Current software development processes are expensive, laborious and error prone. They achieve adaptivity at only a glacial pace, largely through enormous human effort, forcing highly skilled engineers to waste significant time adapting many tedious implementation details. Often, the resulting software is equally inflexible, forcing users to also rely on their innate human adaptivity to find "workarounds". As the letters of support from the DAASE industrial partners demonstrate, this creates a pressing need for greater automation and adaptivity. Suppose we automate large parts of the development process using computational search. Requirements engineering, project planning and testing now become unified into a single automated activity. As requirements change, the project plans and associated tests are adapted to best suit the changes. Now suppose we further embed this adaptivity within the software product itself. Smaller changes to the operating environment can now be handled automatically. Feedback from the operating environment to the development process will also speed adaption of both the software product and process to much larger changes that cannot be handled by such in-situ adaptation. This is the new approach to software engineering DAASE seeks to create. It places computational search at the heart of the processes and products it creates and embeds adaptivity into both. DAASE will also create an array of new processes, methods, techniques and tools for a new kind of software engineering, radically transforming the theory and practice of software engineering. DAASE will develop a hyper-heuristic approach to adaptive automation. A hyper-heuristic is a methodology for selecting or generating heuristics. Most heuristic methods in the literature operate on a search space of potential solutions to a particular problem. However, a hyper-heuristic operates on a search space of heuristics. We do not underestimate the challenges this research agenda poses. However, we believe we have the team, partners and programme plan that will achieve the ambitious aim. DAASE integrates two teams of researchers from the Operational Research and Search Based Software Engineering communities. Both groups of researchers are widely regarded as world leading, having pioneered the fields of Hyper-Heuristics and Search Based Software Engineering (SBSE); the two key fields that DAASE brings together.

Congestion at major airports in the UK and across Europe and the rest of the world is a serious and growing problem. Already Heathrow faces problems occasioned by serious congestion for a major part of the day while at Gatwick demand is expected to exceed capacity for 17 hours per day by 2025. According to a Eurocontrol study, planned capacity at the 138 Eurocontrol Statistical Reference Area (ESRA) airports is expected to increase by 41% in total by 2030, with demand exceeding airport capacity by as much as 2.3 million flights (or 11%) in the most-likely forecast growth scenario. The development and deployment of airport capacity is a major societal issue engendering intense public debate in the UK and around the world. Capacity at congested airports is expressed in slots. A slot identifies a time interval on a specific date during which a carrier is permitted to use the airport infrastructure for landing or take-off. Current slot allocation procedures suffer (inter alia) from the following limitations: 1)Simplistic modelling of the objectives and operational/regulatory constraints bearing on the multiple stakeholders involved in (and affected by) the slot allocation process. 2)Insufficient capture of the interactions encountered in airport networks. 3)The use of empirical or ad hoc processes for determining (rather than computing) declared capacity which address neither the uncertainties involved in airport capacity assessment nor the complexity and size of the real-world problem, even at the single-airport level. Consequently, existing approaches to the allocation of airport capacity fail in a number of critical ways to reflect the complexities presented by the real world. This creates allocation inefficiencies which, in turn, result in poor airport capacity utilisation with significant negative impacts on airport revenues, airline operating costs, the level of service offered to passengers and the environment. There is thus a pressing need to meet the major scientific challenge of developing novel mathematical models and solution approaches to transform the airport slot allocation process and its associated outcomes. The programme grant aims to do just that for a single airport and for a network of airports. Mathematical models will be developed and analysed which consider the objectives and requirements of all stakeholders and which take account of a wide range of operational and regulatory constraints. The intrinsic complexity of the proposed programme and its large scale (especially for the case of the network-wide slot allocation) will mean that it will provide an excellent test-bed for the development of new heuristics and hyper heuristics for large scale complex scheduling problems more widely. Algorithms that will be developed and tested by this project will provide essential support for the complex large scale capacity allocation problems that arise in other types of transportation networks, including rail networks. In addition, it could extend to other types of networks that share similar problem structures, such as those in energy and telecommunications. The models and solution techniques developed will underpin the development of novel decision support systems which have the potential to make a major impact on airport operations. The research team has an internationally leading profile in the areas of mathematical modelling, heuristic development, stochastic optimization, airport slot allocation, airport management and performance assessment. It has an excellent track record of research cooperation with all categories of stakeholders. It will cooperate closely with an impressive array of leading industry stakeholders in order to make sure that the work is as cutting edge industrially as it is scientifically.