A Multi Objective Evolutionary Algorithm for Solving a Real Health Care Fleet Optimization Problem

The problem of the transportation of patients from or to some health care center given a number of vehicles of different kinds can be considered as a common Vehicle Routing Problem (VPR). However, in our particular case, the logistics behind the generation of the vehicle itineraries are affected by a high number of requirements and constraints such as the enterprise benefits, the satisfaction of the patients, and the respect of certain law regulations regarding the patients and the employees. In this work, we discuss the main aspects of the implementation of a Multi Objective Evolutionary Algorithm focused on providing a set of valid solutions to the end users of Patient Transport Services. We provide a detailed description of the process of integrating all the information on different genetic operators and multiple fitness functions. Finally, we present the preliminary results on a real-life problem from an small company that provides transport service and we compare the results that our implementation gets with the itineraries proposed by human experts

The impact of artificial intelligence in the rail industry

Artificial Intelligence (AI) is being introduced in enterprise systems due to the promising benefits that such a disruptive technology can present, even when taking into account its risks and challenges. In the rail industry, AI is being implemented to help improve train delays, reduce infrastructure and rolling stock maintenance costs, and to improve customer’s experience. In intermodal terminals, this technology helps improve passenger flow through hubs, avoids freight cargo losses and improves cargo monitoring inside the terminals. The aim of this investigation is to study the impact of Artificial Intelligence in the rail industry, and, in order to conduct this investigation, a quantitative methodology approach was used to answer three research questions. Initially, an analysis of the differences of sociodemographic factors on the knowledge about AI occurred. Posteriorly, an analysis of the influence of the benefits, risks and trust on the implementation of AI in the rail industry was conducted and, in order to expand the scope of the study outside the rail industry, an analysis of the impact of AI in the intermodal transportation systems was completed. The results show that sociodemographic differences among the respondent’s knowledge about AI exist, along with the confirmation that the factors of benefits, risks and trust influence the implementation of AI in the rail industry. Regarding intermodal transport systems, the same effects and additionally the awareness of AI were proved to influence the implementation of these kinds of systems.A Inteligência Artificial (IA) está a ser implementada em sistemas empresariais devido aos benefícios que esta tecnologia disruptiva pode apresentar, mesmo tendo em conta os riscos e desafios associados. Na indústria ferroviária, a IA está a ser aplicada para melhorar os atrasos na chegada de comboios, para reduzir os custos de manutenção tradicionais de comboios e da infraestrutura, e para melhorar a experiência do cliente. No que diz respeito ao transporte intermodal, esta tecnologia pode melhorar o fluxo de passageiros dentro dos hubs, evitar perdas de mercadoria e melhorar a sua monitorização dentro dos terminais logísticos. O objetivo desta investigação é o estudo do impacto da Inteligência Artificial na indústria ferroviária e, para tal, foi usada uma metodologia quantitativa para responder a três questões de pesquisa. Primeiramente, ocorreu uma análise das diferenças de fatores sociodemográficos no conhecimento de IA. Posteriormente, ocorreu também uma análise da influência dos benefícios, riscos e confiança na implementação da tecnologia de IA nos sistemas desta indústria e, adicionalmente, para expandir esta investigação para além da indústria ferroviária, ocorreu uma análise do impacto da IA nos sistemas de transporte intermodais. Os resultados obtidos permitem demostrar que existem diferenças sociodemográficas entre os inquiridos e existe também a confirmação da influência que os benefícios, riscos e confiança podem trazer para a implementação de IA nesta indústria. No que diz respeito aos sistemas de transporte intermodal, os mesmos efeitos já referidos e adicionalmente da noção da IA na implementação destes sistemas, são confirmados