Heuristic generation via parameter tuning for online bin packing

Online bin packing requires immediate decisions to be made for placing an incoming item one at a time into bins of fixed capacity without causing any overflow. The goal is to maximise the average bin fullness after placement of a long stream of items. A recent work describes an approach for solving...

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Main Authors: Yarimcam, Ahmet, Asta, Shahriar, Özcan, Ender, Parkes, Andrew J.
Format: Conference or Workshop Item
Published: 2014
Online Access:https://eprints.nottingham.ac.uk/34400/
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author Yarimcam, Ahmet
Asta, Shahriar
Özcan, Ender
Parkes, Andrew J.
author_facet Yarimcam, Ahmet
Asta, Shahriar
Özcan, Ender
Parkes, Andrew J.
author_sort Yarimcam, Ahmet
building Nottingham Research Data Repository
collection Online Access
description Online bin packing requires immediate decisions to be made for placing an incoming item one at a time into bins of fixed capacity without causing any overflow. The goal is to maximise the average bin fullness after placement of a long stream of items. A recent work describes an approach for solving this problem based on a ‘policy matrix’ representation in which each decision option is independently given a value and the highest value option is selected. A policy matrix can also be viewed as a heuristic with many parameters and then the search for a good policy matrix can be treated as a parameter tuning process. In this study, we show that the Irace parameter tuning algorithm produces heuristics which outperform the standard human designed heuristics for various instances of the online bin packing problem.
first_indexed 2025-11-14T19:22:39Z
format Conference or Workshop Item
id nottingham-34400
institution University of Nottingham Malaysia Campus
institution_category Local University
last_indexed 2025-11-14T19:22:39Z
publishDate 2014
recordtype eprints
repository_type Digital Repository
spelling nottingham-344002020-05-04T16:58:51Z https://eprints.nottingham.ac.uk/34400/ Heuristic generation via parameter tuning for online bin packing Yarimcam, Ahmet Asta, Shahriar Özcan, Ender Parkes, Andrew J. Online bin packing requires immediate decisions to be made for placing an incoming item one at a time into bins of fixed capacity without causing any overflow. The goal is to maximise the average bin fullness after placement of a long stream of items. A recent work describes an approach for solving this problem based on a ‘policy matrix’ representation in which each decision option is independently given a value and the highest value option is selected. A policy matrix can also be viewed as a heuristic with many parameters and then the search for a good policy matrix can be treated as a parameter tuning process. In this study, we show that the Irace parameter tuning algorithm produces heuristics which outperform the standard human designed heuristics for various instances of the online bin packing problem. 2014-12-12 Conference or Workshop Item PeerReviewed Yarimcam, Ahmet, Asta, Shahriar, Özcan, Ender and Parkes, Andrew J. (2014) Heuristic generation via parameter tuning for online bin packing. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), 9-12 Dec 2014, Orlando, Florida. http://dx.doi.org/10.1109/EALS.2014.7009510 10.1109/EALS.2014.7009510 10.1109/EALS.2014.7009510 10.1109/EALS.2014.7009510
spellingShingle Yarimcam, Ahmet
Asta, Shahriar
Özcan, Ender
Parkes, Andrew J.
Heuristic generation via parameter tuning for online bin packing
title Heuristic generation via parameter tuning for online bin packing
title_full Heuristic generation via parameter tuning for online bin packing
title_fullStr Heuristic generation via parameter tuning for online bin packing
title_full_unstemmed Heuristic generation via parameter tuning for online bin packing
title_short Heuristic generation via parameter tuning for online bin packing
title_sort heuristic generation via parameter tuning for online bin packing
url https://eprints.nottingham.ac.uk/34400/
https://eprints.nottingham.ac.uk/34400/
https://eprints.nottingham.ac.uk/34400/