, a Tower Building (TB) heuristic with less complexity, inspired by the stack building heuristic, is proposed to hybridize with an Ant Colony Optimization (ACO) for solving the Container Loading Problem (CLP). This approach is called, the Hybrid Ant Colony Optimization with Tower Building Heuristic (HACO). The aim of the CLP is to pack a subset of given three-dimensional rectangular boxes of different sizes into a three-dimensional rectangular container of fixed dimensions in order to achieve optimal space utilization. The TB heuristic placed the base box on the container floor and packed the boxes on the base box by stacking them one by one until the container is full, whereas other researchers used the stack building heuristic to generate a set of box towers from all of the given boxes then only arranged them into the container. The HACO is applied with its probabilistic decision rule and pheromone feedback, together with the TB heuristic to construct towers of boxes to be arranged into the container in order to find the optimal solution. The pheromone evaporation will reduce the chances of the other ants selecting the same solution and consequently the search will be diversified. Preliminary computational experiments were conducted on a subset of benchmark data sets as to find the appropriate parameters setting for the developed HACO. The proposed algorithm is tested on two standard benchmark data sets to evaluate the performance and to determine the effectiveness of the algorithm. The results in space utilization obtained were comparable with other heuristic and metaheuristic approaches from the literature. It was showed that the proposed HACO algorithm has the capability in solving the CLP.
format	Thesis
qualification_level	Master's degree
author	Yap, Ching Nei
author_facet	Yap, Ching Nei
author_sort	Yap, Ching Nei
title	Hybrid ant colony optimization algorithm for container loading problem
title_short	Hybrid ant colony optimization algorithm for container loading problem
title_full	Hybrid ant colony optimization algorithm for container loading problem
title_fullStr	Hybrid ant colony optimization algorithm for container loading problem
title_full_unstemmed	Hybrid ant colony optimization algorithm for container loading problem
title_sort	hybrid ant colony optimization algorithm for container loading problem
granting_institution	Universiti Putra Malaysia
granting_department	Institute for Mathematical Research
publishDate	2012
url	http://psasir.upm.edu.my/id/eprint/31439/1/IPM%202012%204R.pdf
_version_	1747811614479876096

Hybrid ant colony optimization algorithm for container loading problem

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