Artículos (Organización Industrial y Gestión de Empresas I)

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

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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; Gonzalez-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.
On the dynamics of closed-loop supply chains under remanufacturing lead time variability
(Elsevier, 2020-12) Domínguez Cañizares, Roberto; Cannella, Salvatore; Ponte, Borja; Framiñán Torres, José Manuel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla; Ministerio de Educación, Universidad e Investigación. Italia; Ministerio de Ciencia e Innovación (MICIN). España
Remanufacturing practices in closed-loop supply chains (CLSCs) are often characterised by highly variable lead times due to the uncertain quality of returns. However, the impact of such variability on the dynamic benefits derived from adopting circular economy models remains largely unknown in the closed-loop literature. To fill the gap, this work analyses the Bullwhip and inventory performance of a multi-echelon CLSC with variable remanufacturing lead times under different scenarios of return rate and information transparency in the remanufacturing process. Our results reveal that ignoring such variability generally leads to an overestimation of the dynamic performance of CLSCs. We observe that enabling information transparency generally reduces order and inventory variability, but it may have negative effects on average inventory if the duration of the remanufacturing process is highly variable. Our findings result in useful and innovative recommendations for companies wishing to mitigate the negative consequences of lead time variability in CLSCs.
Remanufacturing configuration in complex supply chains
(Elsevier, 2021-06) Domínguez Cañizares, Roberto; Cannella, Salvatore; Framiñán Torres, José Manuel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla; Ministerio de Ciencia e Innovación (MICIN). España; Università degli Studi di Catania
The closed-loop dynamics of sustainable supply chains play a key role in their performance, as the reverse flows of returns and remanufactured products entail new sources of uncertainty that alter the normal performance of the supply chain. However most of the studies assume a linear supply chain with a single reverse flow of returns, which is a simplification that may not hold in most of the existent supply chains, which often show more complex structures with several flows of returns. When there are several return flows, a critical decision for the supply chain dynamic performance is whether the remanufacturing process should be centralised (i.e. a single facility jointly remanufactures the returns from all the locations) or decentralised (i.e. the remanufacturing takes place independently in each location). This paper explores the impact of this critical decision on the performance of a divergent closed-loop supply chain with several point-of-sales and reverse flows. A simulation model is developed considering different return rates, information transparency levels, and number of nodes in the supply chain. Findings reveal that a centralized configuration reduces the uncertainty in the reverse flows of remanufactured products, smoothing the production orders of the involved organizations and improving their inventory performance. As a drawback, upstream members of the supply chain may face a higher uncertainty due to some correlation of orders. As such, this configuration may not be recommended in long supply chains with a significant number of return flows and high average return volumes, unless there is a high transparency of information. Furthermore, guidelines for managers are provided in order to reduce the bullwhip effect when implementing these circular economy practices.
Fostering Entrepreneurial Mindsets: Factors Shaping Student Intentions in a Challenging Economic Landscape
(Multidisciplinary Digital Publishing Institute (MDPI), 2024-10) González-Prida, Vicente; Sandoval-Trigos, Jesús César; Moreno-Menéndez, Fabricio Miguel; Gómez-Bernaola, Kesler Osmar; Tello-Porras, Diego Alonso; Pariona-Amaya, Diana; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I
This research aims at investigating factors affecting entrepreneurial intention among university students in Selva Central-Peru. The study seeks to explore how perceptions of behavioural and social norms, as well as entrepreneurs’ self-efficacy, impact entrepreneurial intentions among students. The research method used is a quantitative one, which in turn praises data obtained through questionnaires applied to 114 active students and analysed by statistical methods. Confirmed the positive influences of self-efficacy on entrepreneurial intention are six times larger than those of attitude and subjective norms, with 79.2% explained variance, respectively, in relation to the proposal model’s findings. Results showed that although the majority of students demonstrated medium to high entrepreneurship intentions, much scope remains for increasing this. These findings confirm just how critical it is to provide an educational context that supports entrepreneurial locus of control-building and, in so doing, draws on broader conversation within the education-occupational landscape. With this, the study highlights avenues to better leverage entrepreneurship as a pathway toward sustainable livelihoods and economic inclusion in an economically divided region.
A quantitative approach for the long-term assessment of Railway Rapid Transit network construction or expansion projects
(Elsevier, 2021-10-16) Canca Ortiz, José David; Andrade Pineda, José Luis; Santos Pineda, Alicia de los; González Rodríguez, Pedro Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Economía y Competitividad (MINECO). España; Ministerio de Ciencia e Innovación (MICIN). España
One of the main issues to address in the analysis of a public railway rapid transit network construction project is the assessment of expected revenue and cost. On a network topology already defined, the problem considered in this paper consists on finding the construction schedule that maximizes the project long-term net profit, not only deciding on the construction schedule and the network operation but also determining the subsidy to compensate the service operator along the considered long-term planning horizon. Aiming at early attending the demand of citizens, partial pieces of the constructed lines are put into service as soon as they were finished. Hence, in order to determine a subsidy to compensate service operators from a possible non-profitable network operation, it is necessary to measure the variable operation costs that emerge along with the progressive enlargement of a connected network. Further, the problem can be viewed as a particular case of a multiple resource-constrained scheduling problem, where both, the budget and the construction equipment availability act as limiting resources. We propose a non-linear mixed integer programming model which is fully linearized and solved by using a two-phase branch-and-cut procedure. We illustrate the proposed methodology within a real case, the Metro network project of the city of Seville.
Truck-drone team logistics: A heuristic approach to multi-drop route planning
(Elsevier, 2020-05) González Rodríguez, Pedro Luis; Canca Ortiz, José David; Andrade Pineda, José Luis; Calle Suárez, Marcos; León Blanco, José Miguel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I
Recently there have been significant developments and applications in the field of unmanned aerial vehicles (UAVs). In a few years, these applications will be fully integrated into our lives. The practical application and use of UAVs presents several problems that are of a different nature to the specific technology of the components involved. Among them, the most relevant problem deriving from the use of UAVs in logistics distribution tasks is the so-called “last mile” delivery. In the present work, we focus on the resolution of the truck-drone team logistics problem. The problems of tandem routing have a complex structure and have only been partially addressed in the scientific literature. The use of UAVs raises a series of restrictions and considerations that did not appear previously in routing problems; most notably, aspects such as the limited power-life of batteries used by the UAVs and the determination of rendezvous points where they are replaced by fully-charged new batteries. These difficulties have until now limited the mathematical formulation of truck-drone routing problems and their resolution to mainly small-size cases. To overcome these limitations we propose an iterated greedy heuristic based on the iterative process of destruction and reconstruction of solutions. This process is orchestrated by a global optimization scheme using a simulated annealing (SA) algorithm. We test our approach in a large set of instances of different sizes taken from literature. The obtained results are quite promising, even for large-size scenarios.
Scheduling a dual-resource flexible job shop with makespan and due date-related criteria
(Springer Nature, 2020-08) Andrade Pineda, José Luis; Canca Ortiz, José David; González Rodríguez, Pedro Luis; Calle Suárez, Marcos; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I
Current struggles for customer satisfaction in make-to-order companies focus on product customization and on-time delivery. For better management of demand-mix variability, production activities are typically configured as flexible job shops. The advent of information technology and process automatization has given rise to very specific training requirement for workers, which indeed turns production scheduling into a dual-resource constrained problem. This paper states a novel dual-resource constrained flexible job-shop problem (DRCFJSP) whose performance considers simultaneously makespan and due date-oriented criteria, where eligibility and processing time are both dependent on worker expertise. Our research comes from an automobile collision repair shop with re-scheduling needs to react to real-time events like due date changes, delay in arrival, changes in job processing time and rush jobs. We have developed constructive iterated greedy procedures that performs efficiently on the large-scale bi-objective DRCFJSP arisen (good schedules in < 5 s), hence providing planners with the required responsiveness in their scheduling of repairing orders and allocation of workers at the different work centres. In addition, computational experiments were conducted on a test bed of smaller DRCFJSP instances generated for benchmarking purposes. Off-the-shelf resolution for an 80% of the medium-sized instances is not fruitful after 9000 s.
Job shop management of products under internal lifespan and external due date
(Taylor & Francis, 2018-01-30) González Rodríguez, Pedro Luis; Calle Suárez, Marcos; Andrade Pineda, José Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I
Deteriorating items are found in a wide variety of productive environments and have been extensively reported in the literature. However, the evolution of markets demand the development of new products and new forms of work are made necessary to adapt production systems to those changes. The present work focuses on the production control of perishable products in a job shop environment. Specifically, in those products that have an expiration date within the production interval (internal caducity) that must be delivered before a certain date. As far as we know, there are no previous works that focus on the internal caducity of products at the production-control level. Two systems of different nature have been compared: Workload Control (WLC) and Kanban. WLC is usually a benchmark in job shop and made to order environments. Recent studies show that Kanban, traditionally used in JIT (Just in Time) environments, performs similarly or even better than WLC. The study was performed by discrete events simulation using Python© language, SimPy© and DEAP© modules, and considering several responses of the systems. The results show that both systems have a good performance in a variety of scenarios, with overall performance of Kanban in terms of internal caducity and tardy deliveries.
The Railway Rapid Transit frequency setting problem with speed-dependent operation costs
(Elsevier, 2018-11) Canca Ortiz, José David; Andrade Pineda, José Luis; Santos Pineda, Alicia de los; Calle Suárez, Marcos; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Economía y Competitividad (MINECO). España
In this paper we deal with the problem of determining the best set of frequencies in a Railway Rapid Transit network considering convex non-linear variable operation costs at segments. The operation cost at each track will depend on the train model characteristics operating each line, the passenger load on trains and the average train speed. Given the network topology and the passenger mobility patterns, we propose a methodology to determine the best regular timetable, taking into account both, users’ and service provider points of view. Since the frequency setting and the passengers assignment are intertwined problems, the proposed procedure solves a succession of interrelated transit assignments and frequency setting models. At each iteration, given a transit assignment, the resultant frequency setting problem turns into a Mixed Integer Non-Linear model which is solved to optimality in a sequential way, both considering the different train models and the passenger load on trains. The proposed methodology is illustrated on a real-size scenario.

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