UCL School of Management

Research project

Sharing economy


A standard assumption in workforce management is that the firm can dictate to workers when to show up to work. However, that assumption is challenged in modern business environments, such as those arising in the sharing economy, where workers enjoy various degrees of flexibility, including the right to decide when to work. We use queueing theory to inform decision-making in those novel service environments. 


We derive staffing policies for queueing systems where the number of servers, i.e., the number of workers, is uncertain. We also study the special case of a blended workforce, where a part the workforce is made up of in-house permanent workers, whereas the other part is made up of contractors. The staffing policies that we obtain are useful to improve the management of novel service platforms, such as Uber or virtual call center platforms. 

Selected publications

Ibrahim, R. (2018). Managing Queueing Systems where Capacity is Random and Customers are Impatient. Production and Operations Management. doi:10.1111/poms.12796 [link]
Dong, J., & Ibrahim, R. (2020). Managing Supply in the On-Demand Economy: Flexible Workers, Full-Time Employees, or Both?. Operations Research. doi:10.1287/opre.2019.1916 [link]
Ibrahim, R. (2019). On Queues with a Random Capacity: Theory and Application. In M. Hu (Ed.), Sharing Economy, Springer Series in Supply Chain Management. Springer.
Last updated Friday, 15 January 2021


Research groups

Operations & Technology

Research areas

Management science; Operations management

Research topics

Applied probability; Applied stochastic modelling; Queueing theory