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Integer programming and combinatorial optimization concern developing
theory, methodologies and algorithms that are used to model and solve
decision-making problems of discrete choices. For instance, industrial
problems requiring communications network design, machine/crew scheduling,
plant location, vehicle routing form important classes of optimization
problems involving integer variables. Other applications include gene
sequencing, sports scheduling, and time-tabling. Current projects in this
area at the MORE Institute include designing solution methodologies for
applications in inventory management, telecommunications network design
and logistics, as well as developing theory and methodologies for
mixed-integer programming under uncertainty.
Click
Here Click Here for a listing of
recent papers on Integer Programming and Combinatorial Optimization. |
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