Artículos (Organización Industrial y Gestión de Empresas I)

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

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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; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. Departamento de 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; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Instituto de Salud Carlos III; European Commission (EC); Universidad de Sevilla. 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; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. 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; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. 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; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; Universidad de Sevilla. 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; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Universidad de Sevilla. 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; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; Universidad de Sevilla. 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; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; Universidad de Sevilla. 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; Universidad de Sevilla. Departamento de 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); Universidad de Sevilla. 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; Universidad de Sevilla. Departamento de 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.
Exploring the Effects of Financial Knowledge on Better Decision-Making in SMEs
(MDPI, 2025) González-Prida, Vicente; Pariona-Amaya, Diana; Sánchez Soto, Juan Manuel; Barzola Inga, Sonia Luz; Aguado Riveros, Uldarico; Moreno-Menéndez, Fabricio Miguel; Villar Aranda, Mark David; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I
The knowledge on financial management highly matters as it assists the micro-entrepreneurs in the making of right and sustainable business decisions. This research seeks to examine the effects of financial literacy on microenterprise decision-making in order to improve rational decision-making in financial management. A structured questionnaire with Likert-scaled options was used to measure micro-entrepreneurs’ financial decision-making capacity in terms of information processing and decision-making. They demonstrate a favorable relationship between financial education and rationality, which refers to micro-entrepreneurs’ capacity to select from a range of acceptable options. Based on the findings presented in this research, it is suggested that greater efforts should be paid to the integration of financial literacy within any form of entrepreneurial training targeting improvement in sustainability dimensions and qualities of decisions made by micro-entrepreneurs. Through increased financial knowledge, micro-entrepreneurs can manage financial problems effectively and thereby support the growth of sustainable microenterprises. Moreover, such observations suggest that all future policies must focus on and incorporate financial literacy as the defining strategy towards the improvement of the microenterprise sector and, therefore, economic growth.
Integrating Digitalization and Asset Health Index for Strategic Life Cycle Cost Analysis of Power Converters
(Multidisciplinary Digital Publishing Institute (MDPI), 2024-12) González-Prida, Vicente; Fuente Carmona, Antonio de la; Guillén López, Antonio Jesús; Gómez Fernández, Juan Francisco; Crespo Márquez, Adolfo; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Universidad de Sevilla. TEP134: Organización Industrial
In the context of energy storage systems, optimizing the life cycle of power converters is crucial for reducing costs, making informed decisions, and ensuring sustainability. This study presents a comprehensive methodology for calculating the life cycle cost (LCC) of power converters, employing a nine-step process that integrates digitalization, Internet of Things (IoT) technologies, and the Asset Health Index (AHI). The methodology adapts the Woodward model to provide a detailed cost analysis, encompassing the acquisition, operation, maintenance, and end-of-life phases. Our findings reveal significant insights into asset management, highlighting the importance of preventive and major maintenance in controlling failure rates and extending asset life. This study concludes that adopting sustainable business models and leveraging advanced technologies can enhance the reliability and maintainability of power converters, ultimately leading to more competitive and environmentally friendly energy storage solutions.
Truck-multidrone same-day delivery strategies: On-road resupply vs depot return
(Elsevier, 2025-05) Sánchez Wells, David; Andrade Pineda, José Luis; González Rodríguez, Pedro Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. TEP134: Organización Industrial; Universidad de Sevilla. TEP151: Robótica, Visión y Control
This paper explores an enhanced two-waved same-day delivery (SDD) system that leverages a mothership truck equipped with multiple drones supported by an auxiliary “resupply” truck. Under standard SDD operations, this mothership truck, also capable of performing deliveries, must return to the depot to reload, incurring extra travel time and mileage. In contrast, the proposed resupply strategy enables the second delivery wave by dispatching a secondary vehicle to meet the mothership truck on-road, reloading parcels without interrupting ongoing deliveries by the drones. A single unified routing framework, the Genetic Algorithm with Iterated Estimations for Resupply (GAIER), is presented to optimise both strategies under two selectable criteria: minimising total service time or total truck mileage. In tests with benchmark networks of different sizes (20, 50, and 75 nodes), incorporating a resupply truck reduced every selected criterion when compared to the strategy where the mothership vehicle returns to the depot. Subsequent comparative analysis points an average reduction of 17 % in service time and 21 % in truck mileage while statistical analyses support the strategy choice significancy, confirming resupply strategy’s potential for cost savings and reduced environmental impact. These findings bolster our proposition that incorporating a resupply truck into hybrid truck-multidrone systems enhances flexibility in drone delivery scheduling and improves the system’s ability to meet urban demand.
Contadores inteligentes: ahorros potenciales para el consumidor
(Publicaciones DYNA S.L., 2018) Arcos Vargas, Ángel; Luna Romera, José María; García Gutiérrez, Jorge; Riquelme Santos, José Cristóbal; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Object-oriented Bayesian network for complex system risk assessment
(SAGE Publications, 2018) Liu, Quan; Tchangani, Ayeley; Pérès, François; González-Prida, Vicente; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I
In this article, we present a novel approach of modelling risk management process for complex systems. To overcome difficulties of modelling dynamic large-scale systems, the main idea is to split it into various structural homogeneous units. The object-oriented paradigm is used to this end but, unlike previous works, the proposed methodology allows variation in terms of internal parameters throughout the objects. This novel approach based on Bayesian network techniques is referred to as extended object-oriented Bayesian network. The main contribution of this article consists in establishing algorithms and methods on how to build and run such models. This article is an extension of a communication presented at AMEST by mainly developing a more realistic case study along with other improvements.
Exploring symbiotic supply chains dynamics
(Elsevier, 2024-01) Fussone, Rebecca; Cannella, Salvatore; Domínguez Cañizares, Roberto; Framiñán Torres, José Manuel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; European Union (UE); Ministerio de Educación y Ciencia (MEC). España; Università degli Studi di Catania
Symbiotic Supply Chains – where the inputs of some members belonging to one supply chain are obtained using the waste generated by other companies in a different supply chain – represent a great opportunity for companies to move towards the Circular Economy paradigm. However, the dynamic behavior of Symbiotic Supply Chains (i.e., how detrimental time-varying phenomena of supply chains, such as the bullwhip effect, impact the performance) remains largely unknown. In this work we aim to contribute to this quite unexplored research field by analyzing the dynamics of two supply chains that implement, at the manufacturer level, a symbiotic exchange of waste. We derive closed analytical equations to estimate several indicators of the bullwhip effect under different information sharing scenarios and evaluate them for different coefficients of circularity (i.e. the average proportion of the output of one supply chain that is obtained using the waste of the other supply chain). Furthermore, we extend our analysis by carrying out a simulation study, when some underlying assumptions are removed, and a full-factorial Design of Experiments to emulate and explore the behavior of Symbiotic Supply Chains under further real-life scenarios. By doing so, we identify different properties of Symbiotic Supply Chains and provide some managerial implications. Among those, we observe how the bullwhip effect of one supply chain propagates to the other, which is also greatly influenced by the variability of the demand in both supply chains. Also, we note that high coefficient of circularity does not necessary improve the dynamic performance of Symbiotic Supply Chains. Results point out to the need to carefully design symbiotic exchange in supply chains to avoid undesirable effects that may hinder the advocated advantages of Circular Economy practices.

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