7,230 research outputs found
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Case study: An intelligent decision-support system
© 2005 IEEE.The explosive growth in decision-support systems over the past 30 years has yielded numerous "intelligent" systems that have often produced less-than-stellar results. In addition to generating data that users can't immediately apply to their tasks, such systems are often static, rendering them unable to respond to the dynamic nature of both business and the larger world. In this case study, the authors describe a thorny logistical problem: recommending the best distribution for used cars among various automobile auctions. They solved this problem by combining prediction, optimization, and adaptation techniques into one integrated system that has generated impressive profits for a large auto manufacturer.This article is part of a special issue on transportation and logistics.Zbigniew Michalewicz, Martin Schmidt, Matthew Michalewicz, and Constantin Chiria
Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids
Electric vehicle fleets and smart grids are two growing technologies. These technologies
provided new possibilities to reduce pollution and increase energy efficiency.
In this sense, electric vehicles are used as mobile loads in the power grid. A distributed
charging prioritization methodology is proposed in this paper. The solution is based
on the concept of virtual power plants and the usage of evolutionary computation
algorithms. Additionally, the comparison of several evolutionary algorithms, genetic
algorithm, genetic algorithm with evolution control, particle swarm optimization, and
hybrid solution are shown in order to evaluate the proposed architecture. The proposed
solution is presented to prevent the overload of the power grid
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
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Chapter 2 - Data-Driven Energy Efficient Driving Control in Connected Vehicle Environment
Non-emergency patient transport services planning through genetic algorithms
Non-emergency Patient Transport Services (PTS) are provided by ambulance companies for patients who do not require urgent and emergency transport. These patients require transport to or from a health facility like a hospital, but due to clinical requirements are unable to use private or public transport. This task is performed nowadays mainly by human operators, spending a high amount of time and resources to obtain solutions that are suboptimal in most cases. To overcome this limitation, in this paper we present NURA (Non-Urgent transport Routing Algorithm), a novel algorithm aimed at ambulance route planning. In particular, NURA relies on a genetic algorithm to explore the solution space, and it includes a scheduling algorithm to generate detailed routes for ambulances. Experimental results show that NURA is able to outperform human experts in several real scenarios, reducing the time spent by patients in ambulances during non-emergency transportations, increasing ambulance usage, while saving time and money for ambulance companies
Multi-Criteria Decision Making in Complex Decision Environments
In the future, many decisions will either be fully automated or supported by autonomous system. Consequently, it is of high importance that we understand how to integrate human preferences correctly. This dissertation dives into the research field of multi-criteria decision making and investigates the satellite image acquisition scheduling problem and the unmanned aerial vehicle routing problem to further the research on a priori preference integration frameworks. The work will aid in the transition towards autonomous decision making in complex decision environments. A discussion on the future of pairwise and setwise preference articulation methods is also undertaken. "Simply put, a direct consequence of the improved decision-making methods is,that bad decisions more clearly will stand out as what they are - bad decisions.
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