Modeling Employee Flexible Work Scheduling As A Classification Problem Article Swipe
Fred N. Kiwanuka
,
Louay Karadsheh
,
Ja’far Alqatawna
,
Anang Hudaya Muhamad Amin
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2021.09.101
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2021.09.101
Many organizations have adapted flexible working arrangements during COVID19 pandemic because of restrictions on the number of employees required on site at any time. Unfortunately, current employee scheduling methods are more suited for compressed working arrangements. The problem of automating compressed employee scheduling has been studied by many researchers and is widely adopted by many organizations in an attempt to achieve high quality scheduling. During process of employee scheduling many constraints may have to be considered and may require negotiating a large dimension of constraints like in flexible working. These constraints make scheduling a challenging task in these working arrangements. Most scheduling algorithms are modeled as constraint optimization problems and suited for compressed work but for flexible working with large constraint dimensions, achieving accurate scheduling is even more challenging. In this research, we propose a machine learning approach that takes advantage of mining user-defined constraints or soft constraints and transform employee scheduling into a classification problem. We propose automatically extracting employee personal schedules like calendars in order to extract their availability. We then show how to use the extracted knowledge in a multi-label classification approach in order to generate a schedule for faculty staff in a University that supports flexible working. We show that the results of this approach are comparable to that of a constraint satisfaction and optimization method that is commonly used in literature. Results show that our approach achieved accuracy of 93.1% of satisfying constraints as compared to 92.7% of a common constraint programming approach.
Related Topics
Concepts
Computer science
Scheduling (production processes)
Job shop scheduling
Negotiation
Two-level scheduling
Fair-share scheduling
Constraint programming
Industrial engineering
Schedule
Operations research
Distributed computing
Machine learning
Mathematical optimization
Law
Engineering
Stochastic programming
Operating system
Mathematics
Political science
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2021.09.101
- OA Status
- diamond
- Cited By
- 6
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3203222775
All OpenAlex metadata
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https://openalex.org/W3203222775Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.procs.2021.09.101Digital Object Identifier
- Title
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Modeling Employee Flexible Work Scheduling As A Classification ProblemWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-01-01Full publication date if available
- Authors
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Fred N. Kiwanuka, Louay Karadsheh, Ja’far Alqatawna, Anang Hudaya Muhamad AminList of authors in order
- Landing page
-
https://doi.org/10.1016/j.procs.2021.09.101Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://doi.org/10.1016/j.procs.2021.09.101Direct OA link when available
- Concepts
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Computer science, Scheduling (production processes), Job shop scheduling, Negotiation, Two-level scheduling, Fair-share scheduling, Constraint programming, Industrial engineering, Schedule, Operations research, Distributed computing, Machine learning, Mathematical optimization, Law, Engineering, Stochastic programming, Operating system, Mathematics, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 4Per-year citation counts (last 5 years)
- References (count)
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19Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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