Accommodating Employee Preferences in Algorithmic Worker-Workplace Allocation
Authors: Charlotte Haid, Sebastian Stohrer, Charlotte Unruh, Tim Büthe, Johannes Fottner
Abstract: Since many processes in logistics are difficult to automate, employees will continue to be a crucial part of the logistics ecosystem. Allocating employees to tasks on the shop floor continues to be essential, but should focus more on personal preferences. Traditional allocation systems have hardly taken employee preferences into consideration. We ensure that workers can specify their preferences in more detail, and enable best-fit allocation of workers and tasks. To gather information about employee preferences, we designed a survey that can be completed quickly and allows us to get information about employee preferences. We have developed a solution for our preference-based scheduling, namely a hybrid AI algorithm. The solution is discussed for our use case: matching employees to workplaces in logistics. With this work we contribute to a transparent consideration of preferences in scheduling and show details of the algorithm. We aim to extend research in this area with our open source code on github.
Keywords: Algorithmic Scheduling, Employee Preferences, Artificial Intelligence, Future of Work
Cite this paper: