The similarity heuristic

Decision makers are often called on to make snap judgments using fast-and- frugal decisionrules called cognitive heuristics. Although early research into cognitive heuristicsemphasized their limitations, more recent research has focused on their high level ofaccuracy. In this paper we investigate the performance a subset of the representativenessheuristic which we call the similarity heuristic.Decision makers who use it judge the likelihood that an instance is a member of one category rather than another by the degree towhich it is similar to others in that category. We provide a mathematical model of theheuristic and test it experimentally in a trinomial environment. The similarity heuristic turnsout to be a reliable and accurate choice rule and both choice and response time data suggest itis also how choices are made

Introduction of technological innovations: valuation, selection and timing

In this dissertation, we tackle challenges associated with the introduction of technological innovations, namely (1) the difficulty of valuing new initiatives due to inherent uncertainties and optionalities, (2) the challenges in selecting innovations due to conflicting objectives and stakeholders, and (3) the trade-offs associated with timing the development activities and product introduction. Our research is inspired by problems faced by companies we have worked with and aims to offer practical decision making support and valuable managerial insights. Following an introduction, the second and third chapters present a multi-stakeholder, multi-objective methodology we developed for the valuation and selection of air traffic management system enhancements in relation to the Single European Sky initiative, in effort to cope with forecasted increase in traffic, whilst maintaining safety and protecting the environment. We frame this strategic decision problem and develop a mathematical model, combining quantitative and qualitative multi-criteria decision analysis techniques with large-scale optimization methods, allowing for different stakeholder views on the importance of the objectives and on the performance of the possible enhancements. The fourth chapter examines technological innovation at the firm level. We develop a stochastic dynamic programming framework for valuing managerial flexibilities, accounting for (1) uncertainty in product performance and market requirements, (2) different market environments, and (3) varying strength of competition. We introduce two dimensions of competition, namely its intensity and the competitors' capabilities. We show that the effect of competition on the value of managerial flexibility is complex. Stronger competition may increase or decrease the value of flexibility. We demonstrate that the option of delaying product launch is typically most valuable when competitors are weak, but under certain conditions, delay can offer value in more competitive environments. Our insights can help firms understand how managerial flexibility should be explored, depending on the nature and intensity of the competition they face

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Forecasting airport transfer passenger flow using realtime data and machine learning

PROBLEM DEFINITION: Airports and airlines have been challenged to improve decision making by producing accurate forecasts in real time. We develop a two-phased predictive system that produces forecasts of transfer passenger flows at an airport. In the first phase, the system predicts the distribution of individual transfer passengers’ connection times. In the second phase, the system samples from the distribution of individual connection times and produces distributional forecasts for the number of passengers arriving at the immigration and security areas. ACADEMIC/PRACTICAL RELEVANCE: To our knowledge, this work is the first to apply machine learning for predicting real-time distributional forecasts of journeys in an airport using passenger level data. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment. METHODOLOGY: The predictive system developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict distributions, moving beyond point forecasts. We also formulate a newsvendor-based resourcing problem to evaluate decisions made by applying the new predictive system. RESULTS: We show that, when compared with benchmarks, our two-phased approach is more accurate in predicting both connection times and passenger flows. Our approach also has the potential to reduce resourcing costs at the immigration and transfer security areas. MANAGERIAL IMPLICATIONS: Our predictive system can produce accurate forecasts frequently and in real time. With these forecasts, an airport’s operating team can make data-driven decisions, identify late passengers, and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger flows. Our predictive system can be generalized to other operations management domains, such as hospitals or theme parks, in which customer flows need to be accurately predicted

London Heathrow airport uses real-time analytics for improving operations

Improving airport collaborative decision making is at the heart of airport operations centers (APOCs) recently established in several major European airports. In this paper, we describe a project commissioned by Eurocontrol, the organization in charge of the safety and seamless flow of European air traffic. The project’s goal was to examine the opportunities offered by the colocation and real-time data sharing in the APOC at London’s Heathrow airport, arguably the most advanced of its type in Europe. We developed and implemented a pilot study of a real-time data-sharing and collaborative decision-making process, selected to improve the efficiency of Heathrow’s operations. In this paper, we describe the process of how we chose the subject of the pilot, namely the improvement of transfer-passenger flows through the airport, and how we helped Heathrow move from its existing legacy system for managing passenger flows to an advanced machine learning–based approach using real-time inputs. The system, which is now in operation at Heathrow, can predict which passengers are likely to miss their connecting flights, reducing the likelihood that departures will incur delays while waiting for delayed passengers. This can be done by off-loading passengers in advance, by expediting passengers through the airport, or by modifying the departure times of aircraft in advance. By aggregating estimated passenger arrival time at various points throughout the airport, the system also improves passenger experiences at the immigration and security desks by enabling modifications to staffing levels in advance of expected surges in arrivals. The nine-stage framework we present here can support the development and implementation of other real-time, data-driven systems. To the best of our knowledge, the proposed system is the first to use machine learning to model passenger flows in an airport