Zoekresultaten

An optimization-simulation closed-loop feedback framework for modeling the airport capacity management problem under uncertainty

This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for riving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.

An End-to-End Pipeline for Uncertainty Quantification and Remaining Useful Life Estimation: An Application on Aircraft Engines

Estimating the remaining useful life (RUL) of an asset lies at the heart of prognostics and health management (PHM) of many operations-critical industries such as aviation. Mod- ern methods of RUL estimation adopt techniques from deep learning (DL). However, most of these contemporary tech- niques deliver only single-point estimates for the RUL without reporting on the confidence of the prediction. This practice usually provides overly confident predictions that can have severe consequences in operational disruptions or even safety. To address this issue, we propose a technique for uncertainty quantification (UQ) based on Bayesian deep learning (BDL). The hyperparameters of the framework are tuned using a novel bi-objective Bayesian optimization method with objectives the predictive performance and predictive uncertainty. The method also integrates the data pre-processing steps into the hyperparameter optimization (HPO) stage, models the RUL as a Weibull distribution, and returns the survival curves of the monitored assets to allow informed decision-making. We vali- date this method on the widely used C-MAPSS dataset against a single-objective HPO baseline that aggregates the two ob- jectives through the harmonic mean (HM). We demonstrate the existence of trade-offs between the predictive performance and the predictive uncertainty and observe that the bi-objective HPO returns a larger number of hyperparameter configurations compared to the single-objective baseline. Furthermore, we see that with the proposed approach, it is possible to configure models for RUL estimation that exhibit better or comparable performance to the single-objective baseline when validated on the test sets.

Resilience unpacked – framing of ‘uncertainty’ and ‘adaptability’ in long-term flood risk management strategies for London and Rotterdam

Resilience is held as a promising concept to produce a paradigm shift from traditional flood control to an integration of flood risk management and spatial planning. Central ideas to the resilience narrative are that nothing is certain except uncertainty itself' and adaptability' is key to governing the unknown'. However, this terminology is far from clear, yet increasingly used, which raises the question how it is made sense of in practice. To answer this question, we examine two long-term flood risk management strategies in the London and Rotterdam region with a policy framing perspective (i.e. the English Thames Estuary 2100 Plan and the Dutch Delta Programme). In both strategies, uncertainties are a key concern, leading to adaptive strategic plans. Reconstructing the framing processes shows that the English adopted a scientific pragmatism' frame and the Dutch a joint fact-finding' frame. While this led to different governance approaches, there are also striking parallels. Both cases use established methods such as scenario planning and monitoring to manage' uncertainties. Similarly to previous turns in flood risk management, the resilience narrative seems to be accommodated in a technical-rational way, resulting in policy strategies that are maintaining the status quo rather than bringing about a paradigm shift.

Speculate – or Else! Blockchain Memes on Survival in Radical Uncertainty

How do Hofstede's Cultural Dimensions Apply When Teaching Abroad?

In this chapter, seven intercultural teaching experiences are contextualised by using the Hofstede (2011) model on cultural dimensions. The experiences are from Australia, China, France, Germany, Russia, India and Thailand. They are analysed using Hofstede’s four cultural dimensions of Power Distance, Individualism, Masculinity, and Uncertainty Avoidance. The results show that stereotypical behaviour in the teacher-student interaction is confirmed, but is also refuted in numerous instances and across the dimensions. The described cases also cover interactions between the guest lecturer and staff of the hosting universities in the seven countries. This anecdotal evidence shows more commonality with the Hofstede model than the teacher-student relationships do. Using Hofstede’s model to prepare for and carry out international guest lectures remains a useful tool. It is advised to keep an open mind and not take stereotypical behaviour for granted

COVID 19 Conspiracy Thinking Across the World

Presentatie van het manuscript 'COVID 19 Conspiracy Thinking Across the World ' door Annemarie Walter en Hugo Drochon bij REPRESENT, Research Centre for the Study of Parties and Democracy van de Universiteit van Nottingham, op 24 maart 2021.