Adapting GA to solve a novel model for operating room scheduling problem with endogenous uncertainty

This paper addresses a new variant of the daily operating room scheduling problem in which, surgeries have stochastic durations, and in order to get a more flexible schedule, the initial scheduling and the rescheduling decisions are simultaneously considered within a single optimization model. The main point in the formulation of this problem is that the time of the uncertainty realization is decision dependent and hence, the uncertainty is of endogenous nature which is a new topic in stochastic programming (SP) literature. First, a novel mathematical model is developed for this problem, and an illustrative example is provided to justify the importance of the joint optimization of scheduling and rescheduling decisions. Then, the structure of the proposed model is utilized to develop a genetic algorithm (GA) to solve large instances of this NP-hard optimization problem. Computational experiments on some randomly generated test problems, confirm the efficiency of the proposed GA in terms of the solution quality and time. Moreover, the results indicate that a cost reduction may be achieved by integrating scheduling and rescheduling decisions. (C) 2018 Elsevier Ltd. All rights reserved.