J. Arturo Castillo-Salazar
A greedy heuristic for workforce scheduling and routing with time-dependent activities constraints
Castillo-Salazar, J. Arturo; Landa-Silva, Dario; Qu, Rong
Authors
DARIO LANDA SILVA DARIO.LANDASILVA@NOTTINGHAM.AC.UK
Professor of Computational Optimisation
RONG QU rong.qu@nottingham.ac.uk
Professor of Computer Science
Abstract
We present a greedy heuristic (GHI) designed to tackle five time-dependent activities constraints (synchronisation, overlap, minimum difference, maximum difference and minimum-maximum difference) on workforce scheduling and routing problems. These types of constraints are important because they allow the modelling of situations in which activities relate to each other time-wise, e.g. synchronising two technicians to complete a job. These constraints often make the scheduling and routing of employees more difficult. GHI is tested on set of benchmark instances from different workforce scheduling and routing problems (WSRPs). We compare the results obtained by GHI against the results from a mathematical programming solver. The comparison seeks to determine which solution method achieves more best solutions across all instances. Two parameters of GHI are discussed, the sorting of employees and the sorting of visits. We conclude that using the solver is adequate for instances with less than 100 visits but for larger instances GHI obtains better results in less time.
Citation
Castillo-Salazar, J. A., Landa-Silva, D., & Qu, R. (2015). A greedy heuristic for workforce scheduling and routing with time-dependent activities constraints.
Conference Name | International Conference on Operations Research and Enterprise Systems (ICORES 2015) |
---|---|
End Date | Jan 12, 2015 |
Publication Date | Jan 1, 2015 |
Deposit Date | Jan 21, 2016 |
Publicly Available Date | Mar 29, 2024 |
Peer Reviewed | Peer Reviewed |
Keywords | personnel scheduling, vehicle routing, constructive greedy heuristics |
Public URL | https://nottingham-repository.worktribe.com/output/987880 |
Publisher URL | http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220%2f0005223203670375 |
Additional Information | Published in: Proceedings of the International Conference on Operations Research and Enterprise Systems (ICORES-2015), ISBN 9789897580758, p. 367-375. DOI: 10.5220/0005223203670375. |
Files
dls_icores2015_2_published.pdf
(651 Kb)
PDF
You might also like
Models of Representation in Computational Intelligence [Guest Editorial]
(2023)
Journal Article
Automated algorithm design using proximal policy optimisation with identified features
(2022)
Journal Article
An Efficient Federated Distillation Learning System for Multitask Time Series Classification
(2022)
Journal Article
A Collaborative Learning Tracking Network for Remote Sensing Videos
(2022)
Journal Article
Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification
(2021)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: digital-library-support@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
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 © 2024
Advanced Search