Efficient heuristics for solving precedence constrained scheduling problems

Kalfay, Amy, Crispin, Alan and Crockett, Keeley ORCID: https://orcid.org/0000-0003-1941-6201 (2020) Efficient heuristics for solving precedence constrained scheduling problems. In: Intelligent Systems Conference (IntelliSys) 2019, 05 September 2019 - 06 September 2019, London, United Kingdom.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-03...

Abstract

This paper discusses the occurrence of dependency relationships within NP hard personnel scheduling problems. These dependencies, commonly referred to as precedence constraints, arise in a number of industries including but not limited to: maintenance scheduling, home health care, and unmanned aerial vehicle scheduling. Precedence relationships, as demonstrated in this research, can significantly impact the quality of solution that can be obtained. In such a competitive market it is imperative that new and innovative ways of finding high quality solutions in short computational times are discovered. This paper presents novel datasets, containing 100-1000 jobs to allocate, that are used to benchmark two heuristic algorithms; an intelligent decision heuristic and a greedy heuristic. Each heuristic is coupled with a multi start metaheuristic to provide a set of benchmark results.

Item Type:	Conference or Workshop Item (Paper)
Published Proceedings:	Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys)
Volume:	1
Number:	1037
Peer-reviewed:	Yes
Date Deposited:	29 Jul 2019 10:58
Publisher:	Springer
Additional Information:	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-030-29516-5_85
Divisions:	Faculties > Science and Engineering Research Centres > Centre for Advanced Computational Science
URI:	https://mmu-uat.leaf.cosector.com/id/eprint/622783
DOI:	https://doi.org/10.1007/978-3-030-29516-5_85
ISSN	2194-5357
e-ISSN	2194-5365

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