An Embarrassingly Parallel Method for Large-Scale Stochastic Programs

Burhaneddin Sandıkçı () and Osman Y. Özaltın ()
Additional contact information
Burhaneddin Sandıkçı: University of Chicago Booth School of Business
Osman Y. Özaltın: North Carolina State University

A chapter in Large Scale Optimization in Supply Chains and Smart Manufacturing, 2019, pp 127-151 from Springer

Abstract: Abstract Stochastic programming offers a flexible modeling framework for optimal decision-making problems under uncertainty. Most practical stochastic programming instances, however, quickly grow too large to solve on a single computer, especially due to memory limitations. This chapter reviews recent developments in solving large-scale stochastic programs, possibly with multiple stages and mixed-integer decision variables, and focuses on a scenario decomposition-based bounding method, which is broadly applicable as it does not rely on special problem structure and stands out as a natural candidate for implementation in a distributed fashion. In addition to discussing the method theoretically, this chapter examines issues related to a distributed implementation of the method on a modern computing grid. Using large-scale instances from the literature, this chapter demonstrates the potential of the method in obtaining high quality solutions to very large-scale stochastic programming instances within a reasonable time frame.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-22788-3_5

Ordering information: This item can be ordered from
http://www.springer.com/9783030227883

DOI: 10.1007/978-3-030-22788-3_5

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().