Patient scheduling within the operating room (OR) is subject to several forms of uncertainties related to the state of health of the patients and also to the availability of resources. Surgery duration and postoperative length-of-stay (LOS) in downstream recovery units, namely the intensive care unit (ICU) and the post-surgery units are two of the most significant patient-related sources of disturbance to the schedule. Elective patients undergo the surgery, then go to the post-surgery units. However, some patients must stay for one night or more in the ICU beds before being transferred to the post-surgery unit. Several existing works focus only on the upstream scheduling that concerns only the OR planning, which yields infeasible and sub-optimal schedules. On the other hand, the limited literature that handles the postoperative resources assumes that surgery duration and patients' LOS follow a well-known distribution. Unfortunately, it is challenging to use distributions to deduce surgery duration and LOS as is the case in stochastic approaches for medium and short-term planning. Consequently, we propose a new methodology for tactical planning using robust optimization. As stated in Shehadeh and Padman (2022), some research papers assume that the OR manager has a unique objective to satisfy. In reality, the OR can be managed by a group of decision-makers that strive to satisfy conflicting objectives. This work focuses on building a robust master surgical schedule (MSS) for tactical planning. To do this, we assign elective patients to available ORs under the block scheduling strategy. Our approach considers multiple conflicting objectives: patient priority, assignment cost, and workload balancing. We consider several OR restrictions: OR resources, surgeons' and downstream resources availability while incorporating uncertainty in surgery duration and patients' LOS in the ICU.