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Enabling Massive Parallelism for Stochastic Optimization
SESSION: Research Poster Reception
EVENT TYPE: ACM Student Research Competition Poster, Poster, Electronic Poster
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Bernd Mohr
AUTHOR(S):Akhil Langer, Ramprasad Venkataraman, Gagan Gupta, Laxmikant Kale, Udatta Palekar, Steve Baker, Mark Surina
ROOM:WSCC North Galleria 2nd/3rd Floors
ABSTRACT: The US air fleet is tasked with the worldwide movement of cargo and personnel.
Due to a unique mixture of operating circumstances, it faces a large scale and
dynamic set of cargo movement demands with sudden changes almost being the
norm. Airfleet management involves periodically allocating aircraft to its
myriad operations, while judiciously accounting for this uncertainty to
minimize operating costs. We have formulated this allocation problem as the
optimization of a stochastic two-stage integer program.
Our work aims to enable rapid decisions via a scalable parallel implementation.
We present our initial attempts at parallelization and eventually, a
branch-and-bound approach with two-stage linear programs. This allows the
evaluation of tens of thousands of possible scenarios while converging to an
optimal integer allocation for extremely large problems. We believe that this
is an interesting and uncommon approach to harnessing tera/petascale compute
power for such problems without decomposing the linear programs further.
Bernd Mohr (Chair) - Juelich Supercomputing Centre
Akhil Langer - University of Illinois at Urbana-Champaign
Ramprasad Venkataraman - University of Illinois at Urbana-Champaign
Gagan Gupta - University of Illinois at Urbana-Champaign
Laxmikant Kale - University of Illinois at Urbana-Champaign
Udatta Palekar - University of Illinois at Urbana-Champaign