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
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
Simheuristics: an introductory tutorial
(IEEE, 2022) Juan, Ángel A.; Li, Yuda; Ammouriova, Majsa; Panadero, Javier; Faulín Fajardo, Javier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Both manufacturing and service industries are subject to uncertainty. Probability techniques and simulation methods allow us to model and analyze complex systems in which stochastic uncertainty is present. When the goal is to optimize the performance of these stochastic systems, simulation by itself is not enough and it needs to be hybridized with optimization methods. Since many real-life optimization problems in the aforementioned industries are NP-hard and large scale, metaheuristic optimization algorithms are required. The simheuristics concept refers to the hybridization of simulation methods and metaheuristic algorithms. This paper provides an introductory tutorial to the concept of simheuristics, showing how it has been successfully employed in solving stochastic optimization problems in many application fields, from production logistics and transportation to telecommunication and insurance. Current research trends in the area of simheuristics, such as their combination with fuzzy logic techniques and machine learning methods, are also discussed.
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
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
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.