Open Access
Exploring crowdshippers' behavior and preferences: intertwining urban distribution and people mobility
(Elsevier, 2025-05-14) García Herrera, Alisson Maurinne; Serrano Hernández, Adrián; Faulín Fajardo, Javier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Gobierno de Navarra / Nafarroako Gobernua
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.
Open Access
Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation
(MDPI, 2021) Carmo Martins, Leandro do; Tordecilla, Rafael D.; Castaneda, Juliana; Juan, Ángel A.; Faulín Fajardo, Javier; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
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.
Open Access
A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times
(Institut d'Estadistica de Catalunya (Idescat), 2019) Reyes-Rubiano, Lorena Silvana; Ferone, Daniele; Juan Pérez, Ángel Alejandro; Faulín Fajardo, Javier; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.
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
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
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
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
Valuations of transport nuisances and cognitive biases: a survey laboratory experiment in the Pyrenees region
(Springer, 2021) Denant-Boemont, Laurent; Faulín Fajardo, Javier; Hammiche, Sabrina; Serrano Hernández, Adrián; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
We designed a survey that aims at estimating individual willingness-to-pay to reduce noise and air pollution arising from transportation activity near the Pyrenees in Navarre (Spain). Our participants cope with a series of contingent valuation questions and also with an economic experiment with real incentives about the same topic. Our goal is to identify several methodological problems in the valuation process coming from hypothetical bias, correlation effect and sequence effect when series of responses are requested. Our main results are that hypothetical bias is significant, because the willingness-to-pay is greater when the survey is hypothetical compared to when there is real monetary incentive. Likewise, the correlation effect also observes the same behavior since the willingness-to-pay for pollution mitigation is close to the one established for noise reduction. Finally, we have obtained mixed evidence for the sequence effect, being present only in the contingent valuation survey part.
Open Access
Understanding the dynamics of crowdshipping in last-mile distribution within urban mobility: a comprehensive framework
(Elsevier, 2025-10-01) García Herrera, Alisson Maurinne; Serrano Hernández, Adrián; Faulín Fajardo, Javier; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa; Gobierno de Navarra / Nafarroako Gobernua
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.