Modelling the airline costs of delay propagation

Reactionary delays constitute nearly half of all delay minutes in Europe. A capped, multi-component model is presented for estimating reactionary delay costs, as a non-linear function of primary delay duration. Maximum Take-Off Weights, historically established as a charging mechanism, may be used to model delay costs. Current industry reporting on delay is flight-centric. Passenger-centric metrics are needed to better understand delay propagation. In ATM, it is important to take account of contrasting flight- and passenger-centric effects, caused by cancellations, for example. Costs to airlines and passenger disutility will both continue to be driven by delay relative to the original schedule.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

Diversification, size and risk: The case of bank acquisitions of nonbank financial firms

We investigate the risk effects of bank acquisitions of insurance companies and securities firms between 1991 and 2012 using a newly constructed dataset of M&A deals. We examine risk changes before and after deal announcements by decomposing risk into systematic and idiosyncratic components. Subsequently, we investigate the relationship between risk and diversification by modelling the determinants of risks. We find that bank combinations with securities firms yield higher risks than combinations with insurance companies. Bank size is an important and consistent determinant of risk whereas diversification is not. Our results inform the continuing debate on diversification versus functional separation of bank activities.

Measuring the cost of resilience

Air traffic management research lacks a framework for modelling the cost of resilience during disturbance. There is no universally accepted metric for cost resilience. The design of such a framework is presented and the modelling to date is reported. The framework allows performance assessment as a function of differential stakeholder uptake of strategic mechanisms designed to mitigate disturbance. Advanced metrics, cost- and non-cost-based, disaggregated by stakeholder sub-types, are described. A new cost resilience metric is proposed and exemplified with early test data.