Adaptive multiple crossover genetic algorithm to solve Workforce Scheduling and Routing Problem
The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operational cost. One of the main obstacles in designing...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2018
|
| Online Access: | https://eprints.nottingham.ac.uk/53048/ |
| _version_ | 1848798865633837056 |
|---|---|
| author | Algethami, Haneen Martinez-Gavara, Anna Landa-Silva, Dario |
| author_facet | Algethami, Haneen Martinez-Gavara, Anna Landa-Silva, Dario |
| author_sort | Algethami, Haneen |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operational cost. One of the main obstacles in designing a genetic algorithm for this problem is selecting the best set of operators that enable better performance in a Genetic Algorithm (GA). This paper presents an adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems. A mix of problem-specific and traditional crossovers are evaluated by using an online learning process to measure the operator's effectiveness. Best performing operators are given high application rates and low rates are given to the worse performing ones. Application rates are dynamically adjusted according to the learning outcomes in a non-stationary environment. Experimental results show that the combined performances of all the operators works better than using one operator in isolation. This study makes a contribution to advance our understanding of how to make effective use of crossover operators on this highly-constrained optimisation problem. |
| first_indexed | 2025-11-14T20:26:34Z |
| format | Article |
| id | nottingham-53048 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:26:34Z |
| publishDate | 2018 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-530482020-05-07T18:30:42Z https://eprints.nottingham.ac.uk/53048/ Adaptive multiple crossover genetic algorithm to solve Workforce Scheduling and Routing Problem Algethami, Haneen Martinez-Gavara, Anna Landa-Silva, Dario The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operational cost. One of the main obstacles in designing a genetic algorithm for this problem is selecting the best set of operators that enable better performance in a Genetic Algorithm (GA). This paper presents an adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems. A mix of problem-specific and traditional crossovers are evaluated by using an online learning process to measure the operator's effectiveness. Best performing operators are given high application rates and low rates are given to the worse performing ones. Application rates are dynamically adjusted according to the learning outcomes in a non-stationary environment. Experimental results show that the combined performances of all the operators works better than using one operator in isolation. This study makes a contribution to advance our understanding of how to make effective use of crossover operators on this highly-constrained optimisation problem. Springer 2018-06-22 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/53048/1/dls_joh2018.pdf Algethami, Haneen, Martinez-Gavara, Anna and Landa-Silva, Dario (2018) Adaptive multiple crossover genetic algorithm to solve Workforce Scheduling and Routing Problem. Journal of Heuristics . ISSN 1381-1231 (In Press) |
| spellingShingle | Algethami, Haneen Martinez-Gavara, Anna Landa-Silva, Dario Adaptive multiple crossover genetic algorithm to solve Workforce Scheduling and Routing Problem |
| title | Adaptive multiple crossover genetic algorithm to solve Workforce Scheduling and Routing Problem |
| title_full | Adaptive multiple crossover genetic algorithm to solve Workforce Scheduling and Routing Problem |
| title_fullStr | Adaptive multiple crossover genetic algorithm to solve Workforce Scheduling and Routing Problem |
| title_full_unstemmed | Adaptive multiple crossover genetic algorithm to solve Workforce Scheduling and Routing Problem |
| title_short | Adaptive multiple crossover genetic algorithm to solve Workforce Scheduling and Routing Problem |
| title_sort | adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem |
| url | https://eprints.nottingham.ac.uk/53048/ |