BUSCADOR RECOLECTA

© 2022. 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, In a pandemic situation, a large quantity of medical items are being consumed by citizens globally. If not properly processed, these items can be pollutant or even dangerous. Inspired by a real case study in the city of Barcelona, and assuming that data from container sensors are available in the city open repository, this work addresses a medical waste collection problem both with and without uncertainty. The waste collection process is modeled as a rich open vehicle routing problem, where the constraints are not in the loading dimension but in the maximum time each vehicle can circulate without having to perform a mandatory stop, with the goal of minimizing the time required to complete the collection process. To provide high-quality solutions to this complex problem, a biased-randomized heuristic is proposed. This heuristic is combined with simulation to provide effective collection plans in scenarios where travel and pickup times are uncertain, Peer Reviewed

© 2021 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, The interest of Industry 4.0 in smart contracts and blockchain technologies is growing up day by day. Smart contracts have enabled new kinds of interactions whereby contractors can even fully automate processes they agree on. This technology is really appealing in Internet of Things (IoT) domain because smart devices generate events for software agents involved in a smart contract execution, making full automation possible. However, smart contracts have to comply with national and international laws and accountability of participant's actions. Soundness of a smart contract has to be verified in terms of law compliance. Here, we propose a model for verification and validation of law compliance of smart contracts in IoT environments. The main goal of this article is to propose a formal model (based on multiagent logic and ontological description of contracts) for validating law compliance of smart contracts and to determine potential responsibilities of failures, Peer Reviewed

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm., This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033). In addition, it has received the support of the Doctoral School at the Universitat Oberta de Catalunya (Spain) and the Universidad de La Sabana (INGPhD-12-2020)., Peer Reviewed

This work was partially funded by the Spanish Ministry of Science (PID2019-111100RB-C21 /AEI/ 10.13039/501100011033), aswell as by the Barcelona City Council and Fundació “la Caixa” under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001), and it was supported by the Departament de Recerca i Universitats de la Generalitat de Catalunya (Spain) (2021 SGR 01421 (GRBIO))., Peer Reviewed

Many supply chains are composed of producers, suppliers, carriers, and customers. These agents must be coordinated to reduce waste and lead times. Production and distribution are two essential phases in most supply chains. Hence, improving the coordination of these phases is critical. This paper studies a combined hybrid flow-shop and vehicle routing problem. The production phase is modeled as a hybrid flow-shop configuration. In the second phase, the produced jobs have to be delivered to a set of customers. The delivery is carried out in batches of products, using vehicles with a limited capacity. With the objective of minimizing the service time of the last customer, we propose a biased-randomized variable neighborhood descent algorithm. Different test factors, such as the use of alternative initial solutions, solution representations, and loading strategies, are considered and analyzed., This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21, RED2018-102642-T) and the Erasmus+ program (2019-I-ES01-KA103-062602)., Peer Reviewed

Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision-makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best-known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks., This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/01100011033), as well as by the Barcelona Council and the “laCaixa” Foundation under the framework of the Barcelona Science Plan 2020-2023 (grant21S09355-01) and Generalitat Valenciana (PROMETEO/2021/065)., Peer Reviewed

The fast development in IoT and Cloud technologies has propelled the emergence of a variety of computing paradigms, among which Fog and Edge computing are salient computing technologies. Such new paradigms are opening up new opportunities to implement novel application scenarios, not possible before, by supporting features of mobility, edge intelligence and end-user support. This, however, comes with new computing challenges. One such challenge is the allocation of applications to Fog and Edge nodes. Indeed, for some application scenarios larger computing capacity might be needed. Therefore, due to co-existence of computing devices of different computing granularity, techniques for grouping up and clustering resources into virtual nodes of larger computing capacity are required. In this paper we present some clustering techniques for creating virtual computing nodes from Fog/Edge nodes by combining semantic description of resources with semantic clustering techniques. Then, we use such clusters for optimal allocation (via heuristics and Liner Programming) of applications to virtual computing nodes. Simulation results are reported to support the feasibility of the model and efficacy of the proposed approach. First Fit Heuristic Algorithm (FFHA) outperformed ILP method for medium and large size instances. Likewise, FFHA performed more consistently than ILP on various experimental setting. Finally, the results showed that the proposed clustering techniques deliver relatively fast response times, while enabling the service of a larger number of applications, with more demanding requirements, Peer Reviewed

Fog and Edge computing are opening up new opportunities to implement novel features of mobility, edge intelligence and end-user support. The successful implementation and deployment of Fog layers, as part of Cloud-to-thing-computing, largely depends on optimized allocation of tasks and applications to Fog and Edge nodes. Similarly as in other large scale distributed systems, the optimization problems that arise are computationally hard to solve. Such problems become even more challenging due to the need of application scenarios for larger computing capacity, beyond those of single nodes, requiring thus efficient resource grouping. In this paper we present some clustering techniques for creating virtual computing nodes from Fog/Edge nodes by combining semantic description of resources with semantic clustering techniques. Then, we use such clusters for optimal allocation (via heuristics and Integer Linear Programming) of applications to virtual computing nodes. Simulation results are reported to support the feasibility of the model and efficacy of the proposed approach. Applications of allocation methods to Intelligent Transportation Systems are also discussed, Peer Reviewed

Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos, The growth of e-commerce and the on-demand economy in urban and metropolitan areas
has been accelerated by the recent COVID-19 pandemic. As a consequence, logistics and
transportation operators are subject to a noticeable pressure to develop efficient delivery
systems. These systems are also influenced by the global trend towards more sustainable
transportation and mobility, which implies changes in urban policies and technological
innovations --$e.g.$, the substitution of traditional diesel petrol-drive vehicles by electric
ones. This paper analyzes the current and predicted needs of logistics operators in the
Barcelona metropolitan area. To do so, urban regulations are analyzed and key
shareholders are interviewed. The analysis of these interviews promote a discussion on
how the use of new `agile' optimization algorithms --which are based on the combination
of biased-randomized heuristics, computer parallelization techniques, and IoT / 5G
technologies-- can contribute to enhance urban distribution practices. Finally, we present a
case study in which the effect of different configurations of working/resting times and
parking areas availability on routing solutions is studied. Our research aims to provide
comprehensive knowledge to managers and policy-makers, and to offer them with
powerful tools capable of generating real-time solutions to complex last-mile delivery
challenges under dynamic conditions., This work has been partially supported by the Spanish Ministry of Science (PID2019- 111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T), and the Erasmus+ Program (2019-I-ES01-KA103-062602). We also thank the entities that accepted to be interviewed during the development of this study.

Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos, The sustainable development of freight transport has received much attention in recent
years. The new regulations for sustainable transport activities established by the European
Commission and the United Nations have created the need for road freight transport
companies to develop methodologies to measure the social and environmental impact of
their activities. This work aims to develop a model based on supervised machine learning
methods with intelligent classification algorithms and key performance indicators for each
dimension of sustainability as input data. This model allows establishing the level of
sustainability (high, medium or low). Several classification algorithms were trained,
finding that the support vector machines algorithm is the most accurate, with 98% accuracy
for the data set used. The model is tested by establishing the level of sustainability of a
European company in the road freight sector, thus allowing the establishment of green
strategies for its sustainable development., This work has been partially supported by the Spanish Ministry of Science (PID2019- 111100RB-C21 / AEI /10.13039/501100011033, RED2018-102642-T), and the Erasmus+ Program (2019-I-ES01-KA103-062602).

Both manufacturing and service industries are subject to uncertainty. Probability techniques and simulation methods allow us to model and analyze complex systems in which stochastic uncertainty is present. When the goal is to optimize the performance of these stochastic systems, simulation by itself is not enough and it needs to be hybridized with optimization methods. Since many real-life optimization problems in the aforementioned industries are NP-hard and large scale, metaheuristic optimization algorithms are required. The simheuristics concept refers to the hybridization of simulation methods and metaheuristic algorithms. This paper provides an introductory tutorial to the concept of simheuristics, showing how it has been successfully employed in solving stochastic optimization problems in many application fields, from production logistics and transportation to telecommunication and insurance. Current research trends in the area of simheuristics, such as their combination with fuzzy logic techniques and machine learning methods, are also discussed., This work has been partially funded by the Spanish Ministry of Science (PID2019-111100RB-C21-C22 /AEI/ 10.13039/501100011033 and RED2018-102642-T), as well as by the Barcelona City Council and Fundació “la Caixa” under the framework of the Barcelona Science Plan 2020–2023 (grant 21S09355-001). Moreover, we appreciate the financial support of the Erasmus+ Program (2019-I-ES01-KA103-062602).

Real-life transport operations are often subject to uncertainties in travel time or customers'demands. Additionally, these uncertainties greatly impact the economic, environmental, and social costs of vehicle routing plans. Thus, analysing the sustainability costs of transportation activities and reliability in the presence of uncertainties is essential for decision makers. Accordingly, this paper addresses the Sustainable Vehicle Routing Problem with Stochastic Travel times and Demands. This paper proposes a novel weighted stochastic recourse model that models travel time and demand uncertainties. To solve this challenging problem, we propose an extended simheuristic that integrates reliability analysis to evaluate the reliability of the generated solutions in the presence of uncertainties. An extensive set of computational experiments is carried out to illustrate the potential of the proposed approach and analyse the influence of stochastic components on the different sustainability dimensions., This work has been developed by the University of Portsmouth, Cardiff Metropolitan University and partially supported by the SEPIE Erasmus+ Program (2019-1-ES01-KA103-062602) in collaboration with the Public University of Navarre, the Petroleum Technology Development Fund (PTDF/E/OSS/PHD/HAH/699/14), and the Spanish Ministry of Science and Innovation (PID2019-111100RB-C21-C22 /AEI/ 10.13039/ 501100011033, RED2018-102642-T, the Barcelona City Council and 'La Caixa' (21S09355-001)).

The team orienteering problem (TOP) is anNP-hardoptimization problem with an increasing number of po-tential applications in smart cities, humanitarian logistics, wildfire surveillance, etc. In the TOP, a fixed fleetof vehicles is employed to obtain rewards by visiting nodes in a network. All vehicles share common originand destination locations. Since each vehicle has a limitation in time or traveling distance, not all nodes inthe network can be visited. Hence, the goal is focused on the maximization of the collected reward, takinginto account the aforementioned constraints. Most of the existing literature on the TOP focuses on its de-terministic version, where rewards and travel times are assumed to be predefined values. This paper focuseson a more realistic TOP version, where travel times are modeled as random variables, which introduces reli-ability issues in the solutions due to the route-length constraint. In order to deal with these complexities, wepropose a simheuristic algorithm that hybridizes biased-randomized heuristics with a variable neighborhoodsearch and MCS. To test the quality of the solutions generated by the proposed simheuristic approach, weemploy the well-known sample average approximation (SAA) method, as well as a combination model thathybridizes the metaheuristic used in the simheuristic approach with the SAA algorithm. The results showthat our proposed simheuristic outperforms the SAA and the hybrid model both on the objective functionvalues and computational time., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21/C22/AEI/10.13039/501100011033). Similarly, we appreciate
the financial support of the Barcelona City Council and “La Caixa” (21S09355-001).

Many real-life applications of the vehicle routing problem (VRP) occur in scenarios subject to
uncertainty or dynamic conditions. Thus, for instance, traveling times or customers demands mightMany real-life applications of the vehicle routing problem (VRP) occur in scenarios subject to
uncertainty or dynamic conditions. Thus, for instance, traveling times or customers’ demands might
be better modeled as random variables than as deterministic values. Likewise, traffic conditions could
evolve over time, synchronization issues should need to be considered, or a real-time re-optimization
of the routing plan can be required as new data become available in a highly dynamic environment.
Clearly, different solving approaches are needed to efficiently cope with such a diversity of scenarios.
After providing an overview of current trends in VRPs, this paper reviews a set of heuristic-based
algorithms that have been designed and employed to solve VRPs with the aforementioned properties.
These include simheuristics for stochastic VRPs, learnheuristics and discrete-event heuristics for
dynamic VRPs, and agile optimization heuristics for VRPs with real-time requirements., This work was 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).

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.

Carsharing practices are spreading across many cities in the world. This paper analyzes real-life data obtained from a private carsharing company operating in the city of Barcelona, Spain. After describing the main trends in the data, machine learning and time-series analysis methods are employed to better understand citizens' needs and behavior, as well as to make predictions about the evolution of their demand for this service. In addition, an original proposal is made regarding the location of the pick-up points. This proposal is based on a capacitated dispersion algorithm, and aims at balancing two relevant factors, including scattering of pick-up points (so that most users can benefit from the service) and efficiency (so that areas with higher demand are well covered). Our aim is to gain a deeper understanding of citizens' needs and behavior in relation to carsharing services. The analysis includes three main components: descriptive, predictive, and prescriptive, resulting in customer segmentation and forecast of service demand, as well as original concepts for optimizing parking station location.

In the context of increasing complexity in manufacturing and logistic systems, the combination of optimization and simulation can be considered a versatile tool for supporting managerial decision-making. An informed storage location assignment policy is key for improving warehouse operations, which play a vital role in the efficiency of supply chains. Traditional approaches in the literature to solve the storage location assignment problem present some limitations, such as excluding the stochastic variability of processes or the interaction among different warehouse activities. This work addresses those limitations by proposing a discrete-event simheuristic framework that ensures robust solutions in the face of real-life warehouse conditions. The approach followed embraces the complexity of the problem by integrating the order sequence and picking route in the solution construction and uses commercial simulation software to reduce the impact of stochastic events on the quality of the solution. The implementation of this type of novel methodology within a warehouse management system can enhance warehouse efficiency without requiring an increase in automation level. The method developed is tested under a number of computational experiments that show its convenience and point toward future lines of research.

In the context of simulation-based optimisation, this paper reviews recent work related to the role of metaheuristics, matheuristics (combinations of exact optimisation methods with metaheuristics), simheuristics (hybridisation of simulation with metaheuristics), biased-randomised heuristics for 'agile' optimisation via parallel computing, and learnheuristics (combination of statistical/machine learning with metaheuristics) to deal with NP-hard and large-scale optimisation problems in areas such as transport and logistics, manufacturing and production, smart cities, telecommunication networks, finance and insurance, sustainable energy consumption, health care, military and defence, e-marketing, or bioinformatics. The manuscript provides the main related concepts and updated references that illustrate the applications of these hybrid optimisation-simulation-learning approaches in solving rich and real-life challenges under dynamic and uncertainty scenarios. A numerical analysis is also included to illustrate the benefits that these approaches can offer across different application fields. Finally, this work concludes by highlighting open research lines on the combination of these methodologies to extend the concept of simulation-based optimisation.

Depopulation of rural areas poses a range of new challenges for the provision of public services. Access to primary care centres is essential for promoting and protecting quality of life, especially for the older population. However, many rural municipalities face abandonment from public administrations by not even having a local clinic and forcing their dwellers to drive to the nearest facility. This research examines the adequacy of providing home healthcare (HHC) systems in such areas to avoid unnecessary trips. When designing routes to provide these services, decision-makers must take into account the following: (i) wide geographical spread of places to visit; (ii) difficulty in accessing some of these places with heavy medically equipped vehicles; and (iii) uncertainty regarding places to visit and availability of professionals. Our study identifies municipalities in which the travelling distance to the closest centre is above a desirable threshold, estimating the magnitude of the problem. Then, we provide an illustrative example in which an agile methodology is presented to design efficient routes that allow policymakers to offer HHC services of reasonable quality.

The Time Capacitated Arc Routing Problem (TCARP) extends the classical Capacitated Arc Routing Problem by considering time-based capacities instead of traditional loading capacities. In the TCARP, the costs associated with traversing and servicing arcs, as well as the vehicle's capacity, are measured in time units. The increasing use of electric vehicles and unmanned aerial vehicles, which use batteries of limited duration, illustrates the importance of time-capacitated routing problems. In this paper, we consider the TCARP with stochastic demands, i.e.: the actual demands on each edge are random variables which specific values are only revealed once the vehicle traverses the arc. This variability affects the service times, which also become random variables. The main goal then is to find a routing plan that minimizes the expected total time required to service all customers. Since a maximum time capacity applies on each route, a penalty time-based cost arises whenever a route cannot be completed within that limit. In this paper, a strategic oscillation simheuristic algorithm is proposed to solve this stochastic problem. The performance of our algorithm is tested in a series of numerical experiments that extend the classical deterministic instances into stochastic ones.

This study delves into the intricate dynamics of urban mobility, a pivotal aspect for policymakers, businesses, and communities alike. By deciphering patterns of movement within a city, stakeholders can craft targeted interventions to mitigate traffic congestion peaks, optimizing both resource allocation and individual travel routes. Focused on Barcelona, Spain, this paper draws on data sourced from the city council's open data service. Through a blend of exploratory analysis, visualization techniques, and modeling methodologies-including time series analysis and the eXtreme Gradient Boosting (XGBoost) algorithm-the research endeavors to forecast traffic conditions. Additionally, a study of variable importance is carried out, and Shapley Additive Explanations are applied to enhance the interpretability of model outputs. Findings underscore the limitations of traditional forecasting methods in capturing the nuanced spatial and temporal dependencies present in traffic flows, particularly over medium- to long-term horizons. However, the XGBoost model demonstrates robust performance, with the area under ROC curves consistently exceeding 80%, indicating its efficacy in handling non-linear traffic data variables.

[EN] Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision-makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best-known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks., Spanish Ministry of Science, Grant/Award Number: PID2019-111100RB-C21/AEI/10.13039/501100011033; Barcelona Council and the "la Caixa" Foundation under the framework of the Barcelona Science Plan 2020-2023, Grant/Award Number: 21S09355-001; Generalitat Valenciana,Grant/Award Number: PROMETEO/2021/065

[EN] Hypertension, a primary risk factor for various cardiovascular diseases, is a global health concern. Early identification and effective management of hypertensive individuals are vital for reducing associated health risks. This study explores the potential of deep learning (DL) techniques, specifically GoogLeNet, ResNet-18, and ResNet-50, for discriminating between normotensive (NTS) and hypertensive (HTS) individuals using photoplethysmographic (PPG) recordings. The research assesses the impact of calibration at different time intervals between measurements, considering intervals less than 1 h, 1-6 h, 6-24 h, and over 24 h. Results indicate that calibration is most effective when measurements are closely spaced, with an accuracy exceeding 90% in all the DL strategies tested. For calibration intervals below 1 h, ResNet-18 achieved the highest accuracy (93.32%), sensitivity (84.09%), specificity (97.30%), and F1-score (88.36%). As the time interval between calibration and test measurements increased, classification performance gradually declined. For intervals exceeding 6 h, accuracy dropped below 81% but with all models maintaining accuracy above 71% even for intervals above 24 h. This study provides valuable insights into the feasibility of using DL for hypertension risk assessment, particularly through PPG recordings. It demonstrates that closely spaced calibration measurements can lead to highly accurate classification, emphasizing the potential for real-time applications. These findings may pave the way for advanced, non-invasive, and continuous blood pressure monitoring methods that are both efficient and reliable., This research has received financial support from public grants PID2021-123804OB-I00, PID2021-00X128525-IV0 and TED2021-130935B-I00 of the Spanish Government, jointly with the European Regional Development Fund (EU), SBPLY/17/180501/000411 and SBPLY/21/180501/000186 from Junta de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana.

[EN] Depopulation of rural areas poses a range of new challenges for the provision of public services. Access to primary care centres is essential for promoting and protecting quality of life, especially for the older population. However, many rural municipalities face abandonment from public administrations by not even having a local clinic and forcing their dwellers to drive to the nearest facility. This research examines the adequacy of providing home healthcare (HHC) systems in such areas to avoid unnecessary trips. When designing routes to provide these services, decision-makers must take into account the following: (i) wide geographical spread of places to visit; (ii) difficulty in accessing some of these places with heavy medically equipped vehicles; and (iii) uncertainty regarding places to visit and availability of professionals. Our study identifies municipalities in which the travelling distance to the closest centre is above a desirable threshold, estimating the magnitude of the problem. Then, we provide an illustrative example in which an agile methodology is presented to design efficient routes that allow policymakers to offer HHC services of reasonable quality., This work was supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033) ; the EU Commission (HORIZON-CL5-2021-D6-01-08) ; and the eHealth Centre at the Universitat Oberta de Catalunya in the framework of the eHealth Research Promotion Programme.

[EN] The growth of e-commerce and the on-demand economy in urban and metropolitan areas has been accelerated by the recent COVID-19 pandemic. As a consequence, logistics and transportation operators are subject to a noticeable pressure to develop efficient delivery systems. These systems are also influenced by the global trend towards more sustainable transportation and mobility, which implies changes in urban policies and technological innovations ¿e.g., the substitution of traditional diesel petrol-drive vehicles by electric ones. This paper analyzes the current and predicted needs of logistics operators in the Barcelona metropolitan area. To do so, urban regulations are analyzed and key shareholders are interviewed. The analysis of these interviews promote a discussion on how the use of new `agile¿ optimization algorithms ¿which are based on the combination of biased-randomized heuristics, computer parallelization techniques, and IoT / 5G technologies¿ can contribute to enhance urban distribution practices. Finally, we present a case study in which the effect of different configurations of working/resting times and parking areas availability on routing solutions is studied. Our research aims to provide comprehensive knowledge to managers and policy-makers, and to offer them with powerful tools capable of generating real-time solutions to complex last-mile delivery challenges under dynamic conditions., This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21 / AEI /
10.13039/501100011033, RED2018-102642-T), and the Erasmus+ Program (2019-I-ES01-KA103-062602). We also
thank the entities that accepted to be interviewed during the development of this study.

[EN] Carsharing practices are spreading across many cities in the world. This paper analyzes real-life data obtained from a private carsharing company operating in the city of Barcelona, Spain. After describing the main trends in the data, machine learning and time-series analysis methods are employed to better understand citizens' needs and behavior, as well as to make predictions about the evolution of their demand for this service. In addition, an original proposal is made regarding the location of the pick-up points. This proposal is based on a capacitated dispersion algorithm, and aims at balancing two relevant factors, including scattering of pick-up points (so that most users can benefit from the service) and efficiency (so that areas with higher demand are well covered). Our aim is to gain a deeper understanding of citizens' needs and behavior in relation to carsharing services. The analysis includes three main components: descriptive, predictive, and prescriptive, resulting in customer segmentation and forecast of service demand, as well as original concepts for optimizing parking station location., This work has been partially funded by the Spanish Ministry of Science (PID2019-111100RB-C21 /AEI/ 10.13039/501100011033), as well as by the Barcelona City Council and Fundacio "la Caixa" under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001).

[EN] The sustainable development of freight transport has received much attention in recent years. The new regulations for sustainable transport activities established by the European Commission and the United Nations have created the need for road freight transport companies to develop methodologies to measure the social and environmental impact of their activities. This work aims to develop a model based on supervised machine learning methods with intelligent classification algorithms and key performance indicators for each dimension of sustainability as input data. This model allows establishing the level of sustainability (high, medium, or low). Several classification algorithms were trained, finding that the support vector machines algorithm is the most accurate, with 98% accuracy for the data set used. The model is tested by establishing the level of sustainability of a European company in the road freight sector, thus allowing the establishment of green strategies for its sustainable development., This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21 / AEI
/10.13039/501100011033, RED2018-102642-T), and the Erasmus+ Program (2019-I-ES01-KA103-062602).

[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).

[EN] Many real-life applications of the vehicle routing problem (VRP) occur in scenarios subject to uncertainty or dynamic conditions. Thus, for instance, traveling times or customers' demands might be better modeled as random variables than as deterministic values. Likewise, traffic conditions could evolve over time, synchronization issues should need to be considered, or a real-time re-optimization of the routing plan can be required as new data become available in a highly dynamic environment. Clearly, different solving approaches are needed to efficiently cope with such a diversity of scenarios. After providing an overview of current trends in VRPs, this paper reviews a set of heuristic-based algorithms that have been designed and employed to solve VRPs with the aforementioned properties. These include simheuristics for stochastic VRPs, learnheuristics and discrete-event heuristics for dynamic VRPs, and agile optimization heuristics for VRPs with real-time requirements., This work was 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 Fundacio "la Caixa" under the framework of the Barcelona Science Plan 2020-2023 (21S09355-001), and the Generalitat Valenciana (PROMETEO/2021/065).

[EN] For many years, European demography is experiencing two worrying phenomena: aging and migrations of young people towards urban agglomerations. Rural areas are becoming depopulated, and many public services are becoming unsustainable. Health care assistance is one of those services called into question, specially for elder people living in small towns without access to primary care. Fortunately, technological development allows us to improve this situation. First by obtaining, storing, and analysing all kinds of data related to the patient's health. Second, by developing intelligent algorithms that optimize the available resources to provide a better service. This article studies the level of coverage of primary care centres in the Pyrenees of Lleida, Spain, and proposes the use of an optimization algorithm for an efficient and effective design of routes that allow primary health care professionals to move from a medical centre to the homes of patients who require a visit. To illustrate the use of this methodology, we present a study focused on Bausen, a depopulated municipality in the Aran Valley, close to the French border., This work has been partially funded by the Spanish Ministry of Science (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033), as well as by the eHealth Center at the Open University of Catalonia under the framework of the eHealth Research Promotion Programme.

[EN] In the context of logistics and transportation, this paper discusses how simheuristics can be extended by adding a fuzzy layer that allows us to deal with complex optimization problems with both stochastic and fuzzy uncertainty. This hybrid approach combines simulation, metaheuristics, and fuzzy logic to generate near-optimal solutions to large scale NP-hard problems that typically arise in many transportation activities, including the vehicle routing problem, the arc routing problem, or the team orienteering problem. The methodology allows us to model different components-such as travel times, service times, or customers' demands-as deterministic, stochastic, or fuzzy. A series of computational experiments contribute to validate our hybrid approach, which can also be extended to other optimization problems in areas such as manufacturing and production, smart cities, telecommunication networks, etc., This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T), and the Erasmus+ program (2019-I-ES01-KA103-062602). This research received no external funding.

[EN] Project portfolio management and optimization constitutes a critical activity for organizations in different industrial sectors and business. The scientific literature in this subject is extremely vast, which makes it difficult to understand the connections among the existing approaches and perspectives. This paper provides a clustering map of the existing work on the subject, thus identifying the main trends and approaches from different scientific communities. After analyzing each of the identified clusters, the paper provides insights and emerging trends that can be useful both for researchers and practitioners in the area., This project has been partially supported by the Spanish Ministry of Science and Innovation (PID2019-111100RB-C21/AEI/ 10.13039/501100011033, RED2018-102642-T), the Erasmus+ Program (2019-I-ES01-KA103-062602), FEDER, and the AGAUR (2018 LLAV 00017).

[EN] The uncapacitated facility location problem (UFLP) is a popular NP-hard optimization problem that has been traditionally applied to logistics and supply networks, where decisions are difficult to reverse. However, over the years, many new application domains have emerged, in which real-time optimization is needed, such as Internet of Vehicles (IoV), virtual network functions placement, and network controller placement. IoV scenarios take into account the presence of multiple roadside units (RSUs) that should be frequently assigned to operating vehicles. To ensure the desired quality of service level, the allocation process needs to be carried out frequently and efficiently, as vehicles' demands change. In this dynamic environment, the mapping of vehicles to RSUs needs to be reoptimized periodically over time. Thus, this article proposes an agile optimization algorithm, which is tested using existing benchmark instances. The experiments show that it can efficiently generate high-quality and real-time results in dynamic IoV scenarios., This research was partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T) and the Erasmus+ program (2019-I-ES01-KA103-062602). A major part of this work was done during Alessandro Fusco's visit to the Universitat Oberta de Catalunya, Spain, supported by the Erasmus+ Study program of the European Union. The authors also thank Jon Raleigh for the final review of the paper.

[EN] The COVID-19 pandemic accelerated the shift towards online shopping, reshaping consumer habits and intensifying the impact on urban freight distribution. This disruption exacerbated traffic congestion and parking shortages in cities, underscoring the need for sustainable distribution models. The European Union's common transport policy advocates for innovative UFD approaches that promote intermodal transportation, reduce traffic, and optimize cargo loads. Our study addresses these challenges by proposing an agile routing algorithm for an alternative UFD model in Barcelona. This model suggests strategically located micro-hubs selected from a set of railway facilities, markets, shopping centers, district buildings, pickup points, post offices, and parking lots (1057 points in total). It also promotes intermodality through cargo bikes and electric vans. The study has two main objectives: (i) to identify a network of intermodal micro-hubs for the efficient delivery of parcels in Barcelona and (ii) to develop an agile routing algorithm to optimize their location. The algorithm generates adaptive distribution plans considering micro-hub operating costs and vehicle routing costs, and using heuristic and machine learning methods enhanced by parallelization techniques. It swiftly produces high-quality routing plans based on transportation infrastructure, transportation modes, and delivery locations. The algorithm adapts dynamically and employs multi-objective techniques to establish the Pareto frontier for each plan. Real-world testing in Barcelona, using actual data has shown promising results, providing potential scenarios to reduce CO2 emissions and improve delivery times. As such, this research offers an innovative and sustainable approach to UFD, that will contribute significantly to a greener future for cities., This research was supported by the European Commission (No. 101069782),
the Spanish Ministry of Science and Innovation (PID2019-111100RB-C21/
AEI/10.13039/501100011033) and (PID2022-138860NB-I00 and RED2022134703-T), and the Barcelona City Council (22S02264-001).

[EN] Nowadays, several alternatives to internal combustion engines are being proposed in
order to reduce CO2 emissions in freight transportation and citizen mobility. According to many
experts, the use of electric vehicles constitutes one of the most promising alternatives for achieving
the desirable reductions in emissions. However, popularization of these vehicles is being slowed by
long recharging times and the low availability of recharging stations. One possible solution to this
issue is to employ the concept of battery sharing or battery swapping. This concept is supported by
important industrial partners, such as Eni in Italy, Ample in the US, and Shell in the UK. This paper
supports the introduction of battery swapping practices by analyzing their effects. A discrete-event
simulation model is employed for this study. The obtained results show that battery sharing practices
are not just a more environmentally and socially friendly solution, but also one that can be highly
beneficial for reducing traffic congestion., This work has been partially supported by the Barcelona City Council and la Caixa Foundation under the framework of the Barcelona Science Plan 2020-2023 (21S09355-001), and the Spanish Ministry of Science and Innovation (RED2022-134703-T, PID2019-111100RB-C21/AEI/ 10.13039/501100011033).

[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.

[EN] This paper analyzes the permutation flow-shop problem with delivery dates and cumulative payoffs (whenever these dates are met) under uncertainty conditions. In particular, the paper considers the realistic situation in which processing times are stochastic. The main goal is to find the permutation of jobs that maximizes the expected payoff. In order to achieve this goal, the paper first proposes a biased-randomized heuristic for the deterministic version of the problem. Then, this heuristic is extended into a metaheuristic by encapsulating it into a variable neighborhood descent framework. Finally, the metaheuristic is extended into a simheuristic by incorporating Monte Carlo simulations. According to the computational experiments, the level of uncertainty has a direct impact on the solutions provided by the simheuristic. Moreover, a risk analysis is performed using two well-known metrics: the value-at-risk and conditional value-at-risk., This work was supported by the Brazilian Coordination for the Improvement of Higher Level Personnel (CAPES) under grant (number 001); the Brazilian National Council for Scientific and Technological Development (CNPq) under grant (number 307403/2019-0); the Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ) under grants (numbers 202.673/2018, E-26/010.002576/2019 and 211.086/2019) and Spanish Ministry of Science (PID2019-111100RB-C21, RED2018-102642-T).

[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.

[EN] With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens' mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated., This work was partially supported by the Spanish Ministry of Science (PID2019111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T), and the Erasmus+ program (2019I-ES01-KA103-062602).

[EN] In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles should serve their customers by specified due date efficiently. Coordination between the vehicles might be needed to be accomplished in real-time in exceptional cases, such as after a traffic accident or extreme weather conditions. This paper presents the planning of vehicle routes as a team orienteering problem. In addition, an 'agile' optimization algorithm is presented to plan these routes for drones and other autonomous vehicles. This algorithm combines an extremely fast biased-randomized heuristic and a parallel computing approach., This work has been partially supported by the Spanish Ministry of Science and Innovation (PID2019-111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T). We also acknowledge the support of the Erasmus+ Program (2019-I-ES01-KA103-062602)

[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.

[EN] In this paper, we present a novel framework for enriching time series data in smart cities
by supplementing it with information from external sources via semantic data enrichment. Our
methodology effectively merges multiple data sources into a uniform time series, while addressing
difficulties such as data quality, contextual information, and time lapses. We demonstrate the efficacy
of our method through a case study in Barcelona, which permitted the use of advanced analysis
methods such as windowed cross-correlation and peak picking. The resulting time series data can
be used to determine traffic patterns and has potential uses in other smart city sectors, such as air
quality, energy efficiency, and public safety. Interactive dashboards enable stakeholders to visualize
and summarize key insights and patterns., This work was partially funded by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033), as well as by the Barcelona City Council and Fundació la Caixa
under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001).

[EN] Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an 'Internet of vehicles' with the potential to significantly enhance citizens' mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities., This work has been partially funded by the Spanish Ministry of Science (PID2019-111100RB-C21 /AEI/ 10.13039/501100011033), as well as by the Barcelona City Council and Fundacio "la Caixa" under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001).

[EN] Real-life transport operations are often subject to uncertainties in travel time or customers' demands. Additionally, these uncertainties greatly impact the economic, environmental, and social costs of vehicle routing plans. Thus, analysing the sustainability costs of transportation activities and reliability in the presence of uncertainties is essential for decision makers. Accordingly, this paper addresses the Sustainable Vehicle Routing Problem with Stochastic Travel times and Demands. This paper proposes a novel weighted stochastic recourse model that models travel time and demand uncertainties. To solve this challenging problem, we propose an extended simheuristic that integrates reliability analysis to evaluate the reliability of the generated solutions in the presence of uncertainties. An extensive set of computational experiments is carried out to illustrate the potential of the proposed approach and analyse the influence of stochastic components on the different sustainability dimensions., This work has been developed by the University of Portsmouth, Cardiff Metropolitan University and partially supported by the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602) in collaboration with the Public University of Navarre, the Petroleum Technology Development Fund (PTDF/E/OSS/PHD/HAH/699/14), and the Spanish Ministry of Science and Innovation (PID2019-111100RB-C21-C22 /AEI/ 10.13039/ 501100011033, RED2018-102642-T, the Barcelona City Council and "La Caixa" (21S09355-001)).

[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).

[EN] The Time Capacitated Arc Routing Problem (TCARP) extends the classical Capacitated Arc Routing Problem by considering time-based capacities instead of traditional loading capacities. In the TCARP, the costs associated with traversing and servicing arcs, as well as the vehicle's capacity, are measured in time units. The increasing use of electric vehicles and unmanned aerial vehicles, which use batteries of limited duration, illustrates the importance of time-capacitated routing problems. In this paper, we consider the TCARP with stochastic demands, i.e.: the actual demands on each edge are random variables which specific values are only revealed once the vehicle traverses the arc. This variability affects the service times, which also become random variables. The main goal then is to find a routing plan that minimizes the expected total time required to service all customers. Since a maximum time capacity applies on each route, a penalty time-based cost arises whenever a route cannot be completed within that limit. In this paper, a strategic oscillation simheuristic algorithm is proposed to solve this stochastic problem. The performance of our algorithm is tested in a series of numerical experiments that extend the classical deterministic instances into stochastic ones., This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033, RED2018102642T, PGC2018-0953322-B-C21/MCIU/AEI/FEDERUE) . The authors are also grateful to the Michael Smurfit Graduate Business School at University College Dublin, Ireland for supporting research stays that contributed to the development of this work.

[EN] The fast development in IoT and Cloud technologies has propelled the emergence of a variety of computing paradigms, among which Fog and Edge computing are salient computing technologies. Such new paradigms are opening up new opportunities to implement novel application scenarios, not possible before, by supporting features of mobility, edge intelligence and end-user support. This, however, comes with new computing challenges. One such challenge is the allocation of applications to Fog and Edge nodes. Indeed, for some application scenarios larger computing capacity might be needed. Therefore, due to co-existence of computing devices of different computing granularity, techniques for grouping up and clustering resources into virtual nodes of larger computing capacity are required. In this paper we present some clustering techniques for creating virtual computing nodes from Fog/Edge nodes by combining semantic description of resources with semantic clustering techniques. Then, we use such clusters for optimal allocation (via heuristics and Liner Programming) of applications to virtual computing nodes. Simulation results are reported to support the feasibility of the model and efficacy of the proposed approach. First Fit Heuristic Algorithm (FFHA) outperformed ILP method for medium and large size instances. Likewise, FFHA performed more consistently than ILP on various experimental setting. Finally, the results showed that the proposed clustering techniques deliver relatively fast response times, while enabling the service of a larger number of applications, with more demanding requirements., This work is supported by Research Project, "Efficient & Sustainable Transport Systems in Smart Cities: Internet of Things, Transport Analytics, and Agile Algorithms" (TransAnalytics) PID2019-111100RB-C21/AEI/ 10.13039/501100011033, Ministerio de Ciencia e Innovacion, Spain

[EN] Alginate is a highlypromising biopolymer due to its non-toxicand biodegradable properties. Alginate hydrogels are often fabricatedby cross-linking sodium alginate with calcium cations and can be engineeredwith highly desirable enhanced physical and biological propertiesfor biomedical applications. This study reports on the anticancer,antiviral, antibacterial, in vitro, and in vivo toxicity, water absorption,and compound release properties of an alginate hydrogel crosslinkedwith calcium and different amounts of zinc cations. The results showedthat the calcium alginate hydrogel film crosslinked with the highestamount of zinc showed similar water sorption properties to those ofcalcium alginate and released a suitable amount of zinc to provideanticancer activity against melanoma and colon cancer cells and hasantibacterial properties against methicillin-resistant Staphylococcus epidermidis and antiviral activityagainst enveloped and non-enveloped viruses. This film is non-toxicin both in vitro in keratinocyte HaCaT cells and in vivo in the Caenorhabditis elegans model, which renders it especiallypromising for biomedical applications., The authors would like to express their gratitude to the Fundacion Universidad Catolica de Valencia San Vicente Martir and to the Spanish Ministry of Science and Innovation for their financial support through Grants 2020-231-006UCV and PID2020-119333RBI00/AEI/10.13039/501100011033. The CIBER-BBN initiative is funded by the VI National R & amp;D & amp;I Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program. CIBER actions are financed by the Instituto de Salud Carlos III with assistance from the European Regional Development. Also, the authors acknowledge funding from Researchers Supporting Project number (RSP-2023R782), King Saud University, Riyadh, Saudi Arabia.

[EN] This study delves into the intricate dynamics of urban mobility, a pivotal aspect for policymakers, businesses, and communities alike. By deciphering patterns of movement within a city, stakeholders can craft targeted interventions to mitigate traffic congestion peaks, optimizing both resource allocation and individual travel routes. Focused on Barcelona, Spain, this paper draws on data sourced from the city council's open data service. Through a blend of exploratory analysis, visualization techniques, and modeling methodologies-including time series analysis and the eXtreme Gradient Boosting (XGBoost) algorithm-the research endeavors to forecast traffic conditions. Additionally, a study of variable importance is carried out, and Shapley Additive Explanations are applied to enhance the interpretability of model outputs. Findings underscore the limitations of traditional forecasting methods in capturing the nuanced spatial and temporal dependencies present in traffic flows, particularly over medium- to long-term horizons. However, the XGBoost model demonstrates robust performance, with the area under ROC curves consistently exceeding 80%, indicating its efficacy in handling non-linear traffic data variables., This work has been partially funded by the Spanish Ministry of Science (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033), as well as by the Barcelona City Council and Fundacio "la Caixa" under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001). The authors appreciate the support received from the research group GRBIO under the grant 2021 SGR01421 from the Departament de Recerca i Universitats de la Generalitat de Catalunya (Spain).

[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).