An Automated Employee Timetabling System for Small Businesses

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.

Algorithms as work designers: How algorithmic management influences the design of jobs

“…The main affordance of an AI-augmented scheduling process is to determine, for a specific timeframe, the best match between labor requirements and supply. 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). Calculation of the best matching labor supply is based on information such as the availabilities of workers (or real-time app-active workers, in the case of platforms), their respective performance scores and their previous customer ratings, predicted performance, location or skills set (Cheng et al, 2015;Levy & Barocas, 2018;.…”

Research Updates from the Deployed Applications of the 2018 Innovative Applications of Artificial Intelligence Conference (IAAI‐18): Editorial Introduction to the Special Articles on Innovative AI Applications

“…This is no small feat! IAAI-18 had a diverse set of seven papers in this track: one on improving government human-resource services deployed in three cities (Zheng et al 2018); one on improving employee shift schedules with complex constraints used in two types of businesses (Hoshino et al 2018); one discussing the learnings from deploying sketch technologies in two science, technology, engineering, and mathematics classes (Forbus et al 2018); one on machine-learned revenue forecasting models used in a major software development company (Barker et al 2018); one on a question-answering system used in an enterprise information technologies helpdesk (Mani et al 2018); one on an interactive learning system used to optimize multivariate deployed user interface changes available commercially (Miikkulainen et al 2018); and one that was a unique insight into an analytics, analysis, and dynamic adjustment system integrated into the products of several game studios through their major publisher (Kolen et al 2018).…”