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    Rights statement: This is the author’s version of a work that was accepted for publication in Operations Research Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Operations Research Letters, 49, 4, 2021 DOI: 10.1016/j.orl.2021.06.011

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    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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A separation algorithm for the simple plant location problem

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published
<mark>Journal publication date</mark>31/07/2021
<mark>Journal</mark>Operations Research Letters
Issue number4
Volume49
Number of pages6
Pages (from-to)610-615
Publication StatusPublished
Early online date23/06/21
<mark>Original language</mark>English

Abstract

The Simple Plant Location Problem (SPLP) is a well-known NP-hard optimisation problem with applications in logistics. Although many families of facet-defining inequalities are known for the associated polyhedron, very little work has been done on separation algorithms. We present the first ever polynomial-time separation algorithm for the SPLP that separates exactly over an exponentially large family of facet-defining inequalities. We also present some promising computational
results.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Operations Research Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Operations Research Letters, vol. 49, issue 4, pp. 610-615, 2021. DOI: 10.1016/j.orl.2021.06.011