With Brexit looming, start-ups in the London ecosystem may ask themselves whether they are still in the right place for their business. Are they considering a move to the continent due to the ambiguous Brexit developments? This research analyzes the probability of international start-ups based in the London region relocating to another European entrepreneurial ecosystem. We use location decision theory and secondary data from the European Digital City Index to rank the most attractive eco-systems for the possible relocation of London-based start-ups. In addition, we interview London start-up founders asking how likely they are to leave and where they envision continuing their entrepreneurial endeavors. This study examines whether London will lose its top rank as the most attractive entrepreneurial ecosystem in Europe. We ask which of the competing ecosystems of Europe stands to gain from London’s possible loss. Our quantitative analyses show that Amsterdam is the most likely hub to benefit from any exodus. The qualitative analyses conveyed a mixture of concern and ambivalence as only three of the startups considered relocating their headquarters to another ecosystem. Six of the startups have either opened an office in another European ecosystem or are in the process of doing so. This allows them to watch and wait as they want to remain. The attractiveness of the London region, the social capital investments by team and partners, and the lack of finances to leave are the main reasons for not considering relocation of their headquarters currently.
MULTIFILE
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.
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.
