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An Embarrassingly Parallel Method for Large-Scale Stochastic Programs

In: Large Scale Optimization in Supply Chains and Smart Manufacturing

Author

Listed:
  • Burhaneddin Sandıkçı

    (University of Chicago Booth School of Business)

  • Osman Y. Özaltın

    (North Carolina State University)

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.

Suggested Citation

  • Burhaneddin Sandıkçı & Osman Y. Özaltın, 2019. "An Embarrassingly Parallel Method for Large-Scale Stochastic Programs," Springer Optimization and Its Applications, in: Jesús M. Velásquez-Bermúdez & Marzieh Khakifirooz & Mahdi Fathi (ed.), Large Scale Optimization in Supply Chains and Smart Manufacturing, pages 127-151, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-22788-3_5
    DOI: 10.1007/978-3-030-22788-3_5
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