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Analysis of irregular three-dimensional packing problems in additive manufacturing: a new taxonomy and dataset

Araújo, Luiz J.P.; Özcan, Ender; Atkin, Jason A.D.; Baumers, Martin

Analysis of irregular three-dimensional packing problems in additive manufacturing: a new taxonomy and dataset Thumbnail


Luiz J.P. Araújo

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Professor of Computer Science and Operational Research

Associate Professor


© 2018 Informa UK Limited, trading as Taylor & Francis Group. With most Additive Manufacturing (AM) technology variants, build processes take place inside an internal enclosed build container, referred to as a ‘build volume’. It has been demonstrated that the effectiveness with which this volume is filled with product geometries forms an important determinant of overall process efficiency in AM. For effective operations management, it is important to understand not only the problem faced, but also which methods have proved effective (or ineffective) for problems with these characteristics in the past. This research aims to facilitate this increased understanding. The build volume packing task can be formulated as a three-dimensional irregular packing (3DIP) problem, which is a combinatorial optimisation problem requiring the configuration of a set of arbitrary volumetric items. This paper reviews existing general cutting and packing taxonomies and provides a new specification which is more appropriate for classifying the problems encountered in AM. This comprises a clear-cut problem definition, a set of precise categorisation criteria for objectives and problem instances, and a simple notation. Furthermore, the paper establishes an improved terminology with terms that are familiar to, but not limited to, researchers and practitioners in the field of AM. Finally, this paper describes a new dataset to be used in the evaluation of existing and proposed computational solution methods for 3DIP problems encountered in AM and discusses the importance of this research for further underpinning work.

Journal Article Type Review
Acceptance Date Sep 30, 2018
Online Publication Date Oct 16, 2018
Publication Date Jan 1, 2019
Deposit Date Dec 3, 2018
Publicly Available Date Oct 17, 2019
Journal International Journal of Production Research
Print ISSN 0020-7543
Electronic ISSN 1366-588X
Publisher Taylor and Francis
Peer Reviewed Peer Reviewed
Volume 57
Issue 18
Pages 5920-5934
Keywords Management Science and Operations Research; Strategy and Management; Industrial and Manufacturing Engineering
Public URL
Additional Information This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 16/10/2018, available online:


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