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Dynamics in scheduled networks
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2009When studying real or virtual systems through complex networks theories, usually time restrictions are neglected, and a static structure is defined to characterize which node is connected to which other. However, this approach is oversimplified, as real networks are indeed dynamically modified by external mechanisms. In order to bridge the gap, in this
Massimiliano, Zanin +2 more
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Transportation Science, 2009
Demand stochasticity is a major challenge for the airlines in their quest to produce profit maximizing schedules. Even with an optimized schedule, many flights on departure have empty seats while others suffer a lack of seats to accommodate passengers who desire to travel.
Hai Jiang, Cynthia Barnhart
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Demand stochasticity is a major challenge for the airlines in their quest to produce profit maximizing schedules. Even with an optimized schedule, many flights on departure have empty seats while others suffer a lack of seats to accommodate passengers who desire to travel.
Hai Jiang, Cynthia Barnhart
openaire +1 more source
Dynamic Scheduling on Heterogeneous Multicores
2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2019Heterogeneous multicore systems can help adherence to design goals by providing a diverse set of hardware components to meet application requirements. Each core may also have tunable hardware that can reconfigured for different applications. However, scheduling becomes difficult in the presence of tunable hardware due to the additional constraint that ...
Ruben Vazquez +3 more
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Dynamics of propranolol dosing schedules
Clinical Pharmacology and Therapeutics, 1983Kinetic and dynamic data from 27 healthy male subjects were evaluated in a double-blind, randomized, double-crossover study to test the hypothesis that 180 mg/day propranolol twice and three times a day would provide much the same plasma levels and beta 1-blockade. The data indicate that propranolol twice rather than three times a day should be favored
J B, Coelho +8 more
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2007
As discussed in the previous chapter, offline analysis can be used to generate a schedule of DVFS state changes to minimize energy consumption, while ensuring sufficient processing cycles are available for all tasks to meet their deadlines, even under worst-case computation requirements.
Padmanabhan S. Pillai, Kang G. Shin
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As discussed in the previous chapter, offline analysis can be used to generate a schedule of DVFS state changes to minimize energy consumption, while ensuring sufficient processing cycles are available for all tasks to meet their deadlines, even under worst-case computation requirements.
Padmanabhan S. Pillai, Kang G. Shin
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Dynamic Journeying in Scheduled Networks
IEEE Transactions on Intelligent Transportation Systems, 2013We study a dynamic-journey planning problem for multimodal transportation networks. The goal is to find a journey, possibly involving transfers between different transport modes, from a given origin to a given destination within a specified time horizon.
Hakula, Harri, Hame, Lauri
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Dynamic leading-edge flap scheduling
13th Applied Aerodynamics Conference, 1995We report on an experimental determination of a leading-edge flap schedule used to maintain attached flow during arbitrary dynamic pitch motions of a NACA 0009 airfoil. The airfoil could be made to dynamically pitch about its midchord, and was equipped with a 20% leading-edge flap and 27% trailing-edge flap, both of which could be independently and ...
R. M. Rennie, E. J. Jumper
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1999
In parallel and distributed computing scheduling low level tasks on the available hardware is a fundamental problem. Traditionally, one has assumed that the set of tasks to be executed is known beforehand. Then the scheduling constraints are given by a precedence graph. Nodes represent the elementary tasks and edges the dependencies among tasks.
Andreas Jakoby +2 more
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In parallel and distributed computing scheduling low level tasks on the available hardware is a fundamental problem. Traditionally, one has assumed that the set of tasks to be executed is known beforehand. Then the scheduling constraints are given by a precedence graph. Nodes represent the elementary tasks and edges the dependencies among tasks.
Andreas Jakoby +2 more
openaire +1 more source