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A genetic algorithm for two-dimensional bin packing with due dates

This paper considers a new variant of the two-dimensional bin packing problem where each rectangle is assigned a due date and each bin has a fixed processing time. Hence the objective is not only to minimize the number of bins, but also to minimize the maximum lateness of the rectangles. This proble...

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Main Authors: Bennell, Julia A., Lee, Lai Soon, Potts, Chris N.
Format: Article
Language:English
English
Published: Elsevier 2013
Online Access:http://psasir.upm.edu.my/id/eprint/30331/1/A%20genetic%20algorithm%20for%20two.pdf
http://psasir.upm.edu.my/id/eprint/30331/
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spelling my.upm.eprints.303312015-08-25T06:42:17Z http://psasir.upm.edu.my/id/eprint/30331/ A genetic algorithm for two-dimensional bin packing with due dates Bennell, Julia A. Lee, Lai Soon Potts, Chris N. This paper considers a new variant of the two-dimensional bin packing problem where each rectangle is assigned a due date and each bin has a fixed processing time. Hence the objective is not only to minimize the number of bins, but also to minimize the maximum lateness of the rectangles. This problem is motivated by the cutting of stock sheets and the potential increased efficiency that might be gained by drawing on a larger pool of demand pieces by mixing orders, while also aiming to ensure a certain level of customer service. We propose a genetic algorithm for searching the solution space, which uses a new placement heuristic for decoding the gene based on the best fit heuristic designed for the strip packing problems. The genetic algorithm employs an innovative crossover operator that considers several different children from each pair of parents. Further, the dual objective is optimized hierarchically with the primary objective periodically alternating between maximum lateness and number of bins. As a result, the approach produces several non-dominated solutions with different trade-offs. Two further approaches are implemented. One is based on a previous Unified Tabu Search, suitably modified to tackle this revised problem. The other is randomized descent and serves as a benchmark for comparing the results. Comprehensive computational results are presented, which show that the Unified Tabu Search still works well in minimizing the bins, but the genetic algorithm performs slightly better. When also considering maximum lateness, the genetic algorithm is considerably better. Elsevier 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30331/1/A%20genetic%20algorithm%20for%20two.pdf Bennell, Julia A. and Lee, Lai Soon and Potts, Chris N. (2013) A genetic algorithm for two-dimensional bin packing with due dates. International Journal of Production Economics, 145 (2). pp. 547-560. ISSN 0925-5273 10.1016/j.ijpe.2013.04.040 English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description This paper considers a new variant of the two-dimensional bin packing problem where each rectangle is assigned a due date and each bin has a fixed processing time. Hence the objective is not only to minimize the number of bins, but also to minimize the maximum lateness of the rectangles. This problem is motivated by the cutting of stock sheets and the potential increased efficiency that might be gained by drawing on a larger pool of demand pieces by mixing orders, while also aiming to ensure a certain level of customer service. We propose a genetic algorithm for searching the solution space, which uses a new placement heuristic for decoding the gene based on the best fit heuristic designed for the strip packing problems. The genetic algorithm employs an innovative crossover operator that considers several different children from each pair of parents. Further, the dual objective is optimized hierarchically with the primary objective periodically alternating between maximum lateness and number of bins. As a result, the approach produces several non-dominated solutions with different trade-offs. Two further approaches are implemented. One is based on a previous Unified Tabu Search, suitably modified to tackle this revised problem. The other is randomized descent and serves as a benchmark for comparing the results. Comprehensive computational results are presented, which show that the Unified Tabu Search still works well in minimizing the bins, but the genetic algorithm performs slightly better. When also considering maximum lateness, the genetic algorithm is considerably better.
format Article
author Bennell, Julia A.
Lee, Lai Soon
Potts, Chris N.
spellingShingle Bennell, Julia A.
Lee, Lai Soon
Potts, Chris N.
A genetic algorithm for two-dimensional bin packing with due dates
author_facet Bennell, Julia A.
Lee, Lai Soon
Potts, Chris N.
author_sort Bennell, Julia A.
title A genetic algorithm for two-dimensional bin packing with due dates
title_short A genetic algorithm for two-dimensional bin packing with due dates
title_full A genetic algorithm for two-dimensional bin packing with due dates
title_fullStr A genetic algorithm for two-dimensional bin packing with due dates
title_full_unstemmed A genetic algorithm for two-dimensional bin packing with due dates
title_sort genetic algorithm for two-dimensional bin packing with due dates
publisher Elsevier
publishDate 2013
url http://psasir.upm.edu.my/id/eprint/30331/1/A%20genetic%20algorithm%20for%20two.pdf
http://psasir.upm.edu.my/id/eprint/30331/
_version_ 1643830026249961472
score 13.211869

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