Artículos (Organización Industrial y Gestión de Empresas I)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11402

Examinar

Mostrando 1 - 20 de 251

An ILS-VND approach to dynamic pricing of perishable products
(Elsevier, 2026-01-01) Villa Caro, Gabriel; Adenso-Díaz, Belarmino; Lozano Segura, Sebastián; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
This paper deals with the problem of setting prices of a perishable product whose demand decreases over time due to its perishable character and its price elasticity. It considers a discrete-time, deterministic model whose decision variables are the order quantity and the dynamic pricing policy (modelled as a series of discrete discounts in specific periods). Given the combinatorial structure of the problem and its non-linear nature, a hybrid Iterated Local Search (ILS) + Variable Neighborhood Descent (VND) metaheuristic approach is proposed. The initial solution for the search is computed using a heuristic which generally finds a good starting solution. The proposed approach is rather flexible and can accommodate many different scenarios. In particular, it has been validated on two scenarios: one involving a two-day horizon, with 1 h unit time and 12 h open/12 h closed cycle, and another one that considers a 48-day horizon, with 6 h unit time and 12/6 h open cycle. The results show that, in both cases, the proposed metaheuristic outperforms Simulated Annealing (SA), achieves a slight improvement over the heuristic, and reaches the optimal solution (verified through complete enumeration) while maintaining low computational costs. It has also been shown that profit increases of almost 20 %, compared to the no-discount policy.
Assessing sustainability indicators using inverse integer-valued data envelopment analysis with undesirable outputs
(Springer, 2024-06-24) Jahani Sayyad Noveiri, Monireh; Lozano Segura, Sebastián; Kordrostami, Sohrab; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
Conventional Inverse Data Envelopment Analysis (Inverse DEA) models examine the changes of real-valued inputs (respectively, outputs) of DMUs for the perturbation of real-valued outputs (respectively, inputs), while the efficiency values are maintained. However, in many real-world investigations, integer measures are presented. Furthermore, the sustainability analysis of systems and assessing the changes of sustainability indicators, including integer-valued factors are significant aspects for managers. Accordingly, this research develops an Inverse DEA approach to address the limitations of classical Inverse DEA models when dealing with integer-valued measures and sustainability indicators. The weighted Russell directional distance is adopted. Actually, an inverse integer-valued DEA approach with weakly disposable undesirable outputs is rendered in this study to estimate sustainability indicators. The proposed multi-objective optimization Inverse DEA technique is solved using a simple weighted sum approach with additional constraints. To validate the proposed approach, the sustainability performance of the road transport system of 24 OECD member countries and the changes of desirable and undesirable outputs considered as sustainability indicators are investigated. The results of our study highlight the significance of considering weakly disposable undesirable outputs and integer-valued data when assessing sustainability performance.
Target Setting for Digital Economy in China: A DEA Bargaining Approach with General Production Network Structure
(Elsevier, 2025) Yu, Ming-Miin; Lozano Segura, Sebastián; See, Kok Fong; Organización Industrial y Gestión de Empresas I
Over the past two decades, China's digital economy (DE) has grown significantly, and this is mainly due to investments in digital infrastructure, technological advancements, and government support. Despite this growth, regional disparities and sustainability challenges persist. This study aims to establish a more realistic and equitable target-setting framework for enhancing DE performance across provinces. To this end, we propose a generalized Network Data Envelopment Analysis (NDEA) model that incorporates a Nash bargaining mechanism, allowing for cooperative optimization of inputs, intermediate products, and outputs within a multi-stage network production structure. The methodological innovation lies in its ability to capture interdependencies among sub-processes and distinguish between desirable, undesirable, and neutral intermediate products, thereby integrating green development considerations. Empirical results reveal substantial improvement potential across provinces, with differentiated targets in areas such as energy consumption, software income, and e-commerce sales. The proposed model not only advances methodological development in NDEA but also provides policymakers with a practical tool for promoting balanced regional development and sustainable digital transformation in China.
Integer fuzzy production possibility set and slack-based integer fuzzy DEA model with hybrid extension
(Springer, 2026) Sánchez Gil, M. Carmen; Younesi, Atefeh; Lozano Segura, Sebastián; Stefanini, Luciano; Arana Jiménez, Manuel; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
This paper introduces an axiomatic approach to integer fuzzy DEA for efficiency assessment. To this end, we model the information by means of a new class of trapezoidal integer fuzzy numbers, also considering a hybrid scenario. Using integer fuzzy arithmetic and partial orders, we propose a non-oriented slacks-based integer fuzzy DEA approach that allows computing inefficiency scores as well as efficient fuzzy targets. The proposed approach involves two phases, with the phase II model required to distinguish between efficient, weakly efficient, and inefficient units. Several numerical examples are provided to illustrate and explain the new concepts and the proposed approach.
An energy management system for industrial manufacturing: A hybrid approach with demand response
(Elsevier, 2026-01) Gómez Jiménez, Javier; Framiñán Torres, José Manuel; Escaño González, Juan Manuel; Bordons Alba, Carlos; Organización Industrial y Gestión de Empresas I; Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía
This paper presents a novel matheuristic approach for a high-level energy management system (EMS) integrated with demand response, aiming to optimise energy costs and enhance renewable energy utilisation in industrial manufacturing. The primary research objective is to develop a scalable solution procedure capable of tackling the complex, NP-hard problem of energy-aware production scheduling. The system employs a hybrid approach, integrating a Mixed Integer Linear Program (MILP) within the Genetic Algorithm’s (GA) fitness function for job scheduling and minimising total energy costs while maximising renewable energy penetration and guaranteeing production constraints. The EMS is applied to a factory microgrid scenario, considering energy production from wind turbines, photovoltaic panels, and combined heat and power (CHP) plants, alongside battery energy storage systems (BESS). The manufacturing process possesses a number of realistic features, including several stages with parallel unrelated machines with different energy-consumption states, batching in some machines, or setup times, among others. The proposed solution for this case study achieves a 32% reduction in energy costs compared to baseline operation, which requires only seconds of computational effort, demonstrating its effectiveness and scalability for demand-responsive manufacturing environments. Methodology is validated using real production data, providing insights into the potential of this approach to improve both economic and environmental performance in the industrial sector.
Environmental arbitrage with battery storage: Reducing emissions from electricity generation
(Elsevier, 2025-07) Arcos Vargas, Ángel; Canca Ortiz, José David; Núñez Hernández, Fernando; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
Although the power industry has significantly reduced its emissions in recent years, society’s environmental concerns continue. Likewise, the technological and economic progress experienced and expected in energy storage systems has allowed them to be incorporated as one more tool for electricity system operations. This work develops a model that, using utility-scale energy batteries, intends to carry out an environmental arbitrage in the wholesale electricity market consisting of buying energy in those hours in which the marginal technology is non-polluting and selling it in those hours with highly polluting marginal technologies. To solve this social arbitrage problem, a mixed-integer linear programming model has been proposed. Since the problem depends on the ratio between battery and inverter sizes, without losing generality, the model is solved for a battery of 10MWh by parametrically fixing the inverter size from 1 to 8 MW, considering the battery degradation due to charge/discharge cycles and a planning horizon of 25 years. For each inverter, the optimization model provides the optimal strategy to avoid emissions. Finally, the net present value of each investment alternative is calculated by including in the positive cash-flow of each year the implicit value that society obtains from each ton of CO2 emissions avoided in that year. Our results suggest that this type of investment is socially desirable, given the current prices of emission allowances. The rate of 10 MWh/6 MW offers the most promising results in environmental temrs. Purely economic arbitrage destroys net value, with the 2 MW inverter destroying the least value.
A Data-Driven Monitoring System for a Prescriptive Maintenance Approach: Supporting Reinforcement Learning Strategies
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Ordieres Meré, Joaquín; Sánchez Herguedas, Antonio Jesús; Mena Nieto, Ángel; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
The aim of this study was to evaluate machine learning algorithms’ capacity to improve prescriptive maintenance. A pumping system consisting of two hydraulic pumps with an electric motor from a Spanish petrochemical company was used as a case study. Sensors were used to record data on the variables, with the target variable being the bearing temperature of the electric motor. Several regression models and a neural network time series model were tested to model the system variables. A bearing temperature sensitivity analysis was conducted based on the coefficients obtained from the optimization of the regression model. To fully exploit the capabilities of these techniques for application in this field, we designed a reference framework intended to foster model deployment in an industrial context by promoting the self-monitoring and updating of the models when required. The impact on decision-making processes is explored using reinforcement learning in the context of this framework.
Improving Financial Sustainability Through Effective Credit Risk Management and Human Talent Development in Microfinance Institutions
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Moreno-Menéndez, Fabricio Miguel; González-Prida, Vicente; Pariona-Amaya, Diana; Zacarías-Rodríguez, Victoriano Eusebio; Zacarías-Vallejos, Víctor; Zacarías-Vallejos, Sara Ricardina; Aguilar-Cuevas, Luis Alberto; Campos-Carpena, Lisette Paola; Organización Industrial y Gestión de Empresas I
This paper explores how credit risk management and human capital development sustain financial stability in microfinance institutions. Both qualitative and quantitative research methods allow this study to investigate credit risk management strategies while examining policies for inclusivity plus incentive plans along with debt portfolio selection efficiency. This research emphasizes that financial operations depend on skilled employees who require motivating interventions alongside training programs while developing ethical practices. The research discovers that organizations with strong credit risk management frameworks along with dedicated personnel achieve enhanced financial performances and reduced default incidents. This study confirms that microfinance institutions need both superior risk management along with human resource development systems to achieve sustainable development. This study enriches economic development research by demonstrating that implementing an equal mixture of financial and human resources produces successful economic results.
Organizational Commitment and Administrative Management in Public Service Delivery: Evidence from an Emerging Governance Context
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Moreno-Menéndez, Fabricio Miguel; Aguado-Riveros, Uldarico Inocencio; Hadi-Mohamed, Mohamed Mehdi; Tapia-Silguera, Ruben Darío; Silva-Infantes, Manuel; Vía y Rada-Vittes, José Francisco; Huaynate-Espejo, Luis Ángel; González-Prida, Vicente; Organización Industrial y Gestión de Empresas I
This study examines the relationship between organizational commitment and administrative management within a public service institution operating in an emerging governance context. Grounded in the three-component model of organizational commitment (affective, continuance, and normative) and classical administrative theory (planning, organizing, directing, and controlling), the research investigates how internal psychological bonds among frontline personnel influence institutional performance. A quantitative, cross-sectional, non-experimental design was applied, surveying 30 operational police officers using validated Likert-scale instruments. The results reveal a strong and statistically significant positive correlation between organizational commitment and administrative management (Spearman’s ρ = 0.775, p < 0.01), with normative commitment displaying the highest effect size (ρ = 0.812). These findings underscore the critical role of ethical obligation, loyalty, and affective alignment in enhancing managerial coherence and institutional responsiveness. The study contributes to ongoing debates on public sector reform and strategic human capital management by emphasizing the need for emotionally engaged and ethically anchored personnel. It aligns with Sustainable Development Goals (SDGs) 16 (Peace, Justice, and Strong Institutions) and 8 (Decent Work and Economic Growth), promoting inclusive, accountable governance and resilient administrative practices in resource-constrained environments.
A Structured Data Model for Asset Health Index Integration in Digital Twins of Energy Converters
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Gómez Fernández, Juan Francisco; Candón Fernández, Eduardo; Crespo Márquez, Adolfo; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; TEP134: Organización Industrial
A persistent challenge in digital asset management is the lack of standardized models for integrating health assessment—such as the Asset Health Index (AHI)—into Digital Twins, limiting their extended implementation beyond individual projects. Asset managers in the energy sector face challenges of digitalization such as digital environment selection, employed digital modules (absence of an architecture guide) and their interconnection, sources of data, and how to automate the assessment and provide the results in a friendly decision support system. Thus, for energy systems, the integration of Asset Assessment in virtual replicas by Digital Twins is a complete way of asset management by enabling real-time monitoring, predictive maintenance, and lifecycle optimization. Another challenge in this context is how to compound in a structured assessment of asset condition, where the Asset Health Index (AHI) plays a critical role by consolidating heterogeneous data into a single, actionable indicator easy to interpret as a level of risk. This paper tries to serve as a guide against these digital and structured assessments to integrate AHI methodologies into Digital Twins for energy converters. First, the proposed AHI methodology is introduced, and after a structured data model specifically designed, orientated to a basic and economic cloud implementation architecture. This model has been developed fulfilling standardized practices of asset digitalization as the Reference Architecture Model for Industry 4.0 (RAMI 4.0), organizing asset-related information into interoperable domains including physical hierarchy, operational monitoring, reliability assessment, and risk-based decision-making. A Unified Modeling Language (UML) class diagram formalizes the data model for cloud Digital Twin implementation, which is deployed on Microsoft Azure Architecture using native Internet of Things (IoT) and analytics services to enable automated and real-time AHI calculation. This design and development has been realized from a scalable point of view and for future integration of Machine-Learning improvements. The proposed approach is validated through a case study involving three high-capacity converters in distinct operating environments, showing the model’s effective assistance in anticipating failures, optimizing maintenance strategies, and improving asset resilience. In the case study, AHI-based monitoring reduced unplanned failures by 43% and improved maintenance planning accuracy by over 30%.
Water usage efficiency and productivity change: a non-convex metafrontier Malmquist index
(Springer, 2025) Lozano Segura, Sebastián; Borrego Marín, María del Mar; Organización Industrial y Gestión de Empresas I; Economía Aplicada III; Ministerio de Ciencia e Innovación (MICIN). España
This paper uses a non-convex metafrontier approach to study the temporal evolution and the productivity change in the water usage efficiency of 91 countries during the period 2015–2020. A circular three-stage Network Data Envelopment Analysis (NDEA) production process is considered. The non-radial Directional Distance Function (DDF) is used to compute the system efficiency as well as that of the three stages. Three sets of NDEA models are solved: contemporaneous group, intertemporal group and intertemporal metafrontier. The corresponding Malmquist index and its decomposition into efficiency change, best practice gap change and technology gap change is computed for each country and averaged for each world region. The results found indicate that water usage inefficiency seems to be limited to a relatively small number of countries and is due to shortfalls in the gross value added and treated municipal wastewater dimensions. The inefficiency in gross value added actually shows an upward trend. The inefficiency in the water withdrawal and treated municipal wastewater dimensions have remained approximately constant during this period while the inefficiency in produced municipal wastewater have decreased slightly. As regards productivity change, most regions had a negative evolution during this period. Central Asia and Middle East-Western Asia are the exception to this trend and their productivity improvement is mostly due to an improvement in their best practice gap and, to a lesser extent, in their technology gap ratios.
Transportation and delivery in flow-shop scheduling problems: A systematic review
(Elsevier, 2025-08) Fernández-Viagas Escudero, Víctor; Organización Industrial y Gestión de Empresas I; Instituto de Salud Carlos III; European Commission (EC); TEP134: Organización Industrial
This paper presents a literature review of flow-shop scheduling problems with transportation or delivery of jobs. Flow-shop scheduling problems are one of the most widely studied optimisation problems in the literature on Operations Research. Although these have traditionally been studied assuming negligible or constant transport times, this does not correspond to real manufacturing scenarios in the industry. In fact, the extensive automation and synchronisation demanded by Industry 4.0 may well be a driving factor in the growing interest in the literature on flow-shop scheduling problems with transport constraints. Despite this interest, the literature is disjointed, and many terms have been used interchangeably. This review aims to organise the literature on the topic and propose a new notation for these problems. This contribution is expected to help structure advancements in the field, classifying them by problem type. Furthermore, a detailed study is carried out on the complexity and relationship between different variants. This provides a representation of the advances discovered in the literature while also demonstrating new theoretical results, before finally identifying the most promising research directions.
Enhancing Customer Quality of Experience Through Omnichannel Digital Strategies: Evidence from a Service Environment in an Emerging Context
(MDPI, 2025-05) Moreno-Menéndez, Fabricio Miguel; Zacarías-Rodríguez, Victoriano Eusebio; Zacarías-Vallejos, Sara Ricardina; González-Prida, Vicente; Torres-Quillatupa, Pedro Emil; Romero-Girón, Hilario; Vía y Rada-Vittes, José Francisco; Huaynate-Espejo, Luis Ángel; Organización Industrial y Gestión de Empresas I; TEP134: Organización Industrial
The proliferation of digital platforms and interactive technologies has transformed the way service providers engage with their customers, particularly in emerging economies, where digital inclusion is an ongoing process. This study explores the relationship between omnichannel strategies and customer satisfaction, conceptualized here as a proxy for Quality of Experience (QoE), within a smart service station located in a digitally underserved region. Grounded in customer journey theory and the expectancy–disconfirmation paradigm, the study investigates how data integration, digital payment systems, and logistical flexibility—key components of intelligent e-service systems—influence user perceptions and satisfaction. Based on a correlational design with a non-probabilistic sample of 108 customers, the findings reveal a moderate association between overall omnichannel integration and satisfaction (ρ = 0.555, p < 0.01). However, a multiple regression analysis indicates that no individual dimension significantly predicts satisfaction (adjusted R2 = 0.002). These results suggest that while users value system integration and interaction flexibility, no single technical feature drives satisfaction independently. The study contributes to the growing field of intelligent human-centric service systems by contextualizing QoE and digital inclusion within emerging markets and by emphasizing the importance of perceptual factors in ICT-enabled environments.
Design and Assessment of Robust Persistent Drone-Based Circular-Trajectory Surveillance Systems
(MDPI, 2025-04) Andrade Pineda, José Luis; Canca Ortiz, José David; Calle Suárez, Marcos; León Blanco, José Miguel; González Rodríguez, Pedro Luis; Organización Industrial y Gestión de Empresas I; TEP216: Tecnologías de la Información e Ingeniería de Organización
We study the use of a homogeneous fleet of drones to design an unattended persistent drone-based patrolling system for vast circular areas. The drones follow flight missions supported by auxiliary on-ground charging stations, whose location and number must be determined. To this end, we first present a mixed integer non-linear programming model for defining cyclic schedules of drone flights considering the selection of the drone model from a set of candidate drone platforms. By imposing a minimum acceptable time between consecutive visits to any perimeter point, the objective consists of minimizing the total surveillance system deployment cost. The solution provides the best platform, the location of base stations, and the number of drones needed to monitor the perimeter, as well as the flight mission for each drone. We test five commercial platforms in six different scenarios whose radios vary between 1196 and 1696 m. In five of them, the MD4-100 Microdrones model achieves the lower cost solution, with values of EUR 66,800 and 83,500 for Scenarios 1 and 2 and EUR 116,900 for Scenarios 3, 4 and 5, improving its rivals in average percentages that vary between 8.46% and 70.40%. In Scenario number 6, the lower cost solution is provided by the TARTOT-500 model, with a total cost of EUR 168,000, improving by 20% the solution provided by the MD4-100. After obtaining the optimal solution, to evaluate the system robustness, we propose a discrete event simulation model incorporating uncertain flight times taking into account the possibility of accelerated depletion of drones’ Lithium-Ion polymer (Li-Po) batteries. Overall, our research investigates how various factors—such as the number of drones in the fleet and the division of the perimeter into sectors—impact the reliability of the system. Using Scenario number 3, our tests demonstrate that under a risk of battery failures of 2.5% and three UAVs per station, the surveillance system reaches a global percentage of punctually patrolled sectors of 92.6% and limits the number of delayed sectors (the relay UAV reaches the perimeter slightly above the required time, but it positively re-establishes the cyclic pattern for patrolling) to only a 5.6%. Our findings provide valuable insights for designing more robust and cost-effective drone patrol systems capable of operating autonomously over large planning horizons.
Digitalization and Dynamic Criticality Analysis for Railway Asset Management
(MDPI, 2024) Rodríguez-Hernández, Mauricio; Sánchez Herguedas, Antonio Jesús; González-Prida, Vicente; Soto Contreras, Sebastián; Crespo Márquez, Adolfo; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; TEP134: Organización Industrial
The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment.
Digital Transformation in Aftersales and Warranty Management: A Review of Advanced Technologies in I4.0
(MDPI, 2025) González-Prida, Vicente; Parra Márquez, Carlos; Viveros Gunckel, Pablo; Kristjanpoller Rodríguez, Fredy Ariel; Crespo Márquez, Adolfo; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; TEP134: Organización Industrial
This research examines how Industry 4.0 technologies such as artificial intelligence (AI), the Internet of Things (IoT), and digital twins (DT) are used in the digital transformation process of warranty management. This research focuses on converting traditional warranty management practices from reactive systems to predictive and proactive ones, improving operational performance and customer experiences. Based on an already established eight-phase framework for warranty management, this paper reviews machine learning (ML), natural language processing (NLP), and predictive analytics, among other advanced technologies, to enhance warranty optimization processes. Best practices in the automotive sector, as well as in the railway and aeronautics industries, have experienced substantial achievements, including optimized resource utilization and savings, together with tailored services. This study describes the limitations of capital investments, labor training requirements, and data protection issues. Therefore, it suggests implementation sequencing and staff education approaches as solutions. In addition to the current evolution of Industry 4.0, this research’s conclusion highlights how digital warranty management advancements optimize resources and reduce costs while adhering to international standards and ethical data practices.
Digitalization as an Enabler in Railway Maintenance: A Review from “The International Union of Railways Asset Management Framework” Perspective
(MDPI, 2025) Rodríguez-Hernández, Mauricio; Crespo Márquez, Adolfo; Sánchez Herguedas, Antonio Jesús; González-Prida, Vicente; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; TEP134: Organización Industrial
This paper conducts a comprehensive review of the role of digitalization in railway maintenance management, particularly through the lens of the International Union of Railways (UIC) asset management framework. The study aims to assess how digital technologies such as Big Data, the Internet of Things (IoT), and Artificial Intelligence (AI) serve as enablers for more efficient and effective maintenance practices in the railway sector. By employing a bibliometric analysis, we identify the current trends, challenges, and gaps in the literature concerning the integration of digital tools into maintenance management frameworks. The findings reveal that while digitalization offers significant potential for optimizing maintenance operations and enhancing decision-making processes, its successful implementation requires a more integrated approach that aligns with the strategic goals of railway organizations. This paper also discusses future research directions, emphasizing the need for a global framework incorporating technological advancements and organizational change to achieve sustainable and safe railway operations.
Leveraging Generative AI for Modelling and Optimization of Maintenance Policies in Industrial Systems
(MDPI, 2025-03) Crespo Márquez, Adolfo; Pérez Oliver, Diego; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; TEP134: Organización Industrial
This paper explores how generative AI can enhance the modelling and optimization of maintenance policies by incorporating real-time problem-solving techniques into structured maintenance frameworks. Maintenance policies, evolving from simple calendar-dependent or age-dependent preventive maintenance strategies to more complex approaches involving partial system replacement, minimal repairs, or imperfect maintenance, have traditionally been optimized based on minimizing costs, maximizing reliability, and ensuring operational continuity. In this work, we leverage AI models to simulate and analyze the implementation and overlap of different maintenance strategies to an industrial asset, including the combined use of different preventive (total and partial replacement) and corrective actions (minimal repair and normal repairs), with perfect or imperfect maintenance results. Integrating generative AI with well-established maintenance policies and optimization criteria, this paper tries to demonstrate how AI-assisted tools can model maintenance scenarios dynamically, learning from predefined strategies and improving decision-making in real-time. Python-based simulations are employed to validate the approach, showcasing the benefits of using AI to enhance the flexibility and efficiency of maintenance policies. The results highlight the potential for AI to revolutionize maintenance optimization, particularly in single-unit systems, and lay the groundwork for future studies in multi-unit systems.
Forest efficiency assessment and prediction using dynamic DEA and machine learning
(Elsevier, 2025-04) Lozano Segura, Sebastián; Gutiérrez Moya, Ester; Susaeta, Andrés; 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); TEP127: Ingeniería de Organización
This paper proposes a novel Dynamic Data Envelopment Analysis (DEA) approach to assess the efficiency of forests in providing three key ecosystem services: timber production, water yield, and carbon sequestration. Carbon sequestration is modeled as a carryover (along with plot age), while timber production and water yield are considered as outputs. Given that the inputs considered (e.g. annual precipitation and average temperature, tree density, etc) are considered non-discretionary, an output orientation is used. Using a weighted additive normalized-slacks DEA model, efficiency scores are computed for each plot over the entire time horizon and for individual periods. Additionally, efficiency scores for each ecosystem service, along with corresponding slacks (e. g., carbon sequestration shortfall per hectare), are estimated. Aggregate efficiency scores for the full sample are also derived. In a second stage, regression trees (RT) and random forest (RF) models are applied to identify plot characteristics that affect ecosystem service efficiency. A case study of of 84 forest plots in Florida reveals that overall carbon sequestration efficiency exceeds timber production efficiency, with both positively correlated. Private ownership and the implementation of management practices enhance efficiency across all three ecosystem services, particularly for timber production and carbon sequestration. However, the impact of disturbances on efficiency is less clear and appears significant only within certain elevation ranges. In terms of predictive performance, RF outperforms RT in accuracy but offers lower explainability.
Sustainable Economic Growth and Land Management: A Case Study on the Role of Tax Legislation in Emerging Markets
(MDPI, 2025) Quispe Espinoza, Edith Pilar; Barzola Inga, Sonia Luz; Adauto Justo, Carlos Antonio; Borja Mucha, Carlos Samuel; Moreno-Menéndez, Fabricio Miguel; Gutiérrez Meza, Fredi Paul; Silva Murillo, Jefrin Marlon; González-Prida, Vicente; Organización Industrial y Gestión de Empresas I
The purpose of this study is to examine how tax incentives resulting from the so-called Amazon Law (Law No. 27037) affect small- and medium-sized agro-industrial producers (SMEAPs) in the Junín and Huánuco regions in Peru. This research fills a void that relates to the exclusion of these producers regarding the Law’s incentives that aim to encourage investment in the Amazon. In this study, the research design was non-experimental, and since the data were descriptive–correlational in nature, a structured questionnaire with a Likert scale was used to gauge participants’ opinions about economic progress and tax benefits. The survey participants included 72 co-operatives drawn from a population of 88, and their awareness and use of tax incentives were targeted. SPSS and similar statistical analysis tools were used and showed that there was a positive correlation between tax benefits and economic development, with a correlation coefficient of 0.873, indicating a strong relationship. However, most co-operatives ranked the benefits only as average or poor, with 34.72% rating them as regular and 31.94% as poor. This study indicates that the present laws do not provide these producers with sufficient opportunities for development. The authors suggest that changes to the Law are required to improve the inclusion of small- and medium-sized agricultural producers so that proposals for improvements in their economic development and management of the agricultural lands in the Amazon region can be promoted.

Examinar

Envíos recientes