An Adjustable Robust Optimization Approach to Multi-objective Personnel Scheduling Under Uncertain Demand: A Case Study at a Pathology Department

Abstract

In this thesis, we address a multi-objective personnel scheduling problem where personnel’s workload is uncertain and propose a two-stage robust modelling approach with demand uncertainty. In the first stage, we model a multi-objective personnel scheduling problem without incorporating demand coverage and, in the second stage, we minimize over or under-staffing after the realization of the demand and the assignments from the first stage. Two solution approaches are introduced for this model. The first approach solves the proposed model through a cutting plane strategy known as Benders dual cutting plane method, and the second approach reformulates the problem based on the strong duality theory. As a case study, the proposed model and the first solution approach are applied to an existing scheduling problem in the pathology department at The Ottawa Hospital. It is shown that the proposed model is successful at reducing the unmet demand while maintaining the performance with respect to other metrics when compared against the deterministic alternative.