Dr Neil Urquhart N.Urquhart@napier.ac.uk
Lecturer
Dr Neil Urquhart N.Urquhart@napier.ac.uk
Lecturer
Prof Achille Fonzone A.Fonzone@napier.ac.uk
Professor
Agencies who provide social care services typically have to optimise staff allocations and the travel whilst attempting to satisfy conflicting objectives. In such cases it is desirable to have a range of solutions to choose from, allowing the agency's planning staff to explore the various options available This paper examines the use of multi-objective evolutionary algorithms to produce solutions to the Workforce Scheduling and Routing Problem (WSRP) formulated with three objectives which should be minimised: financial cost, CO2 emissions and car use. We show that financial cost and CO2 increase with the size of the problem and the imposed constraints. In order to support the planning staff in their decision making, we present an Evolutionary Algorithm based support tool that will identify a group of solutions from the Pareto front which match criteria specified by the planner. We demonstrate that our approach is able to find a wide range of solutions, which enhance the flexibility of the agency’s choices, the decision support tool subsequently allows the planner to discover small groups of solutions that meet their specific requirements.
Urquhart, N., & Fonzone, A. (2017, July). Evolving solution choice and decision support for a real-world optimisation problem. Presented at Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '17, Berlin, Germany
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '17 |
Start Date | Jul 15, 2017 |
End Date | Jul 19, 2017 |
Acceptance Date | Mar 20, 2017 |
Online Publication Date | Jul 1, 2017 |
Publication Date | Jul 1, 2017 |
Deposit Date | Mar 30, 2017 |
Publicly Available Date | Jun 5, 2017 |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1264-1271 |
Book Title | Proceedings of the Genetic and Evolutionary Computation Conference 2017 |
ISBN | 9781450349208 |
DOI | https://doi.org/10.1145/3071178.3071207 |
Keywords | Evolutionary Algorithms, Transportation, Multi-Objective Optimisation, Decision Support, Real-World Problems |
Public URL | http://researchrepository.napier.ac.uk/Output/822796 |
Contract Date | Jun 5, 2017 |
Evolving solution choice and decision support...revised copy
(2 Mb)
PDF
Copyright Statement
"© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Neil Urquhart and Achille Fonzone. 2017. Evolving solution choice and decision support for a real-world optimisation problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '17). ACM, New York, NY, USA, 1264-1271. DOI: Https://doi.org/10.1145/3071178.3071207
State assignment for sequential circuits using multi-objective genetic algorithm
(2011)
Journal Article
Manipulation and optimization techniques for Boolean logic
(2010)
Journal Article
Creating optimised employee travel plans.
(2015)
Presentation / Conference Contribution
Techniques for Auditing the ICT Carbon Footprint of an Organisation
(2014)
Journal Article
Minimization of incompletely specified mixed polarity Reed Muller functions using genetic algorithm.
(2009)
Presentation / Conference Contribution
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search