.

ADVANCE

Responsible KEG Investigator:
Dr Aniko Ekart

Co-Investigator(s):

Project Collaborator(s):

  • MTA SZTAKI, Hungary
  • University of Groningen, the Netherlands
  • Technology Transfer Systems, Italy
  • Palletways, United Kingdom
Type of Project: STREP

Funder: European Comission

Date Commissioned: 10/2010
Date Completed: 09/2013

Project Summary
The three-year 1.98 Million Euro FP7 ADVANCE project started in October 2010 with the kick-off meeting at Aston University. The project is lead by the Computer and Automation Research Institute of the Hungarian Academy of Sciences, Hungary, has academic partners Aston University and University of Groningen, the Netherlands and industrial partners Technology Transfer Systems, Italy and Palletways, United Kingdom. The Aston team consists of Anikó Ekárt, Christopher Buckingham, and Philip Welch, all from Computer Science, School of Engineering and Applied Science. Philip joined the group in October as a full time research fellow on the project, after previously working in the logistics industry, which is the focus of the Advance project. The launch had fourteen participants from the five partner institutions in Hungary, the Netherlands, Italy and UK and was followed by the two user workshops at Palletways, UK.
One of the main aims of the project is to increase efficiency of lorry usage by reducing the "delivery of air": transport industry parlance for lorries running with less than full capacity. Logistics networks typically accumulate OVER 1 BILLION new items of information each month (customer orders, pallets, trailer images, postcodes, depot data, GPS tracking of vehicles, etc.), generated every minute of every day by thousands of pallets travelling on hundreds of trailers for more than one million customers scattered across hundreds of thousands of postcodes, each with multiple different service requirements. Every second, thousands of data items come on stream at any point of the network and need analysis to guide short-term decisions about lorry deployment (within minutes) as well as longer term plans for carrying capacity.
We are developing a decision support system to help make management, operations and planning decisions that may be rapidly changing and not easily specified in advance. The ADVANCE decision-support engine will enable strategic planning coupled with instant decision making to provide vision in a blizzard of data. Predictive analysis will predict future trends and events based on current and historical facts. The patterns and dependencies that exist in the 50 million or more data elements created daily in logistics networks can only be meaningfully processed by intelligent data mining approaches linked to strategic decision making based on longer term analyses of billions of pieces of information. The ADVANCE software will have the capacity to analyse massive data sets for long term planning as well as rapidly processing huge amounts of new data in real time. It will provide a dual perspective on transport requirements and decision making dependent on the latest snapshot information and the best higher-level intelligence.

See the ADVANCE project website for more details.

Employable Graduates; Exploitable Research