Abstract
Operating rooms are amongst the most critical resources in hospitals. Appropriate schedules of surgical interventions increase the surgeries' success rates. Indeed, surgery outcomes strongly depend on the timing of each step in the surgery process. Therefore, effective and efficient surgery schedules can ease patients' suffering and even save their lives while making good use of limited hospital resources. This paper studies the two-stage nowait hybrid flow shop scheduling problem with inter-stage flexibility. The problem is inspired from hospital operating room scheduling under limited healthcare resources. We propose a time-indexed mixed integer linear programming formulation of the problem. We also introduce valid inequalities along with four lower bounds and four heuristics to handle the large scale of the problem. The proposed model is tested on randomly generated instances based on realistic data for operating room scheduling. Experimental results on the performance of the model and comparisons among the lower bounds and heuristics are reported for the different sizes of instance classes.