Information relaxations, duality, and convex stochastic dynamic programs

Published

Journal Article

© 2014 INFORMS. We consider the information relaxation approach for calculating performance bounds for stochastic dynamic programs (DPs). This approach generates performance bounds by solving problems with relaxed nonanticipativity constraints and a penalty that punishes violations of these nonanticipativity constraints. In this paper, we study DPs that have a convex structure and consider gradient penalties that are based on first-order linear approximations of approximate value functions. When used with perfect information relaxations, these penalties lead to subproblems that are deterministic convex optimization problems. We show that these gradient penalties can, in theory, provide tight bounds for convex DPs and can be used to improve on bounds provided by other relaxations, such as Lagrangian relaxation bounds. Finally, we apply these results in two example applications: first, a network revenue management problem that describes an airline trying to manage seat capacity on its flights; and second, an inventory management problem with lead times and lost sales. These are challenging problems of significant practical interest. In both examples, we compute performance bounds using information relaxations with gradient penalties and find that some relatively easy-to-compute heuristic policies are nearly optimal.

Full Text

Duke Authors

Cited Authors

  • Brown, DB; Smith, JE

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 62 / 6

Start / End Page

  • 1394 - 1415

Electronic International Standard Serial Number (EISSN)

  • 1526-5463

International Standard Serial Number (ISSN)

  • 0030-364X

Digital Object Identifier (DOI)

  • 10.1287/opre.2014.1322

Citation Source

  • Scopus