Abstract

International audience; This paper describes a study to develop an intelligent air traffic ground control operation system that is able to integrate initiatives from both controllers andautonomous vehicles. We use a multi-agent approach to optimize dynamically a routing and scheduling task while taking into account real-time human suggestions. Weexpect that such mixed-initiative systems combined with autonomous vehicles will permit reduction of congestion and fuel consumption in large airports.

Abstract

the share of renewable electricity generation is expected to rise and plug-in (hybrid) electrical vehicles (PHEVs) are making inroads in the transport sector, there is a need to align increasing electricity demand with unpredicatable generation. Demand Side Management (DSM) is widely seen as one enabling technology and while many such mechanisms have been proposed, challenges remain regarding instantaneous control and communication. To address these, we propose an agent based dual coordination mechanism, which controls a fleet of PHEVs on two operation levels. The market operation level uses an existing timeslot-based algorithm to optimize energy allocation for discrete time steps. The real-time level translates this allocation into event-based control actions for a highly responsive environment, wherein a limited number of control messages can be exchanged. ispartof: pages:1-2 ispartof: Proceedings of the Fourth International Workshop on Agent Technologies for Energy Systems (ATES2013) pages:1-2 ispartof: Fourth International Workshop on Agent Technologies for Energy Systems location:Minnesota date:6 May - 6 May 2013 status: published

Abstract

We investigate the hypothesis that long-term memory in populations of agents can lead to counterproductive emergent properties at the system level. Our investigation is framed in the context of a discrete, one-dimensional road-traffic congestion model: we investigate the influence of simple cognition in a population of rational commuter agents that use memory to optimise their departure time, taking into account congestion delays on previous trips. Our results differ from the well-known minority game in that crowded slots do not carry any explicit penalty. We use Markov chain analysis to uncover fundamental properties of this model and then use the gained insight as a benchmark. Then, using Monte Carlo simulations, we study two scenarios: one in which "myopic” agents only remember the outcome (delay) of their latest commute, and one in which their memory is practically infinite. We show that there exists a trade-off, whereby myopic memory reduces congestion but increases uncertainty, while infinite memory does the opposite. We evaluate the performance against the optimal distribution of departure times (i.e. where both delay and uncertainty are minimised simultaneously). This optimal but unstable distribution is identified using a genetic algorithm. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.