  We propose combined allocation, assignment, sequencing, and scheduling
problems under uncertainty involving multiple operation rooms (ORs),
anesthesiologists, and surgeries, as well as methodologies for solving such
problems. Specifically, given sets of ORs, regular anesthesiologists, on-call
anesthesiologists, and surgeries, our methodologies solve the following
decision-making problems simultaneously: (1) an allocation problem that decides
which ORs to open and which on-call anesthesiologists to call in, (2) an
assignment problem that assigns an OR and an anesthesiologist to each surgery,
and (3) a sequencing and scheduling problem that determines the order of
surgeries and their scheduled start times in each OR. To address uncertainty of
each surgery's duration, we propose and analyze stochastic programming (SP) and
distributionally robust optimization (DRO) models with both risk-neutral and
risk-averse objectives. We obtain near-optimal solutions of our SP models using
sample average approximation and propose a computationally efficient
column-and-constraint generation method to solve our DRO models. In addition,
we derive symmetry-breaking constraints that improve the models' solvability.
Using real-world, publicly available surgery data and a case study from a
health system in New York, we conduct extensive computational experiments
comparing the proposed methodologies empirically and theoretically,
demonstrating where significant performance improvements can be gained.
Additionally, we derive several managerial insights relevant to practice.
