Motivated by practices in customer contact centers, we consider a system that offers two modes of service: real-time and postponed with a delay guarantee. Customers are informed of anticipated delays and select their preferred option of service. The resulting system is a multiclass, multiserver queueing system with state-dependent arrival rates.
We analyze an airline yield management problem on a single flight leg in which the buyers' choice of fare classes is modeled explicitly. The choice model we use is very general and includes a wide range of discrete choice models of practical interest. The optimization problem is to find, at each point in time, the optimal subset of fare classes to offer. We characterize the optimal policy for this problem exactly and show it has a surprisingly simple form.
We consider a Markovian model of a multiclass queueing system in which a single large pool of servers attends to the various customer classes. Customers waiting to be served may abandon the queue, and there is a cost penalty associated with such abandonments. Service rates, abandonment rates, and abandonment penalties are generally different for the different classes. The problem studied is that of dynamically scheduling the various classes.
We consider a joint inventory-pricing problem in which buyers act strategically and bid for units of a firm's product over an infinite horizon. The number of bidders in each period as well as the individual bidders' valuations are random but stationary over time. There is a holding cost for inventory and a unit cost for ordering more stock from an outside supplier. Backordering is not allowed. The firm must decide how to conduct its auctions and how to replenish its stock over time to maximize its profits.
This paper considers an overbooking problem with multiple reservation and inventory classes, in which the multiple inventory classes may be used as substitutes to satisfy the demand of a given reservation class (perhaps at a cost). The problem is to jointly determine overbooking levels for the reservation classes, taking into account the substitution options. Such problems arise in a variety of revenue management contexts, including multicabin aircraft, back-to-back scheduled flights on the same leg, hotels with multiple room types, and mixed-vehicle car rental fleets.
The Theory and Practice of Revenue Management is a book that comprehensively covers theory and practice of the entire field, including both quantity and price-based RM, as well as significant coverage of supporting topics such as forecasting and economics. The authors believe such a comprehensive approach is necessary to fully understand the subject. A central objective of the book is to unify the various forms of RM and to link them closely to each other and to the supporting fields of statistics and economics.
This paper develops a stochastic general equilibrium model for an oligopoly, in which all inventory constraint parameters are endogenously determined. We propose several systems of demand processes whose distributions are dunctions of all retailers' prices and all retailers' service levels. We proceed with the investigation of the equilibrium behavior of infinite-horizon models for industries facing this type of generalized competition, under demand uncertainty.
Previously we demonstrated a method, Quantized Surface Complementarity Diversity (QSCD), of defining molecular diversity by measuring shape and functional complementarity of molecules to a basis set of theoretical target surfaces [Wintner E.A. and Moallemi C.C., J. Med. Chem., 43 (2000) 1993]. In this paper we demonstrate a method of mapping actual protein pockets to the same basis set of theoretical target surfaces, thereby allowing categorization of protein pockets by their properties of shape and functionality.
We develop a protocol for optimizing dynamic behavior of a network of simple electronic components, such as a sensor network, an ad hoc network of mobile devices, or a network of communication switches. This protocol requires only local communication and simple computations which are distributed among devices. The protocol is scalable to large networks. As a motivating example, we discuss a problem involving optimization of power consumption, delay, and buffer overflow in a sensor network. Our approach builds on policy gradient methods for optimization of Markov decision processes.