A study of genetic operators for the Workforce Scheduling and Routing Problem
The Workforce Scheduling and Routing Problem (WSRP) is concerned with planning visits of qualified workers to different locations to perform a set of tasks, while satisfying each task time-window plus additional requirements such as customer/workers preferences. This type of mobile workforce schedul...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
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2015
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| Online Access: | https://eprints.nottingham.ac.uk/31292/ |
| _version_ | 1848794169508626432 |
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| author | Algethami, Haneen Landa-Silva, Dario |
| author_facet | Algethami, Haneen Landa-Silva, Dario |
| author_sort | Algethami, Haneen |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The Workforce Scheduling and Routing Problem (WSRP) is concerned with planning visits of qualified workers to different locations to perform a set of tasks, while satisfying each task time-window plus additional requirements such as customer/workers preferences. This type of mobile workforce scheduling problem arises in many real-world operational scenarios. We investigate a set of genetic operators including problem-specific and well-known generic operators used in related problems. The aim is to conduct an in-depth analysis on their performance on this very constrained scheduling problem. In particular, we want to identify genetic operators that could help to minimise the violation of customer/workers preferences. We also develop two cost-based genetic operators tailored to the WSRP. A Steady State Genetic Algorithm (SSGA) is used in the study and experiments are conducted on a set of problem instances from a real-world Home Health Care scenario (HHC). The experimental analysis allows us to better understand how we can more effectively employ genetic operators to tackle WSRPs. |
| first_indexed | 2025-11-14T19:11:55Z |
| format | Conference or Workshop Item |
| id | nottingham-31292 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:11:55Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-312922020-05-04T17:10:58Z https://eprints.nottingham.ac.uk/31292/ A study of genetic operators for the Workforce Scheduling and Routing Problem Algethami, Haneen Landa-Silva, Dario The Workforce Scheduling and Routing Problem (WSRP) is concerned with planning visits of qualified workers to different locations to perform a set of tasks, while satisfying each task time-window plus additional requirements such as customer/workers preferences. This type of mobile workforce scheduling problem arises in many real-world operational scenarios. We investigate a set of genetic operators including problem-specific and well-known generic operators used in related problems. The aim is to conduct an in-depth analysis on their performance on this very constrained scheduling problem. In particular, we want to identify genetic operators that could help to minimise the violation of customer/workers preferences. We also develop two cost-based genetic operators tailored to the WSRP. A Steady State Genetic Algorithm (SSGA) is used in the study and experiments are conducted on a set of problem instances from a real-world Home Health Care scenario (HHC). The experimental analysis allows us to better understand how we can more effectively employ genetic operators to tackle WSRPs. 2015-06-10 Conference or Workshop Item PeerReviewed Algethami, Haneen and Landa-Silva, Dario (2015) A study of genetic operators for the Workforce Scheduling and Routing Problem. In: 11th Metaheuristics International Conference (MIC 2015), 7-10 June 2015, Agadir, Morocco. Personnel Scheduling Genetic Operators Evolutionary Algorithms |
| spellingShingle | Personnel Scheduling Genetic Operators Evolutionary Algorithms Algethami, Haneen Landa-Silva, Dario A study of genetic operators for the Workforce Scheduling and Routing Problem |
| title | A study of genetic operators for the Workforce Scheduling and Routing Problem |
| title_full | A study of genetic operators for the Workforce Scheduling and Routing Problem |
| title_fullStr | A study of genetic operators for the Workforce Scheduling and Routing Problem |
| title_full_unstemmed | A study of genetic operators for the Workforce Scheduling and Routing Problem |
| title_short | A study of genetic operators for the Workforce Scheduling and Routing Problem |
| title_sort | study of genetic operators for the workforce scheduling and routing problem |
| topic | Personnel Scheduling Genetic Operators Evolutionary Algorithms |
| url | https://eprints.nottingham.ac.uk/31292/ |