Stochastic Programming Extensions

William E. Hart, Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil, Bethany L. Nicholson and John D. Siirola
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William E. Hart: Sandia National Laboratories
Carl D. Laird: Sandia National Laboratories
Jean-Paul Watson: Sandia National Laboratories
David L. Woodruff: University of California, Davis
Gabriel A. Hackebeil: University of Michigan
Bethany L. Nicholson: Sandia National Laboratories
John D. Siirola: Sandia National Laboratories

Chapter Chapter 10 in Pyomo — Optimization Modeling in Python, 2017, pp 165-199 from Springer

Abstract: Abstract This chapter describes PySP, a stochastic programming extension to Pyomo. PySP enables the expression of stochastic programming problems as extensions of deterministic models, which are often formulated first. To formulate a stochastic program in PySP, the user specifies both the deterministic base model and the scenario tree with associated uncertain parameters in Pyomo. Given these two models, PySP provides two paths for solving the corresponding stochastic program. The first alternative involves PySP writing the extensive form and invoking a standard deterministic solver. For more complex stochastic programs, PySP includes an implementation of Rockafellar and Wets’ Progressive Hedging algorithm, which provides an effective heuristic for approximating general multi-stage, mixed-integer stochastic programs. By leveraging the combination of a high-level programming language and the embedding of the base deterministic model in that language, PySP provides completely generic and highly configurable solver implementations.

Keywords: Stochastic Program; Extensive Form; Scenario Tree; Time Stage; Stochastic Linear Program (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-58821-6_10

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