MODELOS SOSTENIBLES Y ANALITICA DEL TRASPORTE EN CIUDADES INTELIGENTES

PID2019-111100RB-C22

Nombre agencia financiadora Agencia Estatal de Investigación
Acrónimo agencia financiadora AEI
Programa Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i
Subprograma Subprograma Estatal de Generación de Conocimiento
Convocatoria Proyectos I+D
Año convocatoria 2019
Unidad de gestión Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020
Centro beneficiario UNIVERSIDAD PUBLICA DE NAVARRA
Identificador persistente http://dx.doi.org/10.13039/501100011033

Publicaciones

Found(s) 12 result(s)
Found(s) 2 page(s)

Robots for elderly care: review, multi-criteria optimization model and qualitative case study

Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
  • Sawik, Bartosz
  • Tobis, Sławomir
  • Baum, Ewa
  • Suwalska, Aleksandra
  • Kropińska, Sylwia
  • Stachnik, Katarzyna
  • Pérez-Bernabeu, Elena
  • 0000-0003-2974-4751
  • Agustín Martín, Alba
  • Wieczorowska-Tobis, Katarzyna.
This paper focuses on three areas: the first is a review of current knowledge about social and service robots for elderly care. The second is an optimization conceptual model aimed at maximizing the efficiency of assigning robots to serve the elderly. The proposed multi-criteria optimization model is the first one proposed in the area of optimization for robot assignment for the elderly with robot utilization level and caregiver stress level. The third is the findings of studies on the needs, requirements, and adoption of technology in elderly care. We consider the use of robots as a part of the ENRICHME project for long-term interaction and monitoring of older persons with mild cognitive impairment, to optimize their independence. Additionally, we performed focus group discussions (FGD) to collect opinions about robot-related requirements of the elderly and their caregivers. Four FDGs of six persons were organized: two comprising older adults, and two of the other formal and informal caregivers, based on a detailed script. The statements of older participants and their caregivers were consistent in several areas. The analysis revealed user characteristics, robot-related issues, functionality, and barriers to overcome before the deployment of the robot. An introduction of the robot must be thoroughly planned, include comprehensive pre-training, and take the ethical and practical issues into account. The involvement of future users in the customization of the robot is essential., This research was part of the ENRICHME project (ENabling Robot and assisted living environment for Independent Care and Health Monitoring of the Elderly), funded by the European Union Horizon 2020 Program, No: 643691C. This research was also partly supported by Poznan University of Medical Sciences in Poland and AGH University of Science and Technology in Krakow, Poland (16.16.200.396). Likewise, we appreciate the help of the Polish Ministry of Science & Higher Education grants (N N519 405934; 6459/B/T02/2011/40), and the Polish National Science Centre research grant (DEC-2013/11/B/ST8/04458) and the financial aid of the Spanish Ministry of Science, Innovation, and Universities (RED2022-134703-T; PID2019-111100RB-C22/AEI/10.13039/501100011033).




Space mission risk, sustainability and supply chain: review, multi-objective optimization model and practical approach

Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
  • Sawik, Bartosz
This paper investigates the convergence of risk, sustainability, and supply chain in space
missions, including a review of fundamental concepts, the introduction of a multi-objective conceptual
optimization model, and the presentation of a practical approach. Risks associated with space
missions include technical, human, launch, space environment, mission design, budgetary, and political
risks. Sustainability considerations must be incorporated into mission planning and execution to
ensure the long-term viability of space exploration. The study emphasizes the importance of considering
environmental sustainability, resource use, ethical concerns, long-term planning, international
collaboration, and public outreach in space missions. It emphasizes the significance of reducing
negative environmental consequences, increasing resource use efficiency, and making responsible
and ethical actions. The paper offers a multi-objective optimization conceptual model that may be
used to evaluate and choose sustainable space mission tactics. This approach considers a variety
of elements, including environmental effects, resource utilization, mission cost, and advantages for
society. It provides a systematic decision-making approach that examines trade-offs between different
criteria and identifies optimal conceptual model solutions that balance risk, sustainability, and supply
chain objectives. A practical approach is also offered to demonstrate the use of the multi-criteria
optimization conceptual model in a space mission scenario. The practical approach demonstrates
how the model can aid in the development of mission strategies that minimize risks, maximize
resource consumption, and fit with sustainability goals. Overall, this paper delivers a multi-criteria
optimization conceptual model and provides a space mission planning practical approach, as well
as an overview of the interaction between risk, sustainability, and supply chain in space mission
organization, planning, and execution., This research was partially supported by the AGH University of Science and Technology, Kraków, Poland (16.16.200.396) and the financial aid of the Polish Ministry of Science and Higher Education (MNISW) grants (N N519 405934; 6459/B/T02/2011/40) and the Polish National Science Centre (NCN) research grant (DEC-2013/11/B/ST8/04458). Moreover, I appreciate the support of the Spanish Ministry of Science, Innovation, and Universities (RED2018-102642-T; RED2022-134703-T; PID2019-111100RB-C22/AEI/10.13039/501100011033). Additionally, I acknowledge the support from the Public University of Navarra, Pamplona, Spain and the University of California at Berkeley, USA. The research was also partially supported by the European Union Horizon 2020 research and innovation program under Marie-Skłodowska Curie, No: 101034285.




The role of simulation and serious games in teaching concepts on circular economy and sustainable energy

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
  • Torre-Martínez, María Rocío de la|||0000-0002-8662-8901
  • Onggo, Bahkti S.
  • Corlu, Canan G.
  • Nogal, Maria
  • Juan, Angel A.|||0000-0003-1392-1776
[EN] The prevailing need for a more sustainable management of natural resources depends not only on the decisions made by governments and the will of the population, but also on the knowledge of the role of energy in our society and the relevance of preserving natural resources. In this sense, critical work is being done to instill key concepts-such as the circular economy and sustainable energy-in higher education institutions. In this way, it is expected that future professionals and managers will be aware of the importance of energy optimization, and will learn a series of computational methods that can support the decision-making process. In the context of higher education, this paper reviews the main trends and challenges related to the concepts of circular economy and sustainable energy. Besides, we analyze the role of simulation and serious games as a learning tool for the aforementioned concepts. Finally, the paper provides insights and discusses open research opportunities regarding the use of these computational tools to incorporate circular economy concepts in higher education degrees. Our findings show that, while efforts are being made to include these concepts in current programs, there is still much work to be done, especially from the point of view of university management. In addition, the analysis of the teaching methodologies analyzed shows that, although their implementation has been successful in favoring the active learning of students, their use (especially that of serious games) is not yet widespread., This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602).




Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
  • do C. Martins, Leandro
  • Tordecilla, Rafael D.
  • Castaneda, Juliana
  • Juan, Angel A.|||0000-0003-1392-1776
  • Faulin, Javier
[EN] The increasing use of electric vehicles in road and air transportation, especially in last-mile delivery and city mobility, raises new operational challenges due to the limited capacity of electric batteries. These limitations impose additional driving range constraints when optimizing the distribution and mobility plans. During the last years, several researchers from the Computer Science, Artificial Intelligence, and Operations Research communities have been developing optimization, simulation, and machine learning approaches that aim at generating efficient and sustainable routing plans for hybrid fleets, including both electric and internal combustion engine vehicles. After contextualizing the relevance of electric vehicles in promoting sustainable transportation practices, this paper reviews the existing work in the field of electric vehicle routing problems. In particular, we focus on articles related to the well-known vehicle routing, arc routing, and team orienteering problems. The review is followed by numerical examples that illustrate the gains that can be obtained by employing optimization methods in the aforementioned field. Finally, several research opportunities are highlighted., 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), the SEPIE Erasmus+Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.




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

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
  • Torre-Martínez, María Rocío de la|||0000-0002-8662-8901
  • Corlu, Canan G.
  • Faulin, Javier
  • Onggo, Bahkti S.
  • Juan, Angel A.|||0000-0003-1392-1776
[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.




Optimizing Transport Logistics under Uncertainty with Simheuristics: Concepts, Review and Trends

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
  • Castaneda, Juliana
  • Ghorbani, Elnaz
  • Ammouriova, Majsa
  • Panadero, Javier
  • Juan, Angel A.|||0000-0003-1392-1776
[EN] Background: Uncertainty conditions have been increasingly considered in optimization problems arising in real-life transportation and logistics activities. Generally, the analysis of complex systems in these non-deterministic environments is approached with simulation techniques. However, simulation is not an optimization tool. Hence, it must be combined with optimization methods when our goal is to: (i) minimize operating costs while guaranteeing a given quality of service; or (ii) maximize system performance using limited resources. When solving NP-hard optimization problems, the use of metaheuristics allows us to deal with large-scale instances in reasonable computation times. By adding a simulation layer to the metaheuristics, the methodology becomes a simheuristic, which allows the optimization element to solve scenarios under uncertainty. Methods: This paper reviews the indexed documents in Elsevier Scopus database of both initial as well as recent applications of simheuristics in the logistics and transportation field. The paper also discusses open research lines in this knowledge area. Results: The simheuristics approaches to solving NP-hard and large-scale combinatorial optimization problems under uncertainty scenarios are discussed, as they frequently appear in real-life applications in logistics and transportation activities. Conclusions: The way in which the different simheuristic components interact puts a special emphasis in the different stages that can contribute to make the approach more efficient from a computational perspective. There are several lines of research that are still open in the field of simheuristics., This work has been partially funded by the Spanish Ministry of Science (PID2019-111100RBC21-C22/AEI/10.13039/501100011033), the Barcelona City Council and Fundacio "la Caixa" under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001), and the Generalitat Valenciana (PROMETEO/2021/065).




Combining simheuristics with Petri nets for solving the stochastic vehicle routing problem with correlated demands

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
  • Latorre-Biel, Juan I.
  • Ferone, Daniele
  • Juan, Angel A.|||0000-0003-1392-1776
  • Faulin, Javier
[EN] This paper analyzes a stochastic version of the vehicle routing problem in which customers' demands are not only stochastic but also correlated. In order to solve this stochastic and correlated optimization problem, a simheuristic approach is combined with an adaptive demand predictor. This predictor is based on the use of machine learning methods and Petri nets. The information on real demands, provided by the vehicles as they visit the nodes of the logistic network, allows for a real-time forecast of the demand, as well as for an updated estimate of the correlation between them. A constrained prediction is provided by our hybrid algorithm, which is able to forecast an increase of 50% in the mean value of the demands of all nodes. With a very limited amount of information and reduced computational requirements, our algorithm provides a forecast with a high degree of reliability and a balanced capacity to reject false positives as well as false negatives. To illustrate its effectiveness, the methodology is applied to a wide range of benchmarks. The results show the benefits of applying this methodology in a context of correlated variation of the demands., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21/C22, RED2018-102642-T) and the SEPIE Erasmus + Program, Spain (2019I-ES01-KA103-062602). We also want to acknowledge the support received from the CAN Foundation in Navarre, Spain (Grant ID 903 100010434 under the agreement LCF/PR/PR15/51100007).




Modelling and multi-criteria analysis of the sustainability dimensions for the green vehicle routing problem

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
  • Abdullahi, Hassana
  • Reyes-Rubiano, Lorena
  • Ouelhadj, Djamila
  • Faulin, Javier
  • Juan, Angel A.|||0000-0003-1392-1776
[EN] The transport sector leads to detrimental effects on the economy, environment, and citizens quality of life. During recent years, some key-performance indicators have been proposed to quantify these negative impacts on the economic, environmental, and social dimensions of the sustainability concept. In this paper, we consider the sustainable vehicle routing problem that takes into account the aforementioned dimensions. We propose a weighted sum model and an epsilon-constraint model that combine the three dimensions, as well as a biased-randomised iterated greedy algorithm to solve the integrated problem. A comprehensive set of experiments and sensitivity analysis have been carried out with newly generated instances, which were adapted from existing vehicle routing benchmark instances. The sensitivity analysis is performed to measure the impact of each sustainability dimension and investigate trade-offs among them., This work has been partially supported by the Erasmus+ programme (2018-1-ES01-KA103-049767), and by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21/C22; RED2018-102642-T). We also want to thank the support of the University of Portsmouth and the Public University of Navarre doctoral programmes.




Determining Reliable Solutions for the Team Orienteering Problem with Probabilistic Delays

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
  • Herrera, Erika M.
  • Panadero, Javier
  • Carracedo-Garnateo, Patricia|||0000-0002-9352-9565
  • Juan, Angel A.|||0000-0003-1392-1776
  • Pérez Bernabeu, Elena|||0000-0002-9221-7623
[EN] In the team orienteering problem, a fixed fleet of vehicles departs from an origin depot towards a destination, and each vehicle has to visit nodes along its route in order to collect rewards. Typically, the maximum distance that each vehicle can cover is limited. Alternatively, there is a threshold for the maximum time a vehicle can employ before reaching its destination. Due to this driving range constraint, not all potential nodes offering rewards can be visited. Hence, the typical goal is to maximize the total reward collected without exceeding the vehicle's capacity. The TOP can be used to model operations related to fleets of unmanned aerial vehicles, road electric vehicles with limited driving range, or ride-sharing operations in which the vehicle has to reach its destination on or before a certain deadline. However, in some realistic scenarios, travel times are better modeled as random variables, which introduce additional challenges into the problem. This paper analyzes a stochastic version of the team orienteering problem in which random delays are considered. Being a stochastic environment, we are interested in generating solutions with a high expected reward that, at the same time, are highly reliable (i.e., offer a high probability of not suffering any route delay larger than a user-defined threshold). In order to tackle this stochastic optimization problem, which contains a probabilistic constraint on the random delays, we propose an extended simheuristic algorithm that also employs concepts from reliability analysis., This work has been partially funded by the Spanish Ministry of Science (PID2019-111100RBC21-C22/AEI/10.13039/501100011033), the Barcelona City Council and Fundacio "la Caixa" under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001), and the Generalitat Valenciana (PROMETEO/2021/065).




Optimizing Energy Consumption in Smart Cities¿ Mobility: Electric Vehicles, Algorithms, and Collaborative Economy

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
  • Ghorbani, Elnaz
  • Fluechter, Tristan
  • Calvet, Laura
  • Ammouriova, Majsa
  • Panadero, Javier
  • Juan, Angel A.|||0000-0003-1392-1776
[EN] Mobility and transportation activities in smart cities require an increasing amount of energy. With the frequent energy crises arising worldwide and the need for a more sustainable and environmental friendly economy, optimizing energy consumption in these growing activities becomes a must. This work reviews the latest works in this matter and discusses several challenges that emerge from the aforementioned social and industrial demands. The paper analyzes how collaborative concepts and the increasing use of electric vehicles can contribute to reduce energy consumption practices, as well as intelligent x-heuristic algorithms that can be employed to achieve this fundamental goal. In addition, the paper analyzes computational results from previous works on mobility and transportation in smart cities applying x-heuristics algorithms. Finally, a novel computational experiment, involving a ridesharing example, is carried out to illustrate the benefits that can be obtained by employing these algorithms., This work has been partially funded by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033), the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602), the Barcelona City Council and Fundació ¿la Caixa¿ under the framework of the Barcelona Science Plan 2020-2023 (21S09355-001), and the Generalitat Valenciana (PROMETEO/2021/065).