#### Filter Results:

- Full text PDF available (51)

#### Publication Year

1985

2017

- This year (3)
- Last 5 years (24)
- Last 10 years (48)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Dimitris Bertsimas, Vivek F. Farias, Nikolaos Trichakis
- Operations Research
- 2011

In this paper we study resource allocation problems that involve multiple self-interested parties or players, and a central decision maker. We introduce and study the price of fairness, which is the relative system efficiency loss under a “fair” allocation assuming that a fully efficient allocation is one that maximizes the sum of player utilities. We focus… (More)

- Vijay V. Desai, Vivek F. Farias, Ciamac C. Moallemi
- Operations Research
- 2012

We present a novel linear program for the approximation of the dynamic programming costto-go function in high-dimensional stochastic control problems. LP approaches to approximate DP have typically relied on a natural ‘projection’ of a well studied linear program for exact dynamic programming. Such programs restrict attention to approximations that are… (More)

We develop an approximation algorithm for a dynamic capacity allocation problem with Markov modulated customer arrival rates. For each time period and each state of the modulating process, the algorithm approximates the dynamic programming value function using a concave function that is separable across resource inventory levels. We establish via… (More)

- Dimitris Bertsimas, Vivek F. Farias, Nikolaos Trichakis
- Management Science
- 2012

T paper deals with a basic issue: How does one approach the problem of designing the “right” objective for a given resource allocation problem? The notion of what is right can be fairly nebulous; we consider two issues that we see as key: efficiency and fairness. We approach the problem of designing objectives that account for the natural tension between… (More)

- Vivek F. Farias, Srikanth Jagabathula, Devavrat Shah
- NIPS
- 2009

We visit the following fundamental problem: For a ‘generic’ model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal preference information), how may one predict revenues from offering a particular assortment of choices? This problem is central to areas… (More)

- Vijay V. Desai, Vivek F. Farias, Ciamac C. Moallemi
- Management Science
- 2012

We introduce the pathwise optimization (PO) method, a new convex optimization procedure to produce upper and lower bounds on the optimal value (the ‘price’) of a high-dimensional optimal stopping problem. The PO method builds on a dual characterization of optimal stopping problems as optimization problems over the space of martingales, which we dub the… (More)

We consider the static assortment optimization problem – a central decision problem faced by operations managers that has been widely studied in the literature. The decision deals with finding the assortment of products (from a larger universe of products) that maximizes the expected revenue subject to a constraint on the size of the assortment. More… (More)

- Vivek F. Farias, Srikanth Jagabathula, Devavrat Shah
- Management Science
- 2013

A central push in operations models over the last decade has been the incorporation of models of customer choice. Real world implementations of many of these models face the formidable stumbling block of simply identifying the ‘right’ model of choice to use. Thus motivated, we visit the following problem: For a ‘generic’ model of consumer choice (namely,… (More)

- Vijay V. Desai, Vivek F. Farias, Ciamac C. Moallemi
- NIPS
- 2009

We present a novel linear program for the approximation of the dynamic programming cost-to-go function in high-dimensional stochastic control problems. LP approaches to approximate DP naturally restrict attention to approximations that are lower bounds to the optimal cost-to-go function. Our program – the ‘smoothed approximate linear program’ – relaxes this… (More)

- Dragos Florin Ciocan, Vivek F. Farias
- Math. Oper. Res.
- 2012

The present paper develops a simple, easy to interpret algorithm for a large class of dynamic allocation problems with unknown, volatile demand. Potential applications include ad display problems and network revenue management problems. The algorithm operates in an online fashion and relies on reoptimization and forecast updates. The algorithm is robust (as… (More)