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
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
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
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
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
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
Pricing and internalizing noise externalities in road freight transportation
(Elsevier, 2017) Serrano Hernández, Adrián; Alvarez Indave, Pablo; Lerga Valencia, Iosu; Reyes-Rubiano, Lorena Silvana; Faulín Fajardo, Javier; Institute of Smart Cities - ISC
People living close to main roads may suffer from the nuisance of traffic and noise pollution. This paper assesses the effect of full routing cost in vehicle routing decisions by internalizing the external cost of noise. On a first step, noise externalities are economically assessed through a contingent valuation procedure. Secondly, a novel methodology is proposed to allocate the external costs to the road network links. Results show significant differences in routing planning depending on the approach: minimization of traditional internal cost versus minimization of full cost. These results encourage further research in pricing and methodologies to internalize externalities.
Open Access
Internalizing negative externalities in vehicle routing problems through green taxes and green tolls
(Institut d'Estadística de Catalunya (Idescat), 2019) Serrano Hernández, Adrián; Faulín Fajardo, Javier; Institute of Smart Cities - ISC
Road freight transportation includes various internal and external costs that need to be accounted for in the construction of efficient routing plans. Typically, the resulting optimization problem is formulated as a vehicle routing problem in any of its variants. While the traditional focus of the vehicle routing problem was the minimization of internal routing costs such as travel distance or duration, numerous approaches to include external factors related to environmental routing aspects have been recently discussed in the literature. However, internal and external routing costs are often treated as competing objectives. This paper discusses the internalization of external routing costs through the consideration of green taxes and green tolls. Numeric experiments with a biased-randomization savings algorithm, show benefits of combining internal and external costs in delivery route planning.
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 - ISC
Many 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.