[Opt-Net] Call for Papers: Mathematical Programming, Series B

[David Morton]

Call for Papers
Mathematical Programming, Series B
Special Issue on Topics in Stochastic Programming 

Stochastic optimization has seen recent advances with far-reaching impact involving risk measures, connections between robust optimization and stochastic programming, and applications in areas ranging from energy and natural resources to economics and finance to statistical and machine learning. Mathematical Programming, Series B invites submissions of manuscripts to a special issue on stochastic programming with a focus on distributionally robust optimization; scenario generation and reduction; and, stability of stochastic programs, stress testing, and further output analysis. Of particular interest are manuscripts on these ideas using multi-stage stochastic programming, employing risk measures, or involving important applications. The special issue will be dedicated to Jitka Dupacova, one of the founders of stochastic programming whose deep contributions continue to influence the state of the field today.  

All submissions will be reviewed according to the standards of Mathematical Programming, Series A. Please submit all manuscripts using the Mathematical Programming style files with a maximum of 25 pages. See ftp://ftp.springer.de/pub/tex/latex/svjour3/global.zip for the LaTeX macro package.

The deadline for submission of full papers is January 15, 2017 with first-round reviews expected to be completed by July 15, 2017.

Authors should submit their manuscripts via http://www.editorialmanager.com/mapr/ and select Jong-Shi Pang as the handling editor for consideration in this special issue.

Please direct questions about the special issue to the guest editors:

Tito Homem-de-Mello 
School of Business 
Adolfo Ibanez University 
Santiago, Chile 
tito.hmello at uai.cl

Milos Kopa 
Department of Probability and Mathematical Statistics
Faculty of Mathematics and Physics 
Charles University in Prague  
Prague, Czech Republic   
kopa at karlin.mff.cuni.cz

David Morton 
Department of Industrial Engineering & Management Sciences 
Northwestern University 
Evanston, Illinois, USA 
david.morton at northestern.edu

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