Open Access
Evaluación ponencias debate tren altas prestaciones
(2013) Faulín Fajardo, Javier
Open Access
A reliability-extended simheuristics for the sustainable vehicle routing problem with stochastic travel times and demands
(Springer, 2025-04-01) Abdullahi, Hassana; Reyes-Rubiano, Lorena Silvana; Ouelhadj, Djamila; Faulín Fajardo, Javier; Juan, Ángel A.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
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.
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
Is time more important than distance to optimize freight delivery routes? An approach using the value of time
(Elsevier, 2024-02-23) 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 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.
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
Solving the stochastic team orienteering problem: comparing simheuristics with the sample average approximation method
(Wiley, 2023) Panadero, Javier; Juan, Ángel A.; Ghorbani, Elnaz; Faulín Fajardo, Javier; Pagès-Bernaus, Adela; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
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.
Open Access
Horizontal collaboration in freight transport: concepts, benefits and environmental challenges
(Institut d'Estadística de Catalunya, 2017) Serrano Hernández, Adrián; Juan Pérez, Ángel Alejandro; Faulín Fajardo, Javier; Pérez Bernabeu, Elena; Estatistika eta Ikerketa Operatiboa; Institute of Smart Cities - ISC; Estadística e Investigación Operativa
Since its appearance in the 1990s, horizontal collaboration (HC) practices have revealed them-selves as catalyzers for optimizing the distribution of goods in freight transport logistics. After introducing the main concepts related to HC, this paper offers a literature review on the topic and provides a classification of best practices in HC. Then, the paper analyses the main benefits and optimization challenges associated with the use of HC at the strategic, tactical, and operational levels. Emerging trends such as the concept of ‘green’ or environmentally-friendly HC in freighttransport logistics are also introduced. Finally, the paper discusses the need of using hybrid optimization methods, such as simheuristics and learnheuristics, in solving some of the previously identified challenges in real-life scenarios dominated by uncertainty and dynamic conditions.
Open Access
An extended behavior model for explaining the willingness to pay to reduce the air pollution in road transportation
(Elsevier, 2021) Sánchez García, Mercedes; Zouaghi, Ferdaous; Lera López, Fernando; Faulín Fajardo, Javier; Enpresen Kudeaketa; Ekonomia; Estatistika, Informatika eta Matematika; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Institute for Advanced Research in Business and Economics - INARBE; Institute of Smart Cities - ISC; Gestión de Empresas; Economía; Estadística, Informática y Matemáticas
Road transportation constitutes a key sector in developed countries, as an essential catalyst for economic and social activities. Nevertheless, it is relevant to emphasize the negative impacts of this activity identified in Economics as negative externalities. At the European Union, road transportation is the main cause of the air pollution impact on the population. Thus, this study explores the factors that influence the willingness to pay (WTP) on behalf of the citizens to reduce air pollution generated by road transport. In doing so, we propose two fundamental theoretical frameworks to explain individual behavior towards the environment actions: the Theory of Planned Behavior (TPB) and the Value-Belief-Norm (VBN) models. A questionnaire survey with 1,612 residents was used to collect data in 65 localities located in the Spanish Pyrenees and performing a statistical analysis with the resulting data relied on application of Structural Equation Models (SEM). Moreover, the survey results highlight the importance of psychological aspects as predictors of proenvironmental behaviors. Our empirical results provide a novel contribution about how governments and educational policies can enhance the positive attitude towards environmental actions, unifying the struggle in favor of environmental protection from early childhood.
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.