Faculty & Research

Rene Caldentey

Rene Caldentey

Professor of Operations Management

René Caldentey is a Professor of Operations Management. His primary research interests include stochastic modeling with applications to revenue and retail management, queueing theory, and finance. He has been published in numerous journals including Advances in Applied Probability, Econometrica, Management Science, Mathematics of Operations Research, M&SOM, Operations Research and Queueing Systems. He has served on the editorial board of Management Science, M&SOM, Operations Research, Production and Operations Management and the Journal of Systems and Engineering (in Spanish).

Prior to joining Booth, Caldentey was a professor in the department of Information, Operations and Management Science at New York University Stern School of Business. Before joining NYU Stern in 2001, he worked for the Chilean Central Bank and taught at the University of Chile and The Sloan School of Management at Massachusetts Institute of Technology (MIT).

Professor Caldentey received his Master of Arts in civil industrial engineering from the University of Chile and his Doctor of Philosophy in operations management from MIT.


2019 - 2020 Course Schedule

Number Title Quarter
36600 Workshop in Operations/Management Science 2020 (Winter)
36902 Dynamic Programming/Markov Decision Processes 2020 (Spring)
40000 Operations Management: Business Process Fundamentals 2019 (Fall)
40108 Revenue Management 2020 (Spring)

New: On the Optimal Design of a Bipartite Matching Queueing System
Date Posted: Mar  23, 2019
We consider a multi-class multi-server queueing system and study the problem of designing an optimal matching topology (or service compatibility structure) between customer classes and servers under a FCFS-ALIS service discipline. Specifically, we are interested in finding matching topologies that optimize --in a Pareto efficiency-- sense the trade-off between two competing objectives: (i) minimizing customers' waiting time delays and (ii) maximizing matching rewards generated by pairing customers and servers. Our analysis of the problem is divided in three main parts.

First, under heavy-traffic conditions, we show that any bipartite matching system can be partitioned into a collection of complete resource pooling (CRP) subsystems, which are interconnected by means of a direct acyclic graph (DAG). We show that this DAG together with the aggregate service capacity on each CRP component fully determine the vector of steady-state waiting times. In particular, we show that the ...

New: Inventory Policies and Information Sharing: An Efficient Frontier Approach
Date Posted: Mar  04, 2019
We consider a two-tier inventory management system with one retailer and one supplier. The retailer serves a demand driven by a stationary moving average process (of possibly infinite order) and places periodic inventory replenishment orders to the supplier. In this setting, we study the value of information sharing and its impact on the retailer’s optimal ordering strategy. We argue that information sharing affects performance through two key cost drivers: (i) on-hand inventory variability and (ii) replenishment order variability. We characterize a “Pareto frontier” between these two sources of variability by identifying optimal inventory replenishment strategies that trade-off one type of variability for the other in a cost efficient way. For the case in which the retailer is able to share her complete demand history, we provide a full characterization of the efficient frontier, as well as of an optimal replenishment policy. On the other hand, when the retailer is not able (or ...

New: Learning Customer Preferences from Personalized Assortments
Date Posted: Aug  07, 2018
A company wishes to identify the most popular version of a product from a menu of alternative options. Unaware of customers' true preferences, the company relies on a feedback system that allows potential buyers to provide feedback on their preferred versions. Under a general ranking-based choice model framework, we study how to dynamically individualize the set of versions shown to each customer for them to provide feedback on. This allows the company to identify the top-ranked version with a fixed probabilistic confidence level using a minimal amount of feedback. We prove an instance-specific lower bound on the sample complexity and propose a sampling policy (Myopic Tracking Policy), which is both asymptotically optimal and intuitive to implement. Our methodology draws on previous work in the sequential design of experiments and best arm identification. We illustrate our methodology using a special class of choice models based on Luce's (1959) attraction model and provide a simple ...

New: Crowdvoting the Timing of New Product Introduction
Date Posted: Jan  27, 2016
Launching new products into the marketplace is a complex and risky endeavor that companies must continuously undertake. As a result, it is not uncommon to witness major rms discontinuing a product shortly after its introduction. In this paper, we consider a seller who has the ability to fi rst test the market and gather demand information before deciding whether or not to launch a new product. In particular, we consider the case in which the seller sets up an online voting system that potential customers can use to provide feedback about their willingness to buy the new product. This voting system has the potential of offering a win-win situation whereby a consumer who votes hopes to influence the seller's final assortment, while at the same time these votes and their pace bene fit the seller as they provide valuable information to better forecast demand. We investigate the optimal design of such a crowdvoting system and its implications on the seller's commercialization strategy.

REVISION: Intertemporal Pricing under Minimax Regret
Date Posted: Apr  29, 2015
We consider the pricing problem faced by a monopolist who sells a product to a population of consumers over a finite time horizon. Customers are heterogeneous along two dimensions: (i) willingness-to-pay for the product and (ii) arrival time during the selling season. We assume that the seller knows only the support of the customers' valuations and do not make any other distributional assumptions about customers' willingness-to-pay or arrival times. We consider a robust formulation of the seller's pricing problem which is based on the minimization of her worst-case regret, a framework first proposed by Bergemann and Schlag (2008) in the context of static pricing. We consider two distinct cases of customers' purchasing behavior: myopic and strategic customers. For both of these cases, we characterize optimal price paths. For myopic customers, the regret is determined by the price at a critical time. Depending on the problem parameters, this critical time will be either the end of ...

New: Online Auction and List Price Revenue Management
Date Posted: Oct  29, 2008
We analyze a revenue management problem in which a seller facing a Poisson arriving stream of customers operates an online multiunit auction. Customers have an alternative list price channel where to get the product from. We consider two variants of this problem: In the first one, the list price is an external channel run by another firm. In the second variant, the seller manages simultaneously both the auction and the list price channels. Each consumer, trying to maximize his own surplus, ...

An Overview of Pricing Models for Revenue Management
Date Posted: Apr  24, 2008
In this paper, we examine the research and results of dynamic pricing policies and their relation to Revenue Management. The survey is based on a generic Revenue Management problem in which a perishable and non-renewable set of resources satisfy stochastic price-sensitive demand processes over a finite period of time. In this class of problems, the owner (or the seller) of these resources uses them to produce and offer a menu of final products to the end customers. Within this context, we ...

Optimal Control and Hedging of Operations in the Presence of Financial Markets
Date Posted: Apr  24, 2008
We consider the problem of dynamically hedging the profits of a corporation when these profits are correlated with returns in the financial markets. In particular, we consider the general problem of simultaneously optimizing over both the operating policy and the hedging strategy of the corporation. We discuss how different informational assumptions give rise to different types of hedging and solution techniques. Finally, we solve some problems commonly encountered in operations management to ...

New: Insider Trading With Stochastic Valuation
Date Posted: May  15, 2007
This paper studies a model of strategic trading with asymmetric information of an asset whose value follows a Brownian motion. An insider continuously observes a signal that tracks the evolution of the asset fundamental value. At a random time a public announcement reveals the current value of the asset to all the traders. The equilibrium has two regimes separated by an endogenously determined time T. In [0,T), the insider gradually transfers her information to the market and the market's ...

Dynamic Pricing for Non-Perishable Products with Demand Learning
Date Posted: Feb  26, 2006
A retailer is endowed with a finite inventory of a non-perishable product. Demand for this product is driven by a price-sensitive Poisson process that depends on an unknown parameter, theta; a proxy for the market size. If theta is high then the retailer can take advantage of a large market charging premium prices, but if theta is small then price markdowns can be applied to encourage sales. The retailer has a prior belief on the value of theta which he updates as time and available ...