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An Embarrassingly Parallel Method for Large-Scale Stochastic Programs

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Large Scale Optimization in Supply Chains and Smart Manufacturing

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 149))

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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.

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Acknowledgement

Completed in part with resources provided by the University of Chicago Research Computing Center (RCC).

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Authors and Affiliations

  1. University of Chicago Booth School of Business, Chicago, IL, USA

    Burhaneddin Sandıkçı

  2. Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA

    Osman Y. Özaltın

Authors
  1. Burhaneddin Sandıkçı
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  2. Osman Y. Özaltın
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Correspondence to Burhaneddin Sandıkçı .

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Editors and Affiliations

  1. DecisionWare, Bogotá, Colombia

    Jesús M. Velásquez-Bermúdez

  2. School of Engineering and Science, Tecnologico de Monterrey, Santa Fe, Mexico

    Marzieh Khakifirooz

  3. Department of Industrial & Systems Engineering, Mississippi State University, Starkville, MS, USA

    Mahdi Fathi

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Sandıkçı, B., Özaltın, O.Y. (2019). An Embarrassingly Parallel Method for Large-Scale Stochastic Programs. In: Velásquez-Bermúdez, J., Khakifirooz, M., Fathi, M. (eds) Large Scale Optimization in Supply Chains and Smart Manufacturing. Springer Optimization and Its Applications, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-030-22788-3_5

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