A Decision Support System for the Optimization of Electric Car Sharing Stations

Electric car sharing is a mobility alternative addressing the world’s growing need for sustainability and allowing to reduce pollution, traffic congestion, and shortage of parking in cities. The positioning and sizing of car sharing stations are critical success factors for reaching many potential users. This represents a multi-dimensional challenge that requires decision makers to address the conflicting goals of fulfilling demands and maximizing profit. To provide decision support in anticipating optimal locations and to further achieve profitability, an optimization model in accordance to design science research principles is developed. The integration of the model into a decision support system (DSS) enables easy operability by providing a graphical user interface that helps the user import, edit, export, and visualize data. Solutions are illustrated, discussed, and evaluated using San Francisco as an application example. Results demonstrate the applicability of the DSS and indicate that profitable operation of electric car sharing is possible

Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications

[EN] The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems have been designed with the main goal of reducing their monetary cost while offering a specified quality of service. During the last decade, however, sustainability concepts are also being considered as a critical component of transportation systems, i.e., the environmental and social impact of transportation activities have to be taken into account when managers and policy makers design and operate modern transportation systems, whether these refer to long-distance carriers or to metropolitan areas. This paper reviews the existing work on different scientific methodologies that are being used to promote Sustainable Transportation Systems (STS), including simulation, optimization, machine learning, and fuzzy sets. This paper discusses how each of these methodologies have been employed to design and efficiently operate STS. In addition, the paper also provides a classification of common challenges, best practices, future trends, and open research lines that might be useful for both researchers and practitioners.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Torre-Martínez, MRDL.; Corlu, CG.; Faulin, J.; Onggo, BS.; Juan-Pérez, ÁA. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications. Sustainability. 13(3):1-21. https://doi.org/10.3390/su1303155112113

CASSI: Designing a Simulation Environment for Vehicle Relocation in Carsharing

Simulations offer an efficient solution to comprehensive represent operational services and to track the impact of changing systematic factors and business constraints. Carsharing services provide users with mobility services on demand. Although research has introduced strategies to optimize efforts to set up and operate such a system, they lack reusable and flexible simulation environments. For instance, carsharing research applies simulations to better understand and solve the problem of balancing vehicle supply and demand, which operators need to solve to prevent operational inefficiencies and ensure customer satisfaction. Hence, one cannot feasibly test new balancing mechanisms directly in a real-world environment. As for now, researchers have implemented simulations from scratch, which results in high development efforts and a limited ability to compare results. In this paper, we address this gap by designing a versatile carsharing simulation tool that researchers can easily use and adapt. The tool simplifies the process of modeling a carsharing system and developing operation strategies. Furthermore, we propose various system performance measures to increase the developed solutions’ comparability

Smart Grid Technologies in Europe: An Overview

The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network—the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio

Empowering citizens' cognition and decision making in smart sustainable cities

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Advances in Internet technologies have made it possible to gather, store, and process large quantities of data, often in real time. When considering smart and sustainable cities, this big data generates useful information and insights to citizens, service providers, and policy makers. Transforming this data into knowledge allows for empowering citizens' cognition as well as supporting decision-making routines. However, several operational and computing issues need to be taken into account: 1) efficient data description and visualization, 2) forecasting citizens behavior, and 3) supporting decision making with intelligent algorithms. This paper identifies several challenges associated with the use of data analytics in smart sustainable cities and proposes the use of hybrid simulation-optimization and machine learning algorithms as an effective approach to empower citizens' cognition and decision making in such ecosystemsPeer ReviewedPostprint (author's final draft