Automated Scheduling Optimisation and Planning (ASAP) Research Group
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 Current Projects

Project TitleInvestigatorsFunding SourceAmount AwardedStart DateEnd Date

Encouraging Lifelong Learning for an Inclusive and Vibrant Europe                                        

Assoc Prof  Rong Qu (PI)          

Prof Robert John (CI)

Assoc Prof Jason Atkin (CI)

European Commission- Horizon 2020 £392,000 01/10/2016 30/09/2019

The ENLIVEN (Encouraging Lifelong Learning for an Inclusive and Vibrant Europe) project is funded by the European Commission.  This is a project within a consortium of 10 European countries.  At the Automated Scheduling Planning and Optimisation (ASAP) Research Group, the School of Computer Science , the project focuses on knowledge discovery, based on analysing large databases collected from the consortium member countries, to establish knowledge bases within case based reasoning systems for policy making in lifelong learning in Europe.  The objectives include to mine knowledge from large amount of data, and to develop intelligent decision support systems to support policy makers throughout the policy making process for adult lifelong learning.

OPTIMISED: Operational Planning Tool Interfacing Manufacturing Integrated Simulations with Empirical Data

Prof Robert John (PI)                              European Commission- Horizon 2020


01/11/2015 31/10/2018

OPTIMISED is a 10-party, three year, EU-funded consortium project, comprising SME's, large companies and research institutions from across Europe.  The key aims of the project are contained in the full project title: Operational Planning Tool Interfacing Manfacturing Integrated Simulations with Empirical Data. 

OPTIMISED will develop and demonstrate a manufacturing scheduling optimisation system, which uses real-time smart sensors and big data analytics to monitor, react to and improve manufacturing performance.  Within the scope, the impact of energy management on factory planning and optimisation witll be speciaically addressed.

OPTIMISED will use the principles of Measure, Simulate, Optimise.

Predictive and Prescriptive Analytics with Optimisation for Business Intelligence

Assoc Prof Dario Landa-Silva (PI)

Assis Prof Isaac Triguero (CI)

Innovate UK £207,551 01/10/2016 30/06/2019

This KTP project seeks to enable PXtech to develop predictive and prescriptive analytics with optimisation capability to maintain their leading industry position as a Business Intelligence Solution provider through the application of advanced data analysis, data mining, machine learning, modelling and optimisation techniques. The project will develop techniques to predict future outcomes in order to prescribe long-term planning that aligns with the market needs. Predictive and prescriptive analytics including optimisation will help a business to discover patterns and develop new models to predict (resulting in deeper insights) and prescribe (resulting in better planning), in order to improve business performance.

Automated Intelligent Decision Support Using Hyper-Heuristics

Assis Prof Ender Ozcan (PI), Nelishia Pillay Royal Society £ 11,719 01/11/2015 31/10/2017

This grant will support a two-year long collaborative research project through exchange of visits with Prof Nelishia Pillay and a PhD student from the School of Mathematics, Statistics and Computer Science at University of KwaZulu-Natal. Intelligent decision support systems are playing an increasingly important role in providing solutions to various computational problems in society, industry, academia and government. However, the design, development and maintenance of heuristic methods underpinning those intelligent decision support systems are extremely challenging, time-consuming and so costly, often requiring human expert intervention. This collaboration aims to study adaptive, effective, generic, reusable and low-cost hyper-heuristics automating the heuristic design process for intelligent decision support, focusing on vehicle routing, packing and timetabling domains.

COSLE (Collaborative Optimisation in a Shared-Logistics Environment)

Assoc Prof Dario Landa-Silva (PI)

Prof Robert John (CI)

Innovate UK £ 299,482 01/06/2015 31/05/2017

This project (led by Microlise Ltd) is to develop an innovative service to enable collaboration in a shared freight transport logistics environment. We will bring location data (about where goods, vehicles, senders and consignees are at any time, etc.) and environmental data (weather, traffic, events, etc.) together with GPS, vehicle telematics, optimisation, image processing and mobile technology (including augmented reality). This innovative service will enable a shared distribution concept to reduce freight empty runs and create business opportunities for users at all levels.

Channel 4 Project

ASAP Team Channel 4 Under NDA 2013 Ongoing

An ASAP team is working with Channel 4 (a national TV company) in order to investigate the application of optimisation techniques to the scheduling of commercials during advertising breaks. The objective is to provide schedules that increase advertising effectiveness and lead to greater advertiser satisfaction.

System Tuning and Adaptation for the Heathrow Target Start-Up Approval Time (TSAT) Allocation System

Assis Prof Jason Atkin (PI)

Assis Prof Geert De Maere (CI)

NATS £ 227,300 15/12/2011 Ongoing

This research considers the problem of allocating pushback times to departing aircraft, specifying the time at which they will be given permission to push back from their allocated stand, start their engines, and commence their taxi to the runway. The aim of this research is to first predict the delay (defined as the waiting time at the stand or runway) for each departure, then to use this to calculate a pushback time such that an appropriate amount of the delay is absorbed at the stand, prior to starting the engines. A two-stage approach has been used, where the feasibility of the second stage (pushback time allocation) has to be considered within the first stage (takeoff sequencing). This problem: has a non-linear objective function with a non-convex component; involves the integration of two sequence dependent separation problems; and has separations that can vary over time. Algorithms to solve these problems had been developed by the ASAP research group. This project involves the ongoing research into the appropriate tuning and adaptation of these algorithms to handle the changing real world environment.

ASAP Research Group

The University of Nottingham
School of Computer Science
Jubilee Campus
Wollaton Road
Nottingham, NG8 1BB

telephone: +44 (0) 115 8466543