Evolving Staff Training Schedules using an Extensible Fitness Function and a Domain Specific Language

Urquhart, Neil; Hunter, Kelly

Abstract

When using a meta-heuristic based optimiser in some industrial scenarios, there may be a need to amend the objective function as time progresses to encompass constraints that did not exist during the development phase of the software. We propose a means by which a Domain Specific Language (DSL) can be used to allow constraints to be expressed in language familiar to a domain expert, allowing additional constraints to be added to the objective function without the need to recompile the solver. To illustrate the approach, we consider the construction of staff training schedules within an organisation where staff are already managed within highly constrained schedules. A set of constraints are hard-coded into the objective function in a conventional manner as part of a Java application. A custom built domain specific language (named Basil) was developed by the authors which is used to specify additional constraints affecting individual members of staff or groups. We demonstrate the use of Basil and show how it allows the specification of additional constraints that enable the software to meet the requirements of the user without any technical knowledge.

Citation

Urquhart, N., & Hunter, K. (2024, April). Evolving Staff Training Schedules using an Extensible Fitness Function and a Domain Specific Language. Presented at 27th European Conference, EvoApplications 2024, Aberystwyth, UK

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 27th European Conference, EvoApplications 2024
• Start Date: Apr 3, 2024
• End Date: Apr 5, 2024
• Acceptance Date: Jan 10, 2024
• Online Publication Date: Mar 21, 2024
• Publication Date: 2024
• Deposit Date: Jan 12, 2024
• Publicly Available Date: Mar 22, 2025
• Publisher: Springer
• Pages: 83–97
• Series Title: Lecture Notes in Computer Science
• Series Number: 14634
• Series ISSN: 0302-9743
• Book Title: Applications of Evolutionary Computation
• ISBN: 9783031568510
• DOI: https://doi.org/10.1007/978-3-031-56852-7_6
• Public URL: http://researchrepository.napier.ac.uk/Output/3464317