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This work concerns the advance scheduling of elective surgery when the operating rooms' capacity utilization by emergency surgery, as well as by elective procedures, is uncertain. New requests for bookings of elective surgery arrive each day. Such procedures preferably would be performed as soon as possible, but admitting too many patients may result in exceeding a day's capacity, possibly necessitating turning away some emergency cases. So the problem facing the hospital at the start of each day is how many of the additional requests for elective surgery to assign for that day. We provide a stochastic dynamic programming model for this aggregate advance scheduling problem. The model has some novel mathematical features. We analyze it and characterize the nature of the optimal policy, which is not necessarily of a control-limit type. Plausible numerical examples which confirm our theoretical results and provide additional insights are reported.health care, hospitals, dynamic programming, applications, probability, stochastic model applications

Surgical suites are a key driver of a hospital's costs, revenues, and utilization of postoperative resources such as beds. This article describes some commonly occurring operations management problems faced by the managers of surgical suites. For three of these problems, the article also provides preliminary models and possible solution approaches. Its goal is to identify open challenges to spur further research by the operations management community on an important class of problems that have not received adequate attention in the literature, despite their economic importance.

In addition to having uncertain patient arrivals, primary-care clinics also face uncertainty arising from patient choices. Patients have different perceptions of the acuity of their need, different time-of-day preferences, as well as different degrees of loyalty toward their designated primary-care provider (PCP). Advanced access systems are designed to reduce wait and increase satisfaction by allowing patients to choose either a same-day or a scheduled future appointment. However, the clinic must carefully manage patients' access to physicians' slots to balance the needs of those who book in advance and those who require a same-day appointment. On the one hand, scheduling too many appointments in advance can lead to capacity shortages when same-day requests arrive. On the other hand, scheduling too few appointments increases patients' wait time, patient-PCP mismatch, and the possibility of clinic slots going unused. The capacity management problem facing the clinic is to decide which appointment requests to accept to maximize revenue. We develop a Markov decision process model for the appointment-booking problem in which the patients' choice behavior is modeled explicitly. When the clinic is served by a single physician, we prove that the optimal policy is a threshold-type policy as long as the choice probabilities satisfy a weak condition. For a multiple-doctor clinic, we partially characterize the structure of the optimal policy. We propose several heuristics and an upper bound. Numerical tests show that the two heuristics based on the partial characterization of the optimal policy are quite accurate. We also study the effect on the clinic's optimal profit of patients' loyalty to their PCPs, total clinic load, and load imbalance among physicians.