Stochastic tabu search: application to align physician schedule with patient flow
In this study, we consider the pretreatment phase for cancer patients. This is defined as the period between the referral to a cancer center and the confirmation of the treatment plan. Physicians have been identified as bottlenecks in this process, and the goal is to determine a weekly cyclic schedule that improves the patient flow and shortens the pretreatment duration. High uncertainty is associated with the arrival day, profile and type of cancer of each patient. We also include physician satisfaction in the objective function. We present a MIP model for the problem and develop a tabu search algorithm, considering both deterministic and stochastic cases. Experiments show that our method compares very well to CPLEX under deterministic conditions. We describe the stochastic approach in detail and present a real application.
Holding a PhD in Applied Mathematics from Polytechnique Montreal, Nadia Lahrichi is an associate professor in the department of mathematics and industrial engineering since 2011. Her areas of interest are mainly focused towards applying modelling and operational research tools in healthcare. Patient flow (scheduling, sequencing, …) and resource optimization (space requirements, nurse scheduling, …) problems are especially targeted