Staff Scheduling for Demand-Responsive Services
Debsankha Manik, Rico Raber

TL;DR
This paper introduces a novel approach to staff scheduling in demand-responsive services, focusing on optimizing reward functions based on demand fluctuations rather than fixed staffing constraints.
Contribution
It presents a new model and solution method tailored for demand-responsive staff scheduling, differing from traditional fixed-constraint approaches.
Findings
The approach effectively maximizes total reward over the planning horizon.
It adapts to demand variations without fixed minimum staffing constraints.
The method outperforms traditional scheduling algorithms in relevant scenarios.
Abstract
Staff scheduling is a well-known problem in operations research and finds its application at hospitals, airports, supermarkets, and many others. Its goal is to assign shifts to staff members such that a certain objective function, e.g. revenue, is maximized. Meanwhile, various constraints of the staff members and the organization need to be satisfied. Typically in staff scheduling problems, there are hard constraints on the minimum number of employees that should be available at specific points of time. Often multiple hard constraints guaranteeing the availability of specific number of employees with different roles need to be considered. Staff scheduling for demand-responsive services, such as, e.g., ride-pooling and ride-hailing services, differs in a key way from this: There are often no hard constraints on the minimum number of employees needed at fixed points in time. Rather, the…
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