Faulín Fajardo, Javier
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Faulín Fajardo
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Javier
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Estadística, Informática y Matemáticas
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ISC. Institute of Smart Cities
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Publication 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 MatemáticasThe 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.Publication 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 PublikoaReal-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.Publication Open Access Solving vehicle routing problems under uncertainty and in dynamic scenarios: from simheuristics to agile optimization(MDPI, 2023) Ammouriova, Majsa; Herrera, Erika M.; Neroni, Mattia; Juan, Ángel A.; Faulín Fajardo, Javier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISCMany 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.