90B15 Network models, stochastic
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- Column Generation (1)
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- Robust Optimization (1)
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- Tail Assignment (1)
- affine adjustable robust counterparts (1)
- affine routing (1)
- cycle decomposition (1)
Institute
We present a comprehensive theory for analysis and understanding of transition events between an initial set A and a target set B for general ergodic finite-state space Markov chains or jump processes, including random walks on networks as they occur, e.g., in Markov State Modelling in molecular dynamics. The theory allows us to decompose the probability flow generated by transition events between the sets A and B into the productive part that directly flows from A to B through reaction pathways and the unproductive part that runs in loops and is supported on cycles of the underlying network. It applies to random walks on directed networks and nonreversible Markov processes and can be seen as an extension of Transition Path Theory. Information on reaction pathways and unproductive cycles results from the stochastic cycle decomposition of the underlying network which also allows to compute their corresponding weight, thus characterizing completely which structure is used how often in transition events. The new theory is illustrated by an application to a Markov State Model resulting from weakly damped Langevin dynamics where the unproductive cycles are associated with periodic orbits of the underlying Hamiltonian dynamics.
Affinely-Adjustable Robust Counterparts provide tractable alternatives to (two-stage) robust
programs with arbitrary recourse. We apply them to robust network design with polyhedral demand
uncertainty, introducing the affine routing principle.
We compare the affine routing to the well-studied static and dynamic routing schemes for robust
network design.
All three schemes are embedded into the general framework of two-stage network design with recourse.
It is shown that affine routing can be seen as a generalization of the widely used static
routing still being tractable and providing cheaper solutions. We investigate properties on the
demand polytope under which affine routings reduce to static routings and also develop conditions on
the uncertainty set leading to dynamic routings being affine. We show however that affine routings
suffer from the drawback that (even totally) dominated demand vectors are not necessarily supported
by affine solutions. Uncertainty sets have to be designed accordingly. Finally, we present
computational results on networks from SNDlib. We conclude that for these instances the
optimal solutions based on affine routings tend to be as cheap as optimal network designs for
dynamic routings. In this respect the affine routing principle can be used to approximate the cost
for two-stage solutions with free recourse which are hard to compute.
Robust Tail Assignment
(2010)
We propose an efficient column generation method to minimize the probability of delay propagations along aircraft rotations. In this way, delay resistant schedules can be constructed. Computational results for large-scale real-world problems demonstrate substantial punctuality improvements. The method can be generalized to crew and integrated scheduling problems.