Workload Balancing Among Heathcare Workers Under Uncertain Service Time Using Distributionally Robust Optimization

Healthcare systems are facing serious challenges in balancing their human resources to cope with volatile service demand, while at the same time providing necessary job satisfaction healthcare workers. In this paper, we propose a distributionally robust optimization formulation generate task assignment plan that promotes fairness allocation, attained by reducing difference total working among workers, under uncertain time. The proposed joint chance constraint model is conservatively approximated worst-case Conditional Value-at-Risk, and devise sequential algorithm solve finite-dimensional reformulations which linear (mixed-binary) problems. We also provide explicit formula situation where support set of random vectors hyperrectangle. experiment both synthetic real data indicates promising results for our approach.