BUSCADOR RECOLECTA

[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] 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] 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] 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] 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] 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).

[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).

[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).

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

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 (PID2019-111100RBC21-C22/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). The authors appreciate the support received from the research group GRBIO under the grant 2021 SGR 01421 from the Departament de Recerca i Universitats de la Generalitat de Catalunya (Spain)., Peer Reviewed, Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles

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.

Background: This publication presents a review, multiple criteria optimization models, and
a practical example pertaining to the integration of automated smart locker systems, capillary distribution
networks, crowdshipping, last-mile delivery and supply chain management. This publication
addresses challenges in logistics and transportation, aiming to enhance efficiency, reduce
costs and improve customer satisfaction. This study integrates automated smart locker systems, capillary
distribution networks, crowdshipping, last-mile delivery and supply chain management.
Methods: A review of the existing literature synthesizes key concepts, such as facility location problems,
vehicle routing problems and the mathematical programming approach, to optimize supply
chain operations. Conceptual optimization models are formulated to solve the complex decisionmaking
process involved in last-mile delivery, considering multiple objectives, including cost minimization,
delivery time optimization, service level minimization, capacity optimization, vehicle
minimization and resource utilization. Results: The multiple criteria approaches combine the vehicle
routing problem and facility location problem, demonstrating the practical applicability of the proposed
methodology in a real-world case study within a logistics company. Conclusions: The execution
of multi-criteria models optimizes automated smart locker deployment, capillary distribution
design, crowdshipping and last-mile delivery strategies, showcasing its effectiveness in the logistics
sector., This research was partially supported by the AGH University of Kraków, 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; PID2019-111100RBC22/
AEI/10.13039/501100011033). Additionally, I acknowledge support from the Public University
of Navarra, Pamplona, Spain and University of California at Berkeley, USA. The research was also
partially supported by the European Union Horizon 2020 research and innovation program under
Marie-Sklodowska Curie, No: 101034285.

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).

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.

Urban distribution in medium-sized cities faces a major challenge, mainly when deliveries are difficult in the city center due to: an increase of e-commerce, weak public transportation system, and the promotion of urban sustainability plans. As a result, private cars, public transportation, and freight transportation compete for the same space. This paper analyses the current state for freight logistics in the city center of Pamplona (Spain) and proposes alternative transportation routes and transportation modes in the last-mile city center distribution according to different criteria evaluated by residents. An analytic hierarchy process (AHP) was developed. A number of alternatives have been assessed considering routes and transportation modes: the shortest route criterion and avoiding some city center area policies are combined with traditional van-based, bike, and aerial (drone) distribution protocols for delivering parcels and bar/restaurant supplies. These alternatives have been evaluated within a multicriteria framework in which economic, environmental, and social objectives are considered at the same time. The point in this multicriteria framework is that the criteria/alternative AHP weights and priorities have been set according to a survey deployed in the city of Pamplona (Navarre, Spain). The survey and AHP results show the preference for the use of drone or bike distribution in city center in order to reduce social and environmental issues., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C22/AEI/10.13039/501100011033; RED2018-102642-T), and the 'la Caixa' Foundation (LCF/PR/PR15/51100007) project. Moreover, we appreciate the financial support of the Erasmus+ Program (2018-1-ES01-KA103-049767).

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).

Traditional approaches to optimize freight delivery routes are based on minimizing a distance-based cost function. New approaches use also time as the objective function to minimize. However, the trade-off between time and distance is sometimes unclear. This paper presents a new approach to optimize delivery routes in which both time and distance are used together to optimize delivery routes. For this purpose, the operating cost of a vehicle and the value of time have been used to convert time and distance into monetary units. Through the study of three different networks in Spain with different level of detail (the region of Catalonia, the city of Barcelona, and the old part of Pamplona), the results have indicated that minimizing both time and distance provides better results than the traditional approach, especially at a local level where congestion effects are more relevant., This work was partially funded by the Spanish Ministry of Science, Innovation, and Universities (RED2022-134703-
T; PID2019-111100RB-C22/AEI/10.13039/501100011033) and the SEPIE Erasmus+ Program (2019-I-ES01-
KA103-062602). We also appreciate the help of the Public University of Navarra for Young Researchers Projects
Program (PJUPNA26-2022).

The disruptions experienced by the processes in the last mile delivery during the SARS-CoV-2 pandemic raised the dilemma of up-to-date last mile approaches for Urban Logistics (UL) issues. Self-Collection Delivery Systems (SCDS) have been proved to be an improvement for all the players of the SC, providing flexibility of time-windows and reducing overall mileage, delivery time and, consequently, gas emissions. Differing from previous works involving hybrid modeling for automated parcel lockers (APL) network design, this paper brings a System Dynamics Simulation Model (SDSM) to forecast online shopping demand in the Spanish city of Pamplona. A bi-criteria Facility Location Problem (FLP) is solved by means of an e-constraint method, where e is defined as the level of coverage of the total demand. The experiment run considers 90% of demand coverage, in order to obtain the most complex network possible. The simulation and demand forecast was carried out using Anylogic simulation software and the optimization procedure makes use of the Java-based CPLEX API solver., This work was partially funded by the Spanish Ministry of Science, Innovation, and Universities (RED2022-134703-T; PID2019-111100RB-C22/AEI/10.13039/501100011033) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602). We also appreciate the help of the Public University of Navarra for Young Researchers Projects Program (PJUPNA26-2022). Likewise, we appreciate the help of the AGH University of Science & Technology, Krakow, Poland (16.16.200.396) and the financial aid 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).

The rapid growth of electronic commerce is having an impact on the way urban logistics
are organized. In metropolitan settings, the last-mile delivery problem, i.e., the problem regarding
the final stage of delivering a shipment to a consumer, is a major concern due to its inefficiency.
The development of a convenient automated parcel lockers (APLs) network improves last-mile
distribution by reducing the number of vehicles, the distances driven, and the number of delivery
stops. Using automated parcel lockers, the last-mile issue could be overcome for the environment’s
benefit. This study aimed to define and validate an APL network containing hundreds of APLs with
the use of an example made up of real case study data from the city of Pozna ´n in Poland. The goal of
this research was to use mathematical programming for optimization and simulation to tackle the
facility location problem for automated parcel lockers through a practical approach. Multi-criteria
simulation-optimization analysis was used to assess the data. In fact, the simulation was carried out
using Anylogic software and the optimization with the use of the Java programming language and
CPLEX solver. Three years were simulated, allowing for comparable results for each year in terms
of expenses, e-shoppers, APL users, and demand evolution, as well as achieving the city’s optimal
locker usage. Finally, encouraging conclusions were obtained, such as the relationship between the
demand and the number of lockers, along with the model’s limitations., This research was partially supported by the Spanish Ministry of Science, Innovation,
and Universities (RED2018-102642-T; PID2019-111100RB-C22/AEI/10.13039/501100011033) and the
SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602). Additionally, we acknowledge the support
from the Public University of Navarra for Young Researchers Projects Program (PJUPNA26-2022).
Moreover, we appreciate the support of 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).

We aim at studying how environmental preferences matter when consumers negotiate with sellers in order to contract for delivery at home. To do that, we build an economic laboratory experiment where pairs of participants bargain for choosing either the click-and-collect option, which is free for consumer but implies for him private transportation costs, or the delivery-at-home option, which is pricey for him, but externalize transportation cost to the seller. In addition, in our game, transportation triggers environmental costs that are borne by both partners. We have 4 different treatments: The first one, as a benchmark, corresponds to an ultimatum bargaining game about the last mile cost with environmental costs. In the second one, we deliver a message about environmental impacts of transportation to the buyer, whereas, in the third one, the same message is delivered to the seller. The last one is a control where the message is delivered to both partners. The preliminary results (which included 178 participants) show that the average delivery price proposed by sellers is below Nash equilibrium price but above the "behavioral price" and that acceptance rates of seller's proposals by buyers are quite high., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (RED2018-102642-T; PID2019-111100RB-C22 AEI/10.13039/501100011033) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602). Additionally, we acknowledge the support from the Public University of Navarra for Young Researchers Projects Program (PJUPNA26-2022) and of University of Rennes. Thanks to Elven Priour (CREM CNRS, France) for programming the computer scripts for the experiment and organizing the experimental sessions at LABEX-EM, Rennes, France.

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).

E-commerce has boosted in the last decades because of the achievements of the information and telecommunications technology along with the changes in the society life-style. More recently, the groceries online purchase (or e-grocery), has also prevailed as a way of making the weekly shopping, particularly, the one including fresh vegetables and fruit. Furthermore, this type of virtual shopping in supermarkets is gaining importance as the most efficient delivery system in cost and time. Thus, we have evaluated in this study the influence of the cooperation-based policies on costs and service quality among different supermarkets in Pamplona, Spain. Concerning methodology, first of all, we carried out a survey in Pamplona having the purpose of modelling the demand patterns about e-grocery. Second, we have developed an agent-based simulation model for generating scenarios in non-cooperative, limited cooperation, and full cooperation settings, considering the real data obtained from the survey analysis. At this manner, Vehicle Routing Problems (VRP) and Multi Depot VRPs (MDVRP) are dynamically generated and solved within the simulation framework using a biased-randomization algorithm. Finally, the results show significant reductions in distance driven and lead times when employing horizontal cooperation in e-grocery distribution., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (RED2018-102642-T; PID2019-111100RB-C22/AEI/10.13039/501100011033) and the SEPIE Erasmus+ Program (2019-I-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).

The transportation sector has traditionally been considered essential for commercial activities, although nowadays, it presents clear negative impacts on the environment and can reduce social welfare. Thus, advanced optimization techniques are required to design sustainable routes with low logistic costs. Moreover, these negative impacts may be significantly increased as a consequence of the lack of synergy between the sustainability objectives. Correspondingly, the concept of transport optimization in smart cities is becoming popular in both the real world and academia when public decision making is lit by operations research models. In this paper, however, we argue that the level of urban smartness depends on its sustainability and on the level of information and communication technologies developed in the city. Therefore, the operations research models seek to achieve a higher threshold in the sustainable transport standards in smart cities. Thus, we present a generic definition of smart city, which includes the triple bottom line of sustainability, with the purpose of analyzing its effects on city performance. Finally, this work provides a consolidate study about urban freight transport problems, which show that sustainability is only one facet of the diamond of characteristics that depict a real smart city., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (RED2018-102642-T; PID2019-111100RB-C22/AEI/10.13039/501100011033) and the SEPIE Erasmus+ Program (2019-I-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. We also want to thank the support of the Public University of Navarre doctoral program. Part of the work was funded under ePIcenter project (EU-H2020 grant 861584 and INGPHD-39-2020 from Universidad de La Sabana).

E-commerce has increased tremendously in recent decades because of improvements in the information and telecommunications technology along with changes in societal lifestyles. More recently, e-grocery (groceries purchased online) including fresh vegetables and fruit, is gaining importance as the most-efficient delivery system in terms of cost and time. In this respect, we evaluate the effect of cooperation-based policies on service quality among different supermarkets in Pamplona, Spain. Concerning the methodology, we deploy, firstly, a detailed survey in Pamplona in order to model e-grocery demand patterns. Secondly, we develop an agent-based simulation model for generating scenarios in cooperative and non-cooperative settings, considering the real data obtained from the survey analysis. Thus, a Vehicle Routing Problem is dynamically generated and solved within the simulation framework using a biased-randomization algorithm. Finally, the results show significant reductions in lead times and better customer satisfaction when employing horizontal cooperation in e-grocery distribution., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C22; RED2018-102642-T), the Erasmus Program (2018-1-ES01-KA103-049767) and the la Caixa Foundation (LCF/PR/PR15/51100007) project.

Trabajo presentado en: The 12th International Workshop on Agent-based Mobility, Traffic and Transportation Models,
Methodologies and Applications (ABMTRANS)
March 15-17, 2023, Leuven, Belgium, With the rise of e-commerce and door-to-door sales, last-mile deliveries are gaining more and more importance. As a result, last-mile distribution has become one of the most sensitive logistics processes due to its uniqueness, difficulties in meeting schedules, and high costs. Therefore, this work explores the use of urban consolidation centers to ease these last-mile difficulties. For that purpose, a hub in the city center of Vienna has been selected to deliver up to 150 clients disseminated by the city. This suitability is assessed by defining convenient simulation settings in order to replicate parcel demands in the city. Experiments are based in different hub-based fleets (traditional internal combustion vehicles or electric cargo bikes), demand patterns, and delivery frequency strategies by means of a biased randomization vehicle routing optimization heuristic. Results quantify the effects of having an urban consolidation center and highlight the use of electric cargo bikes for the last-mile distribution., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (RED2018-102642-T; PID2019-111100RB-C22/AEI/10.13039/501100011033) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602). Additionally we acknowledge the support from the Public University of Navarra for
Young Researchers Projects Program (PJUPNA26-2022).

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.