A comparative study of decomposition algorithms for stochastic combinatorial optimization Academic Article

abstract

• This paper presents comparative computational results using three decomposition algorithms on a battery of instances drawn from two different applications. In order to preserve the commonalities among the algorithms in our experiments, we have designed a testbed which is used to study instances arising in server location under uncertainty and strategic supply chain planning under uncertainty. Insights related to alternative implementation issues leading to more efficient implementations, benchmarks for serial processing, and scalability of the methods are also presented. The computational experience demonstrates the promising potential of the disjunctive decomposition (D 2) approach towards solving several large-scale problem instances from the two application areas. Furthermore, the study shows that convergence of the D 2 methods for stochastic combinatorial optimization (SCO) is in fact attainable since the methods scale well with the number of scenarios. 2007 Springer Science+Business Media, LLC.

authors

• Ntaimo, Lewis

published proceedings

• COMPUTATIONAL OPTIMIZATION AND APPLICATIONS

author list (cited authors)

• Ntaimo, L., & Sen, S.

citation count

• 24

complete list of authors

• Ntaimo, Lewis||Sen, Suvrajeet

publication date

• July 2008

publisher

• Springer Nature  Publisher

published in

• Computational Optimization and Applications  Journal

keywords

• Disjunctive Decomposition
• Stochastic Mixed-integer Programming
• Stochastic Server Location
• Strategic Supply Chain Planning

Digital Object Identifier (DOI)

• 10.1007/s10589-007-9085-1

start page

• 299

end page

• 319

volume

• 40

issue

• 3

URL

• http://dx.doi.org/10.1007/s10589-007-9085-1