Artículos (Organización Industrial y Gestión de Empresas I)

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(American Society for Quality (ASQ), 2026-08) de la Pisa Pascual, Miguel; Organización Industrial y Gestión de Empresas I
A two-stage constraint programming-based heuristic for the dual-resource flexible job shop problem
(Elsevier, 2026) Andrade Pineda, José Luis; Canca Ortiz, José David; González Rodríguez, Pedro Luis; Calle Suárez, Marcos; León Blanco, José Miguel; Organización Industrial y Gestión de Empresas I
This paper addresses the Dual-Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP) with heterogeneous labor, arbitrary non-linear job routes, and operation-level flexibility in both machine and worker assignments. These modelling features yield a highly expressive but computationally challenging makespan-minimization problem for which Mixed-Integer Linear Programming (MILP) approaches often struggle to find feasible solutions for medium- and large-scale instances. Leveraging the strong performance of Constraint Programming (CP) in complex scheduling, we develop a novel CP formulation solved with IBM ILOG CP Optimizer. To enhance scalability, we propose a hybrid two-layer solution strategy that decomposes the problem into sequential machine- and worker-assignment phases, both relying on the same CP model with distinct parametrizations. The first layer assigns machine under relaxed worker availability, while the second resolves worker allocations via a pseudo-machine FJSP that preserves feasibility through extended technological precedence constraints. Experiments on a new benchmark of 52 DRCFJSP instances, derived from a well-known FJSP dataset, show that the proposed method significantly outperforms the direct application of the CP model in both time and solution quality.
Centralized resource allocations with adjustment costs and a nonconvex production technology
(Elsevier, 2026-05-26) Cesaroni, Giovanni; Villa Caro, Gabriel; Lozano Segura, Sebastián; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; TEP216: Tecnologías de la Información e Ingeniería de Organización
This paper aims to extend the Data Envelopment Analysis (DEA) literature on Centralized Resource Allocation (CRA) by introducing a new model for a nonconvex technology, where the organizational units face adjustment costs to adapt inputs and outputs to those of their reference operating points. These costs imply that the optimal CRA allocation obtained from the aggregate model, which describes a long-run position, may not be optimal or even feasible in the short-run. An empirical application involving bank branches is presented and a sensitivity analysis regarding unit adjustment costs and input and output change bounds is conducted to illustrate how monetary penalties and structural rigidities constrain the optimal reallocation strategy. The results suggest that, within a CRA framework, these frictions can limit the practical reach of the efficient frontier for individual units, as the marginal cost of adjustment may outweigh the potential systemic efficiency gains. By reconciling non-convexity with adjustment frictions, this approach addresses the limitations of standard convex models in representing indivisibilities, demonstrating that neglecting these factors leads to unfeasible strategic targets in real-world centralized planning.
A mathematical programming approach to hierarchical clustering
(Springer, 2026) Amorosi, Lavinia; Puerto Albandoz, Justo; Valverde Martín, Carlos; Organización Industrial y Gestión de Empresas I; Estadística e Investigación Operativa; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Junta de Andalucía; European Union; Sapienza University of Rome
Hierarchical clustering is a statistical technique for analyzing the existing groups (clusters) within a dataset and constructing a hierarchy of clusters. This is represented by a rooted tree (dendrogram) whose leaves correspond to the data points, and each internal node represents the cluster containing its descendant leaves. Among the methods for performing hierarchical clustering, the agglomerative methods are based on greedy procedures that yield a sequence of nested partitions, where each level of the hierarchy joins two clusters from the lower partition according to a local criterion. In this work, motivated by the lack of exact approaches that guarantee global optimality, we present the first unified mathematical programming formalization of agglomerative approaches for hierarchical clustering. Through computational experiments, we validate the proposed formulations and evaluate, according to different measures commonly used in this context, the dendrograms obtained from the exact solution of the formulations and those produced by the greedy approach. Furthermore, by exploiting the mathematical formulation, we also present a scalable matheuristic algorithm capable of dealing with large-sized datasets.
Strategic business management and its role in the formalization of micro and small enterprises in emerging economies
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-03) Aguado-Riveros, Uldarico Inocencio; Barzola-Inga, Sonia Luz; Adauto-Justo, Carlos Antonio; Pariona-Amaya, Diana; Espinoza-Quispe, Luis Enrique; Poma-Lagos, Luis Alberto; González-Prida, Vicente; Navarro-Veliz, Javier Amador; Organización Industrial y Gestión de Empresas I
This study explores the connection between business management and the formalization of micro and small businesses (MSEs) in a particular developing economy environment. The main objective is to identify the business management factors that influence the business registration and compliance processes of these enterprises. This study uniquely contributes to the literature by empirically identifying and testing the specific business management factors that influence MSE formalization in an emerging economy, using a quantitative, data-driven approach. The research design utilizes quantitative methods and non-experimental and correlational elements while surveying 186 informal entrepreneurs from a total population of 361. The analysis used SPSS software version 25 on Likert-type scale survey data to identify relationships between investigated variables. The results demonstrate a weak positive association between business management and MSE formalization through their r = 0.386 Spearman correlation coefficient, which reaches statistical significance at a p-value of 0.000. In addition, positive correlations were identified between resource availability and accessibility and service quality with formalization, whereas acceptability and adaptability did not show a significant relationship (r = 0.256, p = 0.000; r = 0.359, p = 0.000). The formalization of MSEs depends on proper business management; however, a broader contextual approach is required to meet specific demands in local areas such as the study area. These findings suggest the implementation of integrated policies that improve the availability, accessibility, and quality of resources and services offered to MSEs.
Promoting photovoltaic energy: a generation model for a capacity-constrained grid
(Elsevier, 2025-09) Lugo Laguna, Daniel; Arcos Vargas, Ángel; Núñez Hernández, Fernando; Organización Industrial y Gestión de Empresas I; Agencia Estatal de Investigación. España
This study optimizes the Inverter Loading Ratio (ILR) in large-scale photovoltaic (PV) installations to maximize investment profitability and mitigate grid saturation in capacity-constrained power grids. Different ILR values have been simulated for a PV installation located in Spain, using high resolution (1-minute) energy production data. Results indicate that an ILR of 1.43 optimizes financial returns on investment, achieving an Internal Rate of Return of 9.85 % and a Net Present Value-to-Investment Ratio of 25.5 %. Our sensitivity analyses show that the optimal ILR increases slightly with increasing energy prices (PPA) and significantly with decreasing PV module costs. Furthermore, our ILR optimization process, by increasing energy output without requiring additional grid connections, offers a valuable solution in regions with limited network capacity. Our findings highlight the economic and operational benefits of PV array oversizing.
On the bullwhip effect in circular supply chains combining by-products and end-of-life returns
(Elsevier, 2025-01) Fussone, Rebecca; Cannella, Salvatore; Domínguez Cañizares, Roberto; Framiñán Torres, José Manuel; Organización Industrial y Gestión de Empresas I; European Union (UE); Ministerio de Ciencia e Innovación (MICIN). España; University of Catania; TEP134: Organización Industrial
The move towards a circular economy represents an urgent need for production and distribution systems. For this reason, traditional supply chains must rearrange their structures and configurations to reduce virgin material extraction, as well as production and end-of-life products that are sent to landfill. However, many questions regarding the dynamic behavior of circular supply chains remain unsolved. The aim of this work is to contribute to the literature on circular supply chains by investigating their dynamics when there are several reverse loops, i.e., the products delivered to the final customer have been either produced from virgin materials, by the remanufacturing of end-of-life products, or by the use of by-products generated in another supply chain. Using a full factorial design of experiments and a difference equation modelling approach, a wide range of supply chain scenarios with different return and by-product usage rates are analyzed, and their dynamic and economic performance is evaluated. Our results suggest that there is a combined effect of the return rate and the by-product rate on the dynamic behavior of the supply chain. When their combination exceeds a stability threshold, the order amplification disappears, at the expense of unmanageable inventories. However, they also show that, below this threshold, circular supply chains can outperform traditional ones.
Enhancing circular economy through industrial symbiosis: An agent-based simulation analysis of supply chain dynamics
(Elsevier, 2025) Fussone, Rebecca; Sammatrice, Caterina; Cannella, Salvatore; Domínguez Cañizares, Roberto; Organización Industrial y Gestión de Empresas I; European Commission (EC); Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Catania; TEP134: Organización Industrial
Industrial Symbiosis is recognized in the international context as a useful approach to move toward a Circular Economy. Here, the use of resources in industrial systems is improved, since waste and by-products generated by a firm are upcycled and used as input by other firms, reducing raw material extraction and waste sent to landfills. This work presents an analysis of the dynamic performance when an industrial symbiosis is implemented between two supply chains, where one represents the supplier of the other. Specifically, using the agent-based modeling simulation approach, the behavior and dynamics of the symbiotic supply chain are evaluated in different scenarios by adopting key performance indicators, such as the efficiency of symbiotic exchange, the environmental index, and the bullwhip effect. Based on our analysis, we provide useful managerial suggestions to create supportive and collaborative supply chains to support the Circular Economy.
An SDG composite index based on hierarchical DEA and cooperative game theory
(Elsevier, 2025-06) Lozano Segura, Sebastián; Saavedra Nieves, Alejandro; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Xunta de Galicia
The aim of this research is to develop a new approach to compute an SDG Composite Index (CI) aggregating the close to one hundred indicators compiled by the Sustainable Development Report (SDR). These indicators have a hierarchical structure based on the 17 SDGs. The proposed approach formulates a Common Weights Hierarchical Data Envelopment Analysis (CWH-DEA) model that allows defining a Transferable Utility (TU) game with a priori unions. The associated game belongs to the class of Airport games, for which the Owen value is used to compute the contribution of each index to the aggregate performance index. Allocating these contributions allows computing an SDG composite index for each country. The proposed approach does not require establishing a priori bounds on the importance of the different indicators or group of indicators and has been applied to 167 countries using the most recent SDR 2024 data with the corresponding results analysed and discussed. Besides, the proposed approach has been able to endogenously determine the contribution of each of the 98 indicators considered, leading to a rather balanced share distribution by SDG group. The proposed SDG CI is highly correlated with the SDG Index, identifying the OECD as the region with the highest SDG performance, followed by Eastern Europe and Central Asia, while the Middle East and North Africa, Oceania and, especially, Sub-Saharan Africa fall below the World average.
Semi-parametric estimation of the team wage frontier of European football technology
(Springer, 2026-04) Villa Caro, Gabriel; Lozano Segura, Sebastián; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
In this paper, the team wage function of the football teams in the 5 major European football leagues is estimated using information about their sports results and controlling for the national league they play, the international competitions (i.e., Champions League and Europe League) they participate each year and the fresh promotions. The dataset used covers seasons 2018/19 to 2024/25. The methodology used corresponds to stochastic semi-parametric envelopment of data with contextual variables (StoNEZD), which allows estimating the outputs shadow prices for each team and the marginal effects of the contextual variables. The inefficiency score of each team, as well as their scale efficiency and returns to scale in each season, have also been estimated. The results show significant wage differences between the different national leagues as well as higher wages for teams that participate in the international competitions (higher in the case of the Champions League than in the Europa League). Also, it was found that the wages of freshly promoted teams do not catch up immediately with respect to those of the more established clubs. We also tested the effect of the COVID-19 pandemic (seasons 2019/20 and 2020/21) but it was not found significant pandemic. The estimated average wage inefficiency is rather low for all 5 leagues, indicating that a substantial part of the wage expenses is not explained by the observed ex-post performance. Scale efficiencies are higher and have less dispersion, with a majority of teams (between 62 and 75%, depending on the league) exhibiting increasing returns to scale and another fraction (between 25 and 35%, depending on the league) exhibiting decreasing returns to scale. Interestingly, a wage floor seems to exist so that the wages become insensitive to sports results below certain thresholds.
Athletics performance in Olympic Games: a Network Data Envelopment Analysis approach
(Taylor and Francis, 2026-02) Villa Caro, Gabriel; Lozano Segura, Sebastián; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
This paper presents a Network Data Envelopment Analysis (NDEA) model to evaluate the performance of countries in Olympic athletics, distinguishing between qualification and participation stages. Unlike traditional methods, it includes all nations, regardless of medal wins, ensuring a more inclusive assessment. The model addresses the Constant-Sum-of-Outputs (CSO) constraint by scaling results relative to total points available in each Games. The two-stage framework first evaluates a country’s ability to qualify athletes, then assesses their competition performance. Three efficiency indicators—qualification, participation, and overall system efficiency—are introduced. Additionally, the Relative Athletics Gender Advancement (RAGA) index measures gender disparities by comparing rankings in men’s and women’s events. Findings seem to indicate that while some nations promote gender equality, most still favor men in elite athletics. It was also found that some developing countries (e.g. Kenya and Ethiopia) display more gender equality in their Olympic Games Athletics policies than developed countries such as USA, Brazil or UK. Overall, the analysis shows a strong correlation between consistent performance and high rankings, with dominant nations maintaining their positions through sustained excellence, while others rely on occasional successes to achieve notable efficiencies. This methodology is adaptable to other sports and competitive contexts beyond the Olympic Games.
A global malmquist productivity index of athletics performance in olympic games
(Elsevier, 2026-08) Villa Caro, Gabriel; Lozano Segura, Sebastián; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
This paper introduces a novel approach to evaluate the efficiency and productivity change of nations competing in Athletics events across the last five Summer Olympic Games, from Beijing 2008 to Paris 2024. Each nation's success is measured using a points-based system that assigns decreasing scores to the top eight finishers in each event, while population and GDP per capita are considered non-discretionary inputs. The proposed methodology builds on a three-step Data Envelopment Analysis (DEA) framework. First, an output-oriented intertemporal DEA model with variable returns to scale is used to estimate each nation's ideal performance benchmark. Second, the Weighted Tchebycheff method is applied, imposing that the total number of points awarded remains fixed in each Games, aligning the model with the constant-sum nature of Olympic competitions. Finally, a Global Malmquist Productivity Index (GMPI) is computed to capture productivity changes over time. The empirical results reveal a small group of relatively super-efficient countries, i.e. countries that outperform the others in relative terms, while the majority exhibit irregular efficiency and productivity change trajectories. The approach also shows a strong correlation between the points earned and computed efficiency scores. Additionally, a variant of the model under a non-convex technology was also developed, producing comparable results and confirming the robustness of the framework.
Big-Data-Driven Advances in Smart Maintenance and Industry 4.0
(2026-07) González-Prida, Vicente; Crespo Márquez, Adolfo; Organización Industrial y Gestión de Empresas I
Transporte de energía en HVDC. El resurgir tecnológico de la corriente continua
(Publicaciones DYNA, 2022-05) Serrano-González, Javier; Riquelme Santos, Jesús Manuel; Arcos Vargas, Ángel; Ingeniería Eléctrica; Organización Industrial y Gestión de Empresas I
Perceived Educational Marketing Mix and Student Satisfaction in Higher Education: An Empirical Analysis in a Latin American Emerging Economy
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-07) Moreno-Menéndez, Fabricio Miguel; Solis-Tapia, Nataly Gabriela; Cuadros-Espinoza, José Antonio; Olivera-Bordaes, Karina Rosario; Peña-Ricapa, Isabel Liz; González-Prida, Vicente; Mendoza-Herrera, Joseph; Rivera-Paucarpura, Angela Maria; Organización Industrial y Gestión de Empresas I
Universities increasingly compete through value propositions, service processes, and communication ecosystems rather than through program supply alone. Yet evidence remains limited on how students’ perceptions of the educational marketing mix are associated with satisfaction in non-metropolitan higher education settings in Latin America. This study examines a private university branch campus in a Latin American emerging economy using a quantitative, cross-sectional, non-experimental design and a probabilistic stratified sample of 287 complete student responses. Educational marketing was operationalized as students’ perceived evaluation of the educational marketing mix—product, price, place, and promotion—whereas satisfaction was measured with SERVQUAL-informed, performance-oriented service-evaluation items. The findings show a strong positive association between the perceived educational marketing mix and student satisfaction, with promotional communication emerging as the most closely related dimension. Descriptively, both constructs were concentrated at intermediate levels, indicating an acceptable but non-distinctive institutional experience and pointing to service weaknesses in security, empathy, and responsiveness. The article contributes by problematizing the use of marketing-mix logic in higher education, clarifying that satisfaction is not a proxy for educational quality or belonging, and showing how perceived value communication and service delivery are connected with students’ satisfaction judgments in an emerging-economy context.
Aggressive centralized DEA approach for variable selection: application to OECD countries SDG performance
(Elsevier, 2026) Villa Caro, Gabriel; Lozano Segura, Sebastián; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
This paper introduces a novel Aggressive Variable Selection method for Data Envelopment Analysis (DEA), based on a Centralized DEA approach. Unlike some existing benevolent approaches, based on multiplier formulations, which maximize the efficiency of decision-making units (DMUs), the proposed model uses an envelopment formulation that seeks to maximize the total inefficiency in the sample, thereby enhancing discriminant power in variable selection. Owing to its nonlinear structure, the model is reformulated as a bi-level optimization problem. Once the most discriminant inputs and outputs are identified for a given total number of variables, a conventional (i.e., non-centralized) DEA model is used to compute the efficiency scores. The process is repeated for successively larger subsets of variables until a trade-off is attained between using as many variables as possible and having an acceptable level of discrimination. The approach provides robust efficiency scores and estimations of the discriminating importance of the variables. The proposed approach is first illustrated using a small benchmark dataset and compared with two existing variable selection methods from the literature. Then, the method is applied to the evaluation of OECD countries based on Sustainable Development Goal (SDG) indicators, a high-dimensional dataset characterized by a large number of inputs and outputs relative to the number of decision-making units. This suggests that an aggressive criterion in variable selection yields greater discrimination among units and provides a sharper assessment of variable relevance by emphasizing performance differences among DMUs.
Reliability modelling and requirement derivation for automated railway inspection systems under operational fallback
(Elsevier, 2027-01) González-Prida, Vicente; Crespo Márquez, Adolfo; Marcos Alberca, José Antonio; Fuente Carmona, Antonio de la; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España
Automated inspection systems are increasingly deployed in railway maintenance to reduce workshop-based inspection workload and to support availability-driven planning. Yet, there is limited guidance on how to derive verifiable reliability requirements that are explicitly conditioned by the operational and economic consequences of workshop fallback when automation is unavailable. This paper proposes a consequence-conditioned requirement-derivation framework that (i) models inspection-system availability bottom-up from a modular decomposition, (ii) quantifies fallback-induced functional loss through additional workshop workload, and (iii) formulates an economic admissibility constraint that yields a maximum admissible failure-rate threshold (equivalently, a minimum mean time between failures (MTBF)) consistent with predefined operational-economic targets. The structural properties of this admissibility constraint (feasibility and monotonicity) are analytically characterised to support transparent robustness assessment. The admissible threshold is then operationalised via back-propagation, cast as constrained lifetime-tuning to produce auditable element-level procurement targets under alternative allocation policies. The framework is demonstrated through an industrial automated underframe inspection system, showing how an MTBF requirement for a selected inspection subsystem can be derived from a break-even condition between annual benefit and expected fallback cost, and how required lifetime improvements depend on the allocation policy. The approach positions reliability modelling as a decision-support mechanism for validating the operational and economic viability of automated maintenance-inspection architectures.
A sample average approximation approach for an integrated operating room planning and scheduling problem under uncertainty: A real case study
(Elsevier, 2026) Molina Pariente, José Manuel; Fernández-Viagas Escudero, Víctor; Leal, Sandra; Gomez-Cia, Tomas; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía; European Union (UE)
This paper presents a real case of an operating room planning and scheduling problem under stochastic surgery durations and the arrivals of non-elective patients. This problem arises from the Plastic Surgery and Major Burns service at a Spanish hospital. The objective is twofold: (1) to determine the best sequence of patients in each operating room per day to minimize the total expected cost of surgical resources, and (2) to prioritize elective patients with the highest clinical weights (medical priority and waiting time). The service uses a dedicated operating room distribution policy, i.e., each operating room available on a given day within the planning horizon is assigned exclusively to either elective or non-elective patients. To the best of our knowledge, this problem has not been previously reported in the literature. To solve this stochastic problem in practice, we propose a sample average approximation approach that combines a deterministic metaheuristic and Monte Carlo simulation. To decide which deterministic metaheuristic to embed most efficiently in the procedure, we compare a new iterated greedy algorithm with the most promising metaheuristics from the literature. The results show that the proposed iterated greedy metaheuristic is statistically the best method for solving the deterministic version of the problem. Regarding the sample average approximation, the results show that this procedure converges at an exponential rate with the number of samples using real data from the service under study, resulting in an optimality index value of approximately 1.0%. We also analyze the impact of the sample size in solving the real problem, aiming to balance the robustness of the solution and the key performance indicators set by the hospital. Finally, we discuss several managerial insights for the service under study, including a comparison of dedicated and flexible operating room distribution.
Goal-Induced Pareto Fronts for a Bi-Criterion Truck–Multiple-Drone Routing Problem
(Multidisciplinary Digital Publishing Institute (MDPI), 2026) González Rodríguez, Pedro Luis; Sánchez Wells, David; León Blanco, José Miguel; Calle Suárez, Marcos; Andrade Pineda, José Luis; Organización Industrial y Gestión de Empresas I; TEP216: Tecnologías de la Información e Ingeniería de Organización
Truck–multiple-drone routing problems involve conflicting operational criteria and are therefore naturally suited to multiobjective analysis. In practical settings, however, decision makers may also specify aspiration levels for the considered criteria, which call for a target-oriented perspective. This paper studies a bi-criterion truck–multiple-drone routing problem through a goal-induced deviation framework in which the original objectives are transformed to normalized positive deviations with respect to prescribed targets. First, a general mathematical framework is introduced, and several structural properties are established, including dominance preservation, invariance under positive weighting, equivalence with the original Pareto structure when all the targets are violated, and the loss of discrimination when the targets are attainable. To address this latter effect, an enhanced goal-programming scalarization is proposed and shown to preserve consistency with the Pareto efficiency. The framework is then specialized to a truck–multiple-drone routing problem with truck time and makespan as criteria and evaluated on representative benchmark instances together with a broader attainable-target benchmark battery, using a common agent-based metaheuristic search framework adapted from literature. This search framework is employed both to estimate a reference Pareto frontier and to solve the GP and EGP scalarizations under the same computational scheme. The computational results illustrate two target regimes: When the targets are unattainable, both formulations are mainly driven by the minimization of positive deviations; when they are attainable, classical goal programming may return satisfactory but dominated solutions, whereas the enhanced formulation preserves discrimination and selects Pareto-efficient alternatives.
Monitoring network infrastructures with drones: Application to the railway commuter network of Madrid
(Elsevier, 2026) Canca Ortiz, José David; León Blanco, José Miguel; Andrade Pineda, José Luis; González Rodríguez, Pedro Luis; Calle Suárez, Marcos; Organización Industrial y Gestión de Empresas I; TEP216: Tecnologías de la Información e Ingeniería de Organización
The reliable monitoring of civil network infrastructures is a critical component of modern maintenance and safety strategies, particularly in transportation systems such as commuter railways. Traditional inspection methods, which rely on human teams and specialized vehicles, are costly, infrequent, and prone to operational limitations. This research proposes a comprehensive methodological framework for optimizing drone-based monitoring of large-scale railway networks. The methodology divides the network into non-overlapping linear segments and determines the optimal flight paths and recharging locations to ensure full coverage within the defined operational parameters. From the set of non-overlapping segments, we propose a double-direction vehicle routing problem formulation to assign routes to drones, integrating constraints related to drone endurance, charging rates, and mission duration. The proposed approach is applied to the commuter railway network of Madrid, comprising 391 km of tracks and 92 stations. The computational results demonstrate that complete daily monitoring can be achieved using a fleet of 15 drones and 42 recharging bases, offering a substantial improvement in the inspection frequency compared to conventional methods at a slightly lower annual cost. A sensitivity analysis further highlights the influence of drone endurance and shift monitoring length on the fleet size and infrastructure requirements. The findings confirm the feasibility and cost-effectiveness of integrating optimization-based mission planning with unmanned aerial vehicle technologies, providing a scalable and efficient solution for the recurrent monitoring of complex network infrastructures.

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