A branch and cut framework for multi-stage stochastic programming problems under endogenous uncertainty

Matthew Colvin, Christos T. Maravelias

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

To ensure that decisions in multi-stage stochastic programming (MSSP) formulations do not anticipate future outcomes, it is necessary to introduce nonanticipativity constraints (NACs). In the case of endogenous uncertainty, NACs grow very quickly making all but the smailest multi-stage stochastic programming models computationaily intractable. To address this challenge, we first present a number of theoretical results that allow us to formulate substantially smaller and tighter MSSP models. Second, we discuss a branch and cut algorithm where necessary inequality NACs are removed from the starting formulation and added only if they are violated. Our theoretical results coupled with the proposed algorithm allow us to generate and solve problems that were previously intractable. The methods were applied to the resource-constrained scheduling of ciinical triais in the pharmaceutical research and deveiopment pipeline.

Original languageEnglish (US)
Pages (from-to)255-260
Number of pages6
JournalComputer Aided Chemical Engineering
Volume27
Issue numberC
DOIs
StatePublished - 2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Keywords

  • Branch and cut
  • Endogenous uncertainty
  • Stochastic programming

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