Optimizing Dynamic Resource Allocation

Lucas W. Krakow, Louis Rabiet, Yun Zou, Guillaume Iooss, Edwin K.P. Chong, Sanjay Rajopadhye
<span title="">2014</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cx3f4s3qmfe6bg4qvuy2cxezyu" style="color: black;">Procedia Computer Science</a> </i> &nbsp;
We present a formulation, solution method, and program acceleration techniques for two dynamic control scenarios, both with the common goal of optimizing resource allocations. These approaches allocate resources in a non-myopic way, accounting for long-term impacts of current control decisions via nominal belief-state optimization (NBO). In both scenarios, the solution techniques are parallelized for reduced execution time. A novel aspect is included in the second scenario: dynamically
more &raquo; ... g the computational resources in an online fashion which is made possible through constant aspect ratio tiling (CART).
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.procs.2014.05.115">doi:10.1016/j.procs.2014.05.115</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xe42jy2q4jg5raeokutw75jhuq">fatcat:xe42jy2q4jg5raeokutw75jhuq</a> </span>
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