Resource allocation is a common problem in industry
and real world wherein the demand for resources is
matched to supply while optionally optimizing some
objectives. However, setting up such problems for
efficient solving is time-consuming and error-prone
because there is a diverse set of techniques that could be
applicable depending on subtle problem variations. In
this paper, we seek to tackle this by creating a
semantic model of demand, supply and allocation aspects of
the problem. Then using code-generation utilities for
standard solvers and semantic queries, we show that
one can create new allocation problems rapidly, reuse
results for known allocation instances while setting up
new problems, discover problem characteristics quickly
and understand deep similarity among problems. The
breadth of allocation problems we consider are job-shop
scheduling, tackling forest fires, assigning people to IT
tasks in a service delivery center and evacuating people
and goods. Thus, using semantic technologies, we
are able to extend the reach of allocation techniques to
more real world applications.
By: Pranav Gupta, Biplav Srivastava
Published in: RI12008 in 2012
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