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Detroit, more than a mere metaphor, is a striking and large-scale instance of something that has become pervasively wrong in America.
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For large-scale instances, 2L-GLNPSO is capable of producing high-quality solutions.
We implemented a parallel version of this heuristic and report computational experiments on large-scale instances.
Different diversification strategies are tested and compared on large-scale instances with up to 500 vertices.
To handle large-scale instances, rolling-horizon and relax-and-fix strategies are proposed.
Therefore, we propose a fast heuristic to solve large-scale instances within acceptable time durations.
It is further observed that GPA outperforms other approaches even for large-scale instances.
The experimental result obtained confirms the applicability on large-scale instances.
Moreover, benders decomposition is integrated with scenario reduction technique to solve the large-scale instances.
Companies already running large-scale instances on Microsoft Online include Nokia (who migrated from Notes), Coca Cola, Blockbuster, Energizer and many more.
Besides, in order to have a reliable performance evaluation process, large-scale instances are regarded in computational analysis.
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