In online retailing, core operational questions include deciding on the assortment of products that should be displayed to incoming customers and how to adjust these assortments and prices in real-time according to current capacity and observed demand. In this dissertation, we explore three new developments in online retailing and develop models and techniques for revenue management in these settings. The underlying goal in each of the chapters is to develop computationally efficient and provably good algorithms. First, we consider a revenue management model over a single flight leg, where customers are allowed to lock an available fare. The goal is to find a policy to decide which set of fare classes to make available at each time period to maximize the total expected revenue. We develop an approximate policy that is guaranteed to obtain at least half of the optimal total expected revenue. Second, we consider dynamic assortment problems with reusable products, in which each arriving customer chooses a product within an offered assortment, uses the product for a random duration of time, and returns the product back to the firm to be used by other customers. The goal is to find a policy for deciding on the assortment to offer to each customer so that the total expected revenue over a finite selling horizon is maximized. We present an approximate policy that is guaranteed to obtain at least half of the optimal total expected revenue. The policy is based on constructing linear approximations to the optimal value functions. Lastly, we examine the revenue-utility assortment problem with the goal of finding an assortment that maximizes a linear combination of the expected revenue of the firm and the expected utility of the customer. This criterion captures the tradeoff between the firm-centric objective of maximizing the expected revenue and the customer-centric objective of maximizing the expected utility. When customers choose according to the multinomial logit model, and there is a constraint on the offered assortments characterized by a totally unimodular matrix, finding the optimal assortment requires solving a nonconcave optimization problem. We provide an efficient algorithm for finding the welfare optimal assortment by solving a parametric linear program and testing a polynomial size collection of candidate assortments.