Open Access
Topology effects in drone parcel delivery
(Cal-Tek srl, 2024) Izco Berastegui, Irene; Serrano Hernández, Adrián; Faulín Fajardo, Javier; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA26-2022
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.
Open Access
A strategic multistage tactical two-stage stochastic optimization model for the airline fleet management problem
(Elsevier, 2020) Serrano Hernández, Adrián; Cadarso, Luis; Faulín Fajardo, Javier; Institute of Smart Cities - ISC
This work proposes stochastic optimization for the airline fleet management problem, considering uncertainty in the demand, operational costs, and fares. In particular, a multistage tree is proposed, compounded of strategic and tactical nodes. At the former ones, fleet composition decisions are made, while at the latter ones, aircraft assignment decisions are formulated. Computational experiments are based on a small air network with seven strategic nodes and fourteen tactical nodes (i.e., seasons) where two fleet types are available to be included: Airbus 320, and Boeing 737. These results provide the optimal fleet planning and assignment at both strategic and tactical scopes. Finally, it is shown the superior performance of the stochastic version of this problem against the deterministic one.
Open Access
Integrating simulation and optimization: a case study in Pamplona for self-collection delivery points network design
(Cal-Tek, 2023) Izco Berastegui, Irene; Serrano Hernández, Adrián; Sawik, Bartosz; Faulín Fajardo, Javier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, 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.
Embargo
Optimal charging station deployment for drone-assisted delivery
(Springer, 2025-01-31) Izco Berastegui, Irene; Serrano Hernández, Adrián; Faulín Fajardo, Javier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
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.
Open Access
Optimizing energy consumption in transportation: literature review, insights, and research opportunities
(MDPI, 2020) Corlu, Canan Gunes; Torre Martínez, Rocío de la; Serrano Hernández, Adrián; Juan Pérez, Ángel Alejandro; Faulín Fajardo, Javier; Institute for Advanced Research in Business and Economics - INARBE; Institute of Smart Cities - ISC
From airplanes to electric vehicles and trains, modern transportation systems require large quantities of energy. These vast amounts of energy have to be produced somewhere—ideally by using sustainable sources—and then brought to the transportation system. Energy is a scarce and costly resource, which cannot always be produced from renewable sources. Therefore, it is critical to consume energy as efficiently as possible, that is, transportation activities need to be carried out with an optimal intake of energetic means. This paper reviews existing work on the optimization of energy consumption in the area of transportation, including road freight, passenger rail, maritime, and air transportation modes. The paper also analyzes how optimization methods—of both exact and approximate nature—have been used to deal with these energy-optimization problems. Finally, it provides insights and discusses open research opportunities regarding the use of new intelligent algorithms—combining metaheuristics with simulation and machine learning—to improve the efficiency of energy consumption in transportation.
Open Access
The impact of integrating open data in smart last-mile logistics: the example of Pamplona open data catalog
(MDPI, 2025-01-08) Al-Rahamneh, Anas; Serrano Hernández, Adrián; Faulín Fajardo, Javier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
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.
Open Access
Simulation-optimization in logistics, transportation, and SCM
(MDPI, 2021) Juan, Ángel A.; Rabe, Markus; Goldsman, David; Faulín Fajardo, Javier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
This is a reprint of articles from the Special Issue published online in the open access journal Algorithms (ISSN 1999-4893) (available at: https://www.mdpi.com/journal/algorithms/special issues/Simulation Optimization). This book provides a selected collection of recent works in the growing area of simulation-optimization methods applied to transportation, logistics, and supply chain networks. Many of the authors that contribute to the book are internationally recognized experts in the field, as well as frequent speakers at the prestigious Winter Simulation Conference, where some of the Guest Editors organize an annual track on logistics, transportation and supply chains. Inside this track, it is usual to find several sessions on the concept of simheuristics, a special type of simulation optimization that combines metaheuristics with simulation to deal with complex and large-scale optimization problems under uncertainty conditions. The chapters in the book cover a wide area of logistics and transportation applications, from bike-sharing systems to container terminals, parcel locker systems, or e-commerce applications.
Open Access
A biased-randomized learnheuristic for solving the team orienteering problem with dynamic rewards
(Elsevier, 2020) Reyes-Rubiano, Lorena Silvana; Juan Pérez, Ángel Alejandro; Bayliss, C.; Panadero, Javier; Faulín Fajardo, Javier; Copado, P.; Institute of Smart Cities - ISC
In this paper we discuss the team orienteering problem (TOP) with dynamic inputs. In the static version of the TOP, a fixed reward is obtained after visiting each node. Hence, given a limited fleet of vehicles and a threshold time, the goal is to design the set of routes that maximize the total reward collected. While this static version can be efficiently tackled using a biased-randomized heuristic (BR-H), dealing with the dynamic version requires extending the BR-H into a learnheuristic (BR-LH). With that purpose, a 'learning' (white-box) mechanism is incorporated to the heuristic in order to consider the variations in the observed rewards, which follow an unknown (black-box) pattern. In particular, we assume that: (i) each node in the network has a 'base' or standard reward value; and (ii) depending on the node's position inside its route, the actual reward value might differ from the base one according to the aforementioned unknown pattern. As new observations of this black-box pattern are obtained, the white-box mechanism generates better estimates for the actual rewards after each new decision. Accordingly, better solutions can be generated by using this predictive mechanism. Some numerical experiments contribute to illustrate these concepts.
Open Access
Optimizing freight delivery routes: the time-distance dilemma
(Elsevier, 2024-12-01) Alvarez Indave, Pablo; Serrano Hernández, Adrián; Lerga Valencia, Iosu; Faulín Fajardo, Javier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA26-2022
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.
Open Access
Exploration of a disrupted road network after a disaster with an online routing algorithm
(Springer, 2020) Reyes-Rubiano, Lorena Silvana; Voegl, Jana; Rest, Klaus‑Dieter; Faulín Fajardo, Javier; Hirsch, Patrick; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
This paper considers the problem of supporting immediate response operations after a disaster with information about the available road network to reach certain locations. We propose an online algorithm that aims to minimize the route length required by an unmanned aerial vehicle (UAV) to explore the road accessibility of potential victim locations. It is assumed that no information about disruptions in the road network is available at the start of the exploration. The online algorithm applies two movement and three orientation strategies. Additionally, a cutting strategy is used to restrict the search space after new information about the state of single roads is obtained. We consider a road and an aerial network for the movements of the UAV, since it is not necessary to follow the route of a road any longer, if it can be marked as disrupted. In extensive numerical studies with artificial and real-world test instances, it is evaluated for different disruption levels, which combinations of movement and orientation strategies perform best. Additionally, we propose different refuelling strategies for the UAV and present how they differ in the number of refuelling operations and the required additional route length. The results show that an efficient online algorithm can save valuable exploration time.