8,922 research outputs found
Solving a robust airline crew pairing problem with column generation
In this study, we solve a robust version of the airline crew pairing problem. Our concept of robustness was partially shaped during our discussions with small local airlines in Turkey which may have to add a set of extra flights into their schedule at short notice during operation. Thus, robustness in this case is related to the ability of accommodating these extra flights at the time of operation by disrupting the original plans as minimally as possible. We focus on the crew pairing aspect of robustness and prescribe that the planned crew pairings incorporate a number of predefined recovery solutions for each potential extra flight. These solutions are implemented only if necessary for recovery purposes and involve either inserting an extra flight into an existing pairing or partially swapping the flights in two existing pairings in order to cover an extra flight. The resulting mathematical programming model follows the conventional set covering formulation of the airline crew pairing problem typically solved by column generation with an additional complication. The model includes constraints that depend on the columns due to the robustness consideration and grows not only column-wise but also row-wise as new columns are generated. To solve this dicult model, we propose a row and column generation approach. This approach requires a set of modifications to the multi-label shortest path problem for pricing out new columns (pairings) and various mechanisms to handle the simultaneous increase in the number of rows and columns in the restricted master problem during column generation. We conduct computational experiments on a set of real instances compiled from a local airline in Turkey
SWAMP: Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
Given the ever-increasing size of modern neural networks, the significance of
sparse architectures has surged due to their accelerated inference speeds and
minimal memory demands. When it comes to global pruning techniques, Iterative
Magnitude Pruning (IMP) still stands as a state-of-the-art algorithm despite
its simple nature, particularly in extremely sparse regimes. In light of the
recent finding that the two successive matching IMP solutions are linearly
connected without a loss barrier, we propose Sparse Weight Averaging with
Multiple Particles (SWAMP), a straightforward modification of IMP that achieves
performance comparable to an ensemble of two IMP solutions. For every
iteration, we concurrently train multiple sparse models, referred to as
particles, using different batch orders yet the same matching ticket, and then
weight average such models to produce a single mask. We demonstrate that our
method consistently outperforms existing baselines across different sparsities
through extensive experiments on various data and neural network structures
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
- …