We consider the dynamic pricing and rationing policy of a firm facing strategic customers under the influence of shortage effect. We provide conditions under which it is optimal for the firm to ration. We also identify the necessary and sufficient conditions for the existence of steady state. We also characterize the firm’s pricing and rationing policy under this steady state.

The issue of allocating capacity to accommodate emergent surgery cases while scheduling elective patients has major policy implications for Level-1 trauma centers including most large academic medical centers. This is because operating rooms (ORs) are the greatest source of revenues for hospitals, while also being the largest cost centers. However, scheduling ORs, especially at level-1 trauma hospitals, is challenging due to significant uncertainty in the arrivals of patients requiring emergent surgery.

The introduction of on-demand ride-hailing platforms totally changed the way people commute. In recent years, several firms entered this market to directly compete with traditional taxi companies. Some of these online platforms offer a carpooling service in which several passengers heading in the same direction can share a ride by being efficiently matched to an available vehicle. Examples of such services in NYC include uberPOOL, Lyft Line and Via.

Portfolio risk measurement under the nested setting is a challenging computational problem, and has received increasing attention in recent years. This nested setting often requires mark-to-market reevaluation of the portfolio for a large number of possible scenarios of risk factors up to a future time horizon. When closed-form formula is not available, reevaluation may require intensive simulations that are time consuming. This paper aims to develop a new simulation method that is computationally efficient for measurement of conditional Value-at-Risk (CVaR) for the portfolio.

We consider a new ranking and selection problem in which the performance of each alternative depends on some observable random covariates. The best alternative is thus not constant but depends on the values of the covariates. Assuming a linear model that relates the mean performance of an alternative and the covariates, we design selection procedures producing policies that represent the best alternative as a function in the covariates.

We consider a dynamic pricing problem where the firm tries to maximize the profit upon selling a product over the course of T periods. We do not assume decision maker’s foreknowledge on the demand. Traditionally, the cost is fixed and the problem may be formulated as a multi armed bandit problem, which is known to have an O(log T) lower bound on the expected regret. In this paper, we consider a setting where the cost may change over time and the optimal price is thus a function of the cost. We develop an upper confidence bound like (UCB-Like) algorithm to solve the problem.

We consider a dynamic pricing problem where the firm tries to maximize the profit upon selling a product over the course of T periods. We do not assume decision maker’s foreknowledge on the demand. Traditionally, the cost is fixed and the problem may be formulated as a multi armed bandit problem, which is known to have an O(log T) lower bound on the expected regret. In this paper, we consider a setting where the cost may change over time and the optimal price is thus a function of the cost. We develop an upper confidence bound like (UCB-Like) algorithm to solve the problem.

In this paper, we study the dynamic pricing and availability decisions of a firm that repeatedly introduces new generations of a product over time. Customers are strategic and are affected by two types of scarcity effects----direct scarcity effect and relative scarcity effect. Firm-induced scarcity is never optimal in the absence of scarcity effects but it can be optimal when customers are affected by scarcity effects. We study the long-run optimal policy of the firm and characterize when firm-induced scarcity is optimal.