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

A genetic algorithm for two-dimensional bin packing with due dates
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 lateness of the rectangles. This problem is motivated by the potential increase efficiency that might be gained 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. The genetic algorithm employs an innovative crossover operator that considers a number of different children from each pair of parents. Comprehensive results are presented, and the algorithm is shown to be competitive when compared with other local search methods.
CORMSIS-08-16
University of Southampton
Bennell, J.A.
38d924bc-c870-4641-9448-1ac8dd663a30
Lee, L.S.
843b0022-160e-4556-ab1c-d182eb87cd75
Potts, C.N.
58c36fe5-3bcb-4320-a018-509844d4ccff
Bennell, J.A.
38d924bc-c870-4641-9448-1ac8dd663a30
Lee, L.S.
843b0022-160e-4556-ab1c-d182eb87cd75
Potts, C.N.
58c36fe5-3bcb-4320-a018-509844d4ccff

Bennell, J.A., Lee, L.S. and Potts, C.N. (2008) A genetic algorithm for two-dimensional bin packing with due dates (Discussion Papers in Centre for Operational Research, Management Science and Information Systems, CORMSIS-08-16) Southampton, UK. University of Southampton

Record type: Monograph (Discussion Paper)

Abstract

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 lateness of the rectangles. This problem is motivated by the potential increase efficiency that might be gained 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. The genetic algorithm employs an innovative crossover operator that considers a number of different children from each pair of parents. Comprehensive results are presented, and the algorithm is shown to be competitive when compared with other local search methods.

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Published date: 2008
Organisations: Management

Identifiers

Local EPrints ID: 63409
URI: https://eprints.soton.ac.uk/id/eprint/63409
PURE UUID: 9d8c3f9e-3beb-40f4-b3e0-9431837dabc7

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Date deposited: 28 Oct 2008
Last modified: 13 Mar 2019 20:25

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