Pareto optimal strategies for improved operational plans of elective patients under multiple constrained resources

This paper deals with elective surgery planning under several scarce resources. We use a Mixed Integer Program model to determine the best admission policy at the tactical level, with the objective of minimising the weighted deviations of the expected resources consumptions from their target levels. On the operational level, a flexibility strategy is implemented to adjust the tactical plan to patients in queue so as to get feasible operational plans. We developed two other strategies to obtain improvements in terms of waiting time: slack planning and updating the tactical plan. Performance of the strategies was assessed through extensive simulations based upon data from a Dutch Thoracic Surgery Centre. Hospital efficiency was measured using a global volatility indicator defined as the weighted sum of several criteria such as additional or cancelled operations, plan changes and deviations of resources consumptions compared to their target levels. Weights values in the global volatility indicator were drawn at random in large intervals to portray a wide spread of managers’ preferences. With two indicators – the waiting time and the global volatility index – we were able to conduct a Pareto optimality analysis for efficiently identifying the best strategies to reach some waiting time order of magnitude. Simulation results highlighted a trade-off between waiting time and volatility and show that Pareto optimality of most strategies does not strongly dependent on managers’ preferences profiles.