The multi-path Traveling Salesman Problem with stochastic travel costs

Given a set of nodes, where each pair of nodes is connected by several paths and each path shows a stochastic travel cost with unknown distribution, the multipath Traveling Salesman Problem with stochastic travel costs aims at finding an expected minimum Hamiltonian tour connecting all nodes. Under a mild assumption on the unknown probability distribution a deterministic approximation of the stochastic problem is given. The comparison of such approximation with a Montecarlo simulation shows both the accuracy and the eciency of the deterministic approximation, with a mean percentage gap around 2% and a reduction of the computational times of two orders of magnitude

Standardizing Smart Contracts

In the evolving context of distributed ledger technologies, the standardization of smart contracts is necessary. Smart contracts are tamper-proof computer programs. Due to their security and flexibility, it is possible to exploit smart contracts in a wide variety of use cases. In particular, it could be possible to automate legally recognized contracts by leveraging smart contracts. To this extent, some standards regarding the proper management of smart contracts are surging. However, there are still many technological misconceptions regarding smart contracts. This study describes smart contracts from multiple perspectives and identifies and clarifies some of the most common misconceptions regarding smart contracts. This study also provides some guidelines and insights on the proper management of smart contracts. This study can be a valuable resource for future standards on smart contracts

A Taxonomic Analysis of Smart City Projects in North America and Europe

In recent years, the concept of a “Smart City” became central in the agenda of researchers, practitioners, and stakeholders. Although the application of information and communication technologies on city management has advanced exponentially, also other components would be needed for building a truly sustainable urban environment. Researchers from different domains debated the definition of a smart city and the conceptual variants. However, a broad view of the smart city field is still missing. This paper attempts to fill this gap by proposing a taxonomic classification of the most 105 outstanding smart city projects in Europe and North America. Collected data are then processed by statistical tools for clearly highlighting the success factors, trends and future paths in which all these projects are moving, along with different aspects (e.g., business model, purpose, industry). We then investigate the European and the North American Smart City concepts, illustrating the key role of mixed public and private partnerships in creating successful projects and the focus on the urban transportation, and freight and last-mile delivery in particular. Moreover, it emerges how the business modeling and the exploitation aspects have still low integration in the projects

The Cagliari Airport impact on Sardinia tourism: a Logit-based analysis

In the field of air transportation management, traditionally, airlines have been the main actors in the process for deciding which new flights open in a given airport, while airports acted only as the managers of the operations. The changes in the market due to the introduction of low cost companies, with consequent reduction of the airports' fares, as well as the increment of the density of regional airports in several European countries are modifying the mutual roles of airlines and airports. The final decision on new flight to be opened, in fact, is nowadays the result of a negotiation between airlines and airports. The airports must prove the sustainability on the new routes and forecast the economic impact on their catchment area. This paper contributes to advance the current state-of-the-art along two axes. From the pure transportation literature point of view, we introduce a Logit model able to predict the passengers flow in an airport when the management introduces a change in the flight schedule. The model is also able to predict the impact of this change on the airports in the surrounding areas. The second contribution is a case study on the tourist market of the Sardinia region, where we show how to use the results of the model to deduce the economic impact of the decisions of the management of the Cagliari airport on its catchment area in terms of tourists and economic growt

A Machine Learning-based DSS for mid and long-term company crisis prediction

In the field of detection and prediction of company defaults and bankruptcy, significant effort has been devoted to evaluating financial ratios as predictors using statistical models and machine learning techniques. This problem becomes crucially important when financial decision-makers are provided with predictions on which to act, based on the output of prediction models. However, research has shown that such predictors are sufficiently accurate in the short-term, with the focus mainly directed towards large and medium-large companies. In contrast, in this paper, we focus on mid- and long-term bankruptcy prediction (up to 60 months) targeting small and/or medium enterprises. The key contribution of this study is a substantial improvement of the prediction accuracy in the short-term (12 months) using machine learning techniques, compared to the state-of-the-art, while also making accurate mid- and long-term predictions (measure of the area under the ROC curve of 0.88 with a 60 month prediction horizon). Extensive computational tests on the entire set of companies in Italy highlight the efficiency and accuracy of the developed method, as well as demonstrating the possibility of using it as a tool for the development of strategies and policies for entire economic systems. Considering the recent COVID-19 pandemic, we show how our method can be used as a viable tool for large-scale policy-making

METODO E SISTEMA PER PREVEDERE IL FALLIMENTO DI UN’ENTITÀ MONITORATA

La presente invenzione riguarda un metodo (100, 200) per predire un malfunzionamento di un’entità (4). In particolare, l’entità (4) è caratterizzata da una pluralità di parametri. Il metodo (100, 200) prevede di predire (200) un malfunzionamento dell’entità (4) mediante un sistema di intelligenza artificiale (1) cui sono forniti dati relativi ai parametri dell’entità (4), in cui il sistema di intelligenza artificiale (1) è allenato mediante dei dati di allenamento. Vantaggiosamente, i dati di allenamento sono selezionati secondo i seguenti passi: a. acquisire (101-106) un insieme di dati di allenamento, laddove l’insieme di dati di allenamento comprende una pluralità di gruppi di dati di entità, ciascun gruppo di dati di entità comprendendo valori riferiti a parametri caratteristici di una rispettiva entità di allenamento, b. selezionare (107) dati di entità compresi in gruppi di dati di entità di un primo sottoinsieme di entità di allenamento dell’insieme di dati di allenamento, c. allenare (109) il sistema di intelligenza artificiale mediante i dati di entità del primo sottoinsieme di entità di allenamento selezionati, d. determinare (112) una distribuzione delle probabilità di malfunzionamento delle entità di allenamento dell’insieme di dati di allenamento, eseguendo il sistema di intelligenza artificiale (1) allenato con i dati di entità del primo sottoinsieme di entità di allenamento, e. determinare (113) i dati di allenamento selezionando dati di entità di entità di allenamento di un secondo sottoinsieme di entità di allenamento dell’insieme di dati di allenamento in modo tale che il secondo sottoinsieme di entità di allenamento presenti una distribuzione delle probabilità di malfunzionamento uguale a quella calcolata al punto d