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Cited by 4Scheduling and Timetabling SolutionsHealthcare Operations and Scheduling OptimizationConstraint Satisfaction and Optimization

An Automated Employee Timetabling System for Small Businesses

Open access
Hoshino, Richard, Slobodin, Aaron, Bernoudy, William·2018·Proceedings of the AAAI Conference on Artificial Intelligence
DOI 10.1609/aaai.v32i1.11383
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Abstract

Employee scheduling is one of the most difficult challenges facing any small business owner. The problem becomes more complex when employees with different levels of seniority indicate preferences for specific roles in certain shifts and request flexible work hours outside of the standard eight-hour block. Many business owners and managers, who cannot afford (or choose not to use) commercially-available timetabling apps, spend numerous hours creating sub-optimal schedules by hand, leading to low staff morale. In this paper, we explain how two undergraduate students generalized the Nurse Scheduling Problem to take into account multiple roles and flexible work hours, and implemented a user-friendly automated timetabler based on a four-dimensional integer linear program. This system has been successfully deployed at two businesses in our community, each with 20+ employees: a coffee shop and a health clinic.

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Cites

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  • Both labor needs and supply in algorithmic scheduling rely on algorithmic estimation.

    “Both needs and supply rely on algorithmic estimation, with the precise prediction of needs in workforce based on information such as expected or real-time customer traffic, deadlines, real-time monitoring of fluctuating demand, weather forecasts, or on previous occupancy at the same date (Cheng, Rao, Jiang, & Zhou, 2015;Hoshino, Slobodin, & Bernoudy, 2018).”

    From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Hoshino, Slobodin, & Bernoudy, 2018

    The Mathematical Model (p. 3) uses employee availability and minimum and maximum staffing requirements as inputs to an algorithm that optimizes schedules. The Next Steps section (p. 6) says managers gather employee preferences and enter them into the program. The paper therefore supports algorithmic scheduling using information about needs and supply, but not the claim that the algorithm estimates both. (LLM, conf=0.79)

    Experimental — accuracy not yet measured

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Related

Authors
Hoshino, R., Slobodin, A., & Bernoudy, W.
Affiliations
Quest University Canada (CA)
ORCID
Venue
Proceedings of the AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Published
2018-04-27
Volume
32
Issue
1
ISSN
  • 2159-5399 (ISSN-L)
  • 2374-3468
DOI
10.1609/aaai.v32i1.11383
OpenAlex
W2805910173
Cited by
4
References
10
Citations by year
2023: 1 · 2021: 1 · 2020: 2
FWCI
1.11
Type
conference paper
Language
en
Open access
Locations
  • Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
  • AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Scheduling and Timetabling Solutions
  • Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
  • Physical Sciences › Computer Science › Computer Networks and Communications › Constraint Satisfaction and Optimization
Keywords
workforce scheduling, small business, nurse scheduling, flexible working hours, multiple roles, seniority, integer linear programming

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