NEO Cities 2016

Welcome to the NEO Cities 2016 homepage! The NEO Cities 2016 will be held jointly with the NEO 2016:

  NEO 2016: September 20 + 21, 2016 4th international workshop on numerical and evolutionary optimization. Participation at the NEO 2016 will allow for (passive) attendance of the NEO Cities 2016.
  NEO Cities 2016: September 22-24, 2016 (welcome reception and dinner on Sept. 21)
NEO spin-off with a focus on the optimization aspects in future and smart cities. The NEO Cities 2016 is funded by the Newton Fund of the British Council. Active participation follows by invitation. See here for more details and the call for participation.

In a world where information and communication technologies are pervading the perception and the experience of human living, cities are evolving towards a more functional scope, where the quality of life of their citizens and the quality of the services provided play a central role in their design and planning. With the term smart city, or ubiquitous city, is intended a place where multiple technologies are integrated to manage, monitor and improve city assets for a better living experience. Data collected from citizens and devices are processed and analyzed to gather information and knowledge about the city and its environment in a continuous learning process to get the city evolving closer to resident’s needs.

Cities are very dynamic environments, breeding place for innovation, opportunities and research but also sources of great challenges.  Some of the open questions are for example: how can cities become more resilient? How can you guarantee privacy ethic and security to their citizens? How can cities become more energy sustainable? How can the existing urban fabric be made smarter for the future?

Numerical techniques from optimisation and data analytics can help addressing some of the open questions and shaping the cities of the future. Continuous assimilation of sensor data, as for example the ones coming from traffic lights, park meters, cameras, weather station, used to infer knowledge about traffic and air monitoring, requires advanced techniques for the analysis and processing of large volume of data. The optimisation of railways network and energy grids requires large scale combinatorial algorithms, the optimisation of a building for energy efficiency in a cost effective manner, that satisfies the occupant needs requires multiobjective optimisation strategies. In this scenario the treatment of uncertainties in the optimisation model and in the data processed is crucial to deliver sound and resilient solutions.

Flyer (pdf)