BUSCADOR RECOLECTA

Fecha de publicación completa no encontrada., Despite the positive sustainability prospects of drones, their flight range is compromised due to their limited battery capacity and the payload of delivered parcels. An alternative to address this challenge is the placement of charging stations where drone batteries are recharged to expand their flying range. The aim of this work is determining the number and location of drone charging stations for topology-dependent scenarios: rural areas and densely populated urban areas. To the best of the researchers' knowledge, there is currently no existing study in the literature that specifically investigates the impacts of topology on drone-assisted delivery. This study focuses on designing drone assignment strategies through optimization-simulation, aiming at minimizing charging station installation costs and operational costs and as a novelty, drone battery consumption is considered in the model design. Drone delivery order instances with different sizes and spatial distributions are generated to simulate realistic scenarios of demand and evaluate the optimization model to allocate the customer demands to stations and dimensioning drones fleet. Results show that considering parcel weight and flight distance has a significant impact on the performance of drone allocation to stations and highlight the effects of topology in the implementation of a drone-assisted delivery network., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2022-140278NB-I00 project and RED2022-134703-T network). Additionally, we acknowledge the support from the Public University of Navarre for Young Researchers Projects Program (PJUPNA26-2022) and the UNED Pamplona project (UNEDPAM/PI/PR24/04P).

Collaborative economy companies in the transport field have been a disruptive force in the urban mobility landscape around the world during the last decade 2010-2020. Crowdshipping has emerged as a collaborative economy option promoting improved utilization of currently underutilized transportation capacity, thereby reducing transportation costs and emissions. This article aims to analyze and synthesize existing research on the impact of the crowdshipper (individuals responsible for collecting and delivering the product) behavior on the system and the factors that drive his or her willingness to participate, to identify best practices and opportunities to enhance business analytics, decision-making, and model efficiency in this emerging area., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2022-140278NB-I00 project and RED2022-134703-T network) and the UNED Pamplona (UNEDPAM/PI/ PR24/04P project). Additionally, this work was supported by the Welcoming International Talent (WIT) Project of the Government of Navarra (Spain). This Project has received funding from the European Union¿s Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement No. 101034285.

Last-mile logistics is one of the most complicated operations in the whole logistic process. This concept describes the final leg of a product travel from a warehouse or hub to specific customers. One of the last-mile logistics challenges that courier delivery companies face is route planning. Ineffective route planning can cause operational delays that cascade and affect several last-mile deliveries. Thus, numerous factors need to be considered to plan and optimize effective delivery routes. These involve many extraordinary and unpredictable events, including weather, traffic conditions, and traffic regulations. A lack of accessible data hinders dynamic, efficient, and reliable route planning, leading to these factors being overlooked. In this paper, we propose the use of open data (OD) to overcome these limitations. OD are information available for anyone to access, reuse, and distribute for free with minimal attribution and sharing restrictions. Therefore, the aim of this work is to examine the impact of incorporating specific open data elements on the performance of the Clarke and Wright algorithm, particularly in calculating savings, and identifying optimal routes. The results we obtained showed the effect of considering OD with an increase rate of approximately 2% on the total distance compared to not considering them., This research was funded by the PID2022-140278NB-I00 project and RED2022-134703-T network from the Spanish Ministry of Science, Innovation, and Universities. Additionally, we acknowledge the support from the UNED Pamplona project (UNEDPAM/PI/PR24/04P).

Traditional approaches to optimizing freight delivery routes are based on minimizing a distance-based cost function. New approaches also use time as an objective function to minimize. However, the trade-off between time and distance is sometimes unclear. This paper presents a new approach to route optimization in which both time and distance are considered conjointly. For this purpose, the vehicle operating cost and the value of time have been used to translate time and distance into monetary units. By studying three different networks in Spain with varying levels of detail (the region of Catalonia, the city of Barcelona, and the Pamplona city center), the results show that minimizing both time and distance yield better results than the traditional approach, especially at a local level, where congestion effects are more relevant. These findings are helpful for logistics companies to optimize their operations, as well as for public authorities who could employ these models to make decisions and create policies on logistics., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2022-140278NBI00; RED2022-134703-T) 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, Spain (PJUPNA26-2022) and the UNED-Pamplona for their research projects call (UNEDPAM/PI/PR24/04P).

Crowdshipping, a collaborative economy model that takes advantage of the crowd for the delivery of goods, promises to address the problems of urban logistics. This article integrates the literature to identify relevant factors that influence the success of crowdshipping, while addressing sustainability objectives. We use the PRISMA method, a widely recognized framework for systematic reviews that, by meeting high-quality standards, guarantees the reliability of the evidence. We systematically reviewed the literature to address three research questions: identifying factors that influence crowdshipping success, evaluating its contribution to sustainability goals, and evaluating the role of Operation Research (OR) in improving crowdshipping efficiency. Specifically, OR techniques offer significant potential for optimizing routing, matching supply and demand, and enhancing decision-making processes. Through this comprehensive and in-depth analysis, we provide information for future research, modeling, practical implementation, and potential policy recommendations., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2022-140278NB-I00 project and RED2022-134703-T network) and the UNED Pamplona (UNEDPAM/PI/PR24/04P project). Additionally, this work was supported by the Welcoming International Talent (WIT) Project of the Government of Navarra (Spain). In addition, this WIT project has received funding from the European Union's Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement no. 101034285. Open access funding provided by Universidad Pública de Navarra.

Last-mile delivery of goods made by drones is considered to be in its experimental phase. Nevertheless, international enterprises such as Amazon, Google, UPS or DHL are expanding new unmanned aerial vehicle technologies related to delivery issues. Flight range of drones is compromised due to the limited battery capacity and the payload of delivered parcels. This challenge is addressed through the placement of charging stations where drone batteries are recharged. As assignment issues have not yet received much attention in the literature, this study will focus on designing drone assignment strategies through optimization. The optimization aims at minimizing charging station installation costs, drone energy consumption, and operational costs. The aim of this work is to design a model to determine the optimal number of the drone hubs, along with their configuration. Moreover, we will determine their location and size, allocating the customer demands to stations and dimensioning the drones¿ fleet in each station to deliver packages efficiently., This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2022-140278NB-I00 project and RED2022-134703-T network). Additionally, we acknowledge the support from the Public University of Navarre for Young Researchers Projects Program (PJUPNA26-2022) and the UNED Pamplona (UNEDPAM/PI/ PR24/04P project).