Airport runway optimization is an ongoing challenge for air traffic controllers. Since demand for air-transportation is predicted to increase, there is a need to realize additional take-off and landing slots through better runway scheduling. In this paper, we review the techniques and tools of operational research and management science that are used for scheduling aircraft landings and take-offs. The main solution techniques include dynamic programming, branch and bound, heuristics and meta-heuristics.

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Abstract. Prognostics and Health Management/Monitoring (PHM) are methods to assess the health condition and reliability of systems for the purpose of maximising operational reliability and safety. Recently, PHM systems are emerging in the automotive industry. In the commercial vehicle sector, reducing the maintenance cost and downtime while also improving the reliability of vehicle components can have a major impact on fleet performance and hence business competitiveness. Nowadays, telematics and GPS are used mainly for fleet tracking and diagnostics purposes. Increased numbers of sensors installed on commercial vehicles, advancement of data analytics and computational intelligence methods, increased capabilities for on-board data processing as well as in the cloud, are creating an opportunity for PHM systems to be deployed on commercial vehicles and hence improve the overall operational efficiency. This paper surveys and analyses the nature of PHM as well as progress and challenges towards achieving integrated and intelligent PHM systems for commercial vehicles.

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was originally conceptualised to eliminate very similar instances in data classification tasks. We adapt the method to detect hot spots from a telematics data set containing hundreds of thousands of data points indicating incident locations involving heavy goods vehicles (HGVs) across the United Kingdom. The objective is to provide HGV drivers with information regarding areas of high likelihood of incidents in order to continuously improve road safety and vehicle economy. The problem presents several challenges and constraints. An accurate view of the hot spots produced in a timely manner is necessary. In addition, the solution is required to be adaptable and dynamic, as thousands of new incidents are included in the database daily. Furthermore, the impact on hot spots after informing drivers about their existence has to be considered. Our solution successfully addresses these constraints. It is fast, robust, and applicable to all different incidents investigated. The method is also self-adjustable, which means that if the hot spots configuration changes with time, the algorithm automatically evolves to present the most current topology. Our solution has been implemented by a telematics company to improve HGV safety in the United Kingdom.