This paper deals with the Surgical Case Assignment Problem (SCAP) taking into account the variability pertaining patient surgery duration. In particular, given a surgery waiting list, a set of Operating Room (OR) blocks and a planning horizon, the decision herein addressed is to determine the subset of patients to be scheduled in the considered time horizon and their assignment to the available OR block times. The aim is to minimize a penalty associated to waiting time, urgency and tardiness of patients. We propose a robust optimization approach for the SCAP with uncertain surgery duration, which allows to exploit the potentialities of a mathematical programming model without the necessity of generating scenarios. Tests on a set of real-based instances are carried on in order to evaluate the solutions obtained solving different versions of the problem. Besides the value of the penalty objective function, the solution quality is also evaluated with regards to the number of patients operated and their tardiness. Furthermore, assuming lognormal distribution for the surgery times, we use a set of randomly generated scenarios in order to assess the performance of the proposed solutions in terms of OR utilization rate and number of cancelled patients.
A Robust Optimization Approach for the Operating Room Planning Problem with Uncertain Surgery Duration
CARELLO, GIULIANA;
2014-01-01
Abstract
This paper deals with the Surgical Case Assignment Problem (SCAP) taking into account the variability pertaining patient surgery duration. In particular, given a surgery waiting list, a set of Operating Room (OR) blocks and a planning horizon, the decision herein addressed is to determine the subset of patients to be scheduled in the considered time horizon and their assignment to the available OR block times. The aim is to minimize a penalty associated to waiting time, urgency and tardiness of patients. We propose a robust optimization approach for the SCAP with uncertain surgery duration, which allows to exploit the potentialities of a mathematical programming model without the necessity of generating scenarios. Tests on a set of real-based instances are carried on in order to evaluate the solutions obtained solving different versions of the problem. Besides the value of the penalty objective function, the solution quality is also evaluated with regards to the number of patients operated and their tardiness. Furthermore, assuming lognormal distribution for the surgery times, we use a set of randomly generated scenarios in order to assess the performance of the proposed solutions in terms of OR utilization rate and number of cancelled patients.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.