Artículos (Organización Industrial y Gestión de Empresas II)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11407

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Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach
(MDPI, 2024-08) Flores-Huamán, Kenny-Jesús; Escudero Santana, Alejandro; Muñoz Díaz, María Luisa; Cortés, Pablo; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TEP127: Ingeniería de Organización
This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available.
Exploring the correlation between courier workload, service density and distance with the success of last-mile and first-mile reverse logistics
(Springer, 2024) Lorenzo Espejo, Antonio; Muñuzuri, Jesús; Onieva, Luis; Muñoz Díaz, María Luisa; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Agencia Estatal de Investigación. España; Universidad de Sevilla. TEP127: Ingeniería de Organización
Given the recent surge in online sales, particularly accentuated by the health crisis in 2020 and 2021, companies operating in the retail sector have increasingly recognised the importance of business-to-consumer (B2C) distribution. Consequently, last-mile logistics optimization has garnered increased attention in both academic and industry contexts. In this study, we examine the relationship between the workloads of couriers and their proficiency in executing assigned services in a B2C last-mile and first-mile reverse logistics environment. Additionally, we evaluate the connection between service density in an area and the distance between warehouses and service points with completion rates among couriers. By analysing a dataset corresponding to the deliveries and collections made in Madrid in 2021, we identify significant and moderate correlations between the couriers’ workloads and service completion rate. It should be noted that the correlations of completion rate with distance and visit frequency to each area are weak, yet statistically significant
Double deck elevator group control systems using evolutionary algorithms: interfloor and lunchpeak traffic analysis
(Elsevier, 2021-05) Cortés, Pablo; Muñuzuri, Jesús; Vázquez Ledesma, Alejandro; Onieva, Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
The continuous development of high-rise buildings around the world requires the installation of efficient elevator systems able to vertically transport the different passengers along the buildings in their daily journeys. Double deck elevators can increase the efficiency of these vertical transportation systems. Double deck elevators consist of two adjacent cabins that are joined and travel together along the same shaft, so the handling capacity of the system can be improved by allowing the dispatch of passengers with destination to two consecutive floors at the same instant. This type of architecture emerges as especially appropriate for uppeak traffic conditions. However, its suitability has not been sufficiently analysed for non-dominant (up or down) traffic patterns, such as interfloor and lunchpeak traffic. Our paper deals with conventionally controlled double deck elevators, where the Elevator Group Control System (EGCS) requires specific car-landing call allocation algorithms able to manage such special car architectures. Along this line, we propose a genetic algorithm that demonstrated a good performance when compared to a tabu search algorithm that was used as benchmark for comparison, taking into account different fitness evaluation functions (overall dispatching time and nearest call). The analysis was undertaken for interfloor and lunchpeak traffics and the average waiting, transit and journey times, and the energy consumption are reported as performance indexes of the vertical transportation system. The algorithms produced efficient results outperforming the considered benchmark and emerged as very competitive algorithms considering all the performance indexes as a whole. Results were tested using ELEVATE, the standard simulation software for vertical transportation.
Solving the picker routing problem in multi-block high-level storage systems using metaheuristics
(Springer Nature, 2023-06) Cano, José Alejandro; Cortés, Pablo; Muñuzuri, Jesús; Correa-Espinal, Alexander; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
This study aims to minimize the travel time in multi-block high-level storage systems considering height level constraints for picking devices to leave aisles. Considering these operating environments, the formulation of minimum travel times between each pair of storage positions is proposed and the picker routing problem (PRP) is solved by means of Genetic Algorithms (GA) and Ant Colony Optimization (ACO). A parameter tuning is performed for both metaheuristics, and the performance of the GA and ACO is compared with the optimal solution for small-sized problems demonstrating the reliability of the algorithms solving the PRP. Then, the performance of the GA and ACO is tested under several warehouse configurations and pick-list sizes obtaining that both metaheuristics provide high-quality solutions within short computing times. It is concluded that the GA outperforms the ACO in both efficiency and computing time, so it is recommended to implement the GA to solve the PRP in joint order picking problems.
Scheduling consecutive days off: A case study of maritime pilots
(Elsevier, 2021-05) Lorenzo Espejo, Antonio; Muñuzuri, Jesús; Onieva, Luis; Cortés, Pablo; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Ministerio de Economía y Competitividad (MINECO). España; Universidad de Sevilla. TEP127: Ingeniería de Organización
Pilots are essential for the operation of maritime ports and an efficient piloting workforce management is critical to provide quality service to incoming vessels, to comply with the strict labor regulations associated with piloting and to avoid penalties due to delays in service. However, designing labor schedules that meet workforce demand and fulfill both labor requirements and workers’ preferences at once can become an arduous task. This paper presents two general days-on and days-off scheduling mixed integer linear programming models, which aim to configure extended breaks for each staff member. The first model produces schedules with two long breaks of bounded durations for each worker and minimizes the difference between the employees’ workloads. Having the option to modify the minimum lengths of each of the two types of breaks allows managers to comply with the workers’ desired rest patterns, while at the same time exploiting the flexibility gained by constraining the off-periods with a lower bound and achieving fair schedules in terms of break lengths and workloads. On the other hand, the second model assigns breaks as extended as possible and minimizes the difference between the rest accumulated by the workers. Its novel formulation allows maximizing the length of the workers’ breaks, an objective rarely found in the literature, and can be adjusted to prioritize the overall duration of the off-periods or the fairness of the distribution of breaks. Results of the application of these models to the piloting workforce in a Spanish port are shown, as well as a sensitivity analysis performed in order to assess the behaviour of the models when dealing with longer planning horizons and greater workforce sizes. Additionally, an ad-hoc model is developed for the assignment of special-maneuvering turns to the pilots.
Single station MILP scheduling in discrete and continuous time
(Springer Link, 2024) Muñoz Díaz, María Luisa; Escudero Santana, Alejandro; Lorenzo Espejo, Antonio; Roel, Leus; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Agencia de Innovación y Desarrollo de Andalucía (IDEA); Universidad de Sevilla; Universidad de Sevilla. TEP127: Ingeniería de Organización
This article focuses on production planning in the metallurgical sector. This study undertakes a detailed comparative study of mixed-integer linear programming models using different time representations: continuous and discrete. The analysis shows that the continuous model consistently outperforms its discrete counterpart in all evaluated scenarios. The key difference between the continuous and discrete models is the continuous model’s ability to deliver better makespan results, achieving an improvement of up to 15% compared to the discrete model. This advantage holds even in complex environments with a high number of tasks and machines, where the continuous model consistently outperforms the discrete model by over 6% in the scenario with the highest number of tasks and machines. This preference extends beyond makespan considerations. The continuous model also maintains an edge in terms of runtime efficiency, achieving better times with a 99% improvement over the discrete model in all scenarios except one. These findings provide concrete evidence for the use of continuous models, which promise more effective production planning in analogous manufacturing domains.
Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
(Springer Link, 2024-05) Aparicio Ruiz, Pablo; Barbadilla Martín, Elena; Guadix Martín, José; Muñuzuri, Jesús; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla; Universidad de Sevilla. TEP127: Ingeniería de la Organización
Since indoor clothing insulation is a key element in thermal comfort models, the aim of the present study is proposing an approach for predicting it, which could assist the occupants of a building in terms of recommendations regarding their ensemble. For that, a systematic analysis of input variables is exposed, and 13 regression and 12 classification machine learning algorithms were developed and compared. The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain. Outdoor temperature at 6 a.m., indoor air temperature, indoor relative humidity, comfort temperature and gender were the most relevant features for predicting clothing insulation. When comparing machine learning algorithms, decision tree-based algorithms with Boosting techniques achieved the best performance. The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.
A hybrid knowledge-based method for pipe renewal planning in Water Distribution Systems with limited data: Application to Iran
(Elsevier, 2022-10) Salehi, Sattar; Robles-Velasco, Alicia; Seyedzadeh, Ali; Ghazali, Aliakbar; Davoudiseresht, Mohsen; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
This study uses a hybrid knowledge-based method for pipe renewal planning in Water Distribution Systems (WDSs), which is useful when data are limited. The method is applied to eight Iranian WDSs to demonstrate its effectiveness. An evolutionary systems design negotiation method was used to identify planning criteria for pipe renewal by a 17-member team of expert planners. A group of 48 experienced system operators then ranked the criteria by a nominal group technique. The results indicate when accurate operational data are limited, it is possible to use the combined expertise of knowledgeable planners and experienced operators for planning pipe renewal.
Estimation of a logistic regression model by a genetic algorithm to predict pipe failures in sewer networks
(Springer, 2021-09) Robles-Velasco, Alicia; Cortés, Pablo; Muñuzuri, Jesús; Onieva, Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla; Universidad de Sevilla. TEP127: Ingeniería de Organización
Sewer networks are mainly composed of pipelines which are in charge of transporting sewage and rainwater to wastewater treatment plants. A failure in a sewer pipe has many negative consequences, such as accidents, flooding, pollution or extra costs. Machine learning arises as a very powerful tool to predict these incidents when the amount of available data is large enough. In this study, a real-coded genetic algorithm is implemented to estimate the optimal weights of a logistic regression model whose objective is to forecast pipe failures in wastewater networks. The goal is to create an autonomous and independent predictive system able to support the decisions about pipe replacement plans of companies. From the data processing to the validation of the model, all stages for the implementation of the machine-learning system are explored and carefully explained. Moreover, the methodology is applied to a real sewer network of a Spanish city to check its performance. Results demonstrate that by annually replacing 4% of pipe segments, those whose estimated failure probability is higher than 0.75, almost 30% of unexpected pipe failures are prevented. Furthermore, the analysis of the estimated weights of the logistic regression model reveals some weaknesses of the network as well as the influence of the features in the pipe failures. For instance, the predisposition of vitrified clay pipes to fail and of that pipes with smaller diameters.
Prediction of pipe failures in water supply networks for longer time periods through multi-label classification
(Elsevier, 2023-03) Robles-Velasco, Alicia; Cortés, Pablo; Muñuzuri, Jesús; De Baets, Bernard; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Consejería de Economía, Conocimiento, Empresas y Universidad (Junta de Andalucía); European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla. TEP127: Ingeniería de Organización
The unexpected failure of pipes is a problem that is hitting the water networks of many cities around the world. Nowadays, many proposals based on the use of machine learning techniques are emerging to combat this problem. However, most studies focus their efforts on predicting failures in short time periods, usually a year, while longer time period predictions would be more valuable to address strategic decisions.In this study, the use of multi-label classification techniques is proposed to simultaneously predict pipe failures in water supply systems for multiple years. For this purpose, three models (discriminant analysis, logistic regression and random forest) and different prediction time periods (one, two and three years) have been analysed. As multi-label data require specific quality metrics and sampling techniques, part of this work is dedicated to their exploration and discussion.The models are evaluated on a real-world seven-year database, achieving successful results. An insightful analysis of the use of the methodology shows how the percentage of avoided pipe failures increases over time. In fact, it is demonstrated that 30.2%, 51.4% and 54.0% of the pipe failures of three consecutive years are avoided according to data from a real network.
Assessment of thermal comfort and energy savings in a field study on adaptive comfort with application for mixed mode offices
(Elsevier, 2018-05-15) Barbadilla Martín, Elena; Guadix Martín, José; Salmerón Lissén, José Manuel; Sánchez Ramos, José; Álvarez Domínguez, Servando; salmerón liss; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. Departamento de Ingeniería Energética; Andalusia Government - "Excellence Projects" P11-TEP-7247; Universidad de Sevilla. TEP127: Ingeniería de Organización; Universidad de Sevilla. TEP143: Termotecnia
The study of the thermal comfort of the occupants of a building represents an important challenge, due to its close relation with energy efficiency. Facing the application of set-point temperatures, the adaptive comfort model proposes the linking of the comfort temperature to the outdoor temperature which would potentially reduce the use of the HVAC system. Although there are studies that propose experimental adaptive models, few verify their effectiveness. In the current study an adaptive comfort algorithm for hybrid buildings is experimentally validated based on a 17-month field study in office buildings in Spain. The implementation of the algorithm in the HVAC control system, both during the cooling and the heating period, allowed for the evaluation of the energy consumption, obtaining savings of 27.5% and 11.4% respectively. The percentage of thermal sensation votes in comfort evolved from 94% (prior to implementing the comfort algorithm) to 87.5% (once implemented) for the summer season and from 79.5% to 81.6% for the winter season. The results demonstrate that the adaptive model is effective for the optimization of HVAC systems, and that it is possible to achieve energy savings without impairing the comfort of its occupants for the type of climate and buildings considered.
Building automation system with adaptive comfort in mixed mode buildings
(Elsevier, 2018-11) Aparicio Ruiz, Pablo; Barbadilla Martín, Elena; Salmerón Lissén, José Manuel; Guadix Martín, José; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. Departamento de Ingeniería Energética; Ministerio de Economía. España; Junta de Andalucía, Consejería de Economía, Innovación, Ciencia y Empleo; Universidad de Sevilla. TEP127: Ingeniería de Organización; Universidad de Sevilla. TEP143: Termotecnia
Although there are many fieldstudies to achieve a model of comfort in free running buildings, fewer studies focus on mixed-mode buildings. Moreover, there are even fewer examples of implementing such algorithms into a building automation system for testing its real validity. In this study, a methodology for implementing and validating an Adaptive Control Algorithm in mixed mode buildings is proposed. In particular, the paper shows the implantation and application of an experimental adaptive control algorithm in the current installation of an office building and without additional costs or specific hardware. The experiment seeks to find a relationship between comfort of their occupants and with energy efficiency. The implementation into the building´s system shows the real applicability and the effectiveness of the adaptive model to hybrid buildings, highlighting that the methodology proposed could be applied in another type of building. The results show that it is possible to improve the energy efficiency, while maintaining the comfort of the users using only the tools yet available in the Building Automation System of the buildings and without additional systems, no extra costs and minimum intervention in its control system
Climatic applicability of downdraught evaporative cooling in the United States of America
(Elsevier, 2018-05) Aparicio Ruiz, Pablo; Schiano-Phan, Rosa; Salmerón Lissén, José Manuel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. Departamento de Ingeniería Energética; Universidad de Sevilla. TEP127: Ingeniería de Organización; Universidad de Sevilla. TEP143: Termotecnia
The potential for application of downdraught cooling in the United States of America (U.S.) depends on its climatic characteristics. However, due to the large geographic span of the country, it varies due to differences in latitude, and a range of geographic features influencing climate, including altitude, topography and terrain. This study describes the development of climatic applicability maps of downdraught cooling in the U.S., which can aid designers in the initial identification of the correct cooling strategy for the geographic area of interest. The proposed approach is based on a set of maps, which are derived from two related climatic indexes: dry bulb temperature to wet bulb temperature depression (DBT−WBT), representing the climatic opportunity, and 26 °C minus wet bulb temperature (26 °C−WBT), representing the climatic opportunity against the theoretical cooling requirement for each location. The downdraught cooling strategy and degree of applicability is classified in the map, based on the aforementioned climatic and cooling parameters. Finally, four representative buildings in four different regions with different climatic conditions were selected for climatic analysis. This resulted in the identification of some climate zones for downdraught cooling application in the U.S. and the suggestion of appropriate design strategies for each of them
Analysis of Variables Affecting Indoor Thermal Comfort in Mediterranean Climates Using Machine Learning
(MDPI, 2023-08) Aparicio Ruiz, Pablo; Barbadilla Martín, Elena; Guadix Martín, José; Nevado, Julio; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía, grant number US-1380581; Universidad de Sevilla. TEP127: Ingeniería de Organización
To improve the energy efficiency and performance of buildings, it is essential to understand the factors that influence indoor thermal comfort. Through an extensive analysis of various variables, actions can be developed to enhance the thermal sensation of the occupants, promoting sustainability and economic benefits in conditioning systems. This study identifies eight key variables: indoor air temperature, mean radiant temperature, indoor globe temperature, CO2, age, outdoor temperature, indoor humidity, and the running mean temperature, which are relevant for predicting thermal comfort in Mediterranean office buildings. The proposed methodology effectively analyses the relevance of these variables, using five techniques and two different databases, Mediterranean climate buildings published by ASHRAE and a study conducted in Seville, Spain. The results indicate that the extended database to 21 variables improves the quality of the metrics by 5%, underscoring the importance of a comprehensive approach in the analysis. Among the evaluated techniques, random forest emerges as the most successful, offering superior performance in terms of accuracy and other metrics, and this method is highlighted as a technique that can be used to assist in the design and operation or control of a building’s conditioning system or in tools that recommend adaptive measures to improve thermal comfort.
Innovation in the Spanish retail sector: Analyzing customers' acceptance of a Scan and Go tool
(Universidad Politécnica Madrid-Cepade, 2023-07) Lorenzo Espejo, Antonio; Muñoz Díaz, María Luisa; Guadix Martín, José; Muñuzuri, Jesús; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Ministry of Universities of Spain through a grant for the Training of University Researchers-Ayuda para la Formación del Profesorado Universitario, FPU20/05584; TIER1 company PI-1868/05/2018; Universidad de Sevilla. TEP127: Ingeniería de Organización
Smart retail technologies are making their way into stores at a fast pace, as managers identify them as a great opportunity to save costs and attract clients into their shopping establishments. However, customers’ acceptance of such innovations cannot be taken for granted. Precisely, managers’ concerns about their clients’ perception of in-store innovations are one of the main obstacles for the spread of smart retail technologies. In this article, the results of a survey completed by 2,010 customers concerning the introduction in the Spanish retail sector of a self-checkout system known as Scan and Go are analyzed. This tool allows customers to use their smartphones to scan the desired products, which are automatically added to their tab. In order to finish the purchase and leave the store, clients must simply show a code generated by the app to an employee. The paper analyzes and discusses the results of the survey, mainly the respondents’ usage intention of the tool, focusing on aspects such as determining factors discussed in the literature, consumption habits and socio-economic background, and studying their influence on customer acceptance. Evidence of a positive response from customers to the introduction of the Scan and Go tool in Spanish retail establishments is found, as well as a target customer profile which can serve as a starting market segment for the deployment of the system.
From networking orientation to green image: A sequential journey through relationship learning capability and green supply chain management practices. Evidence from the automotive industry
(Elsevier, 2023) Leal Millán, Antonio Genaro; Guadix Martín, José; Criado García-Legaz, Fernando; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. Departamento de Administración de Empresas y Comercialización e Investigación de Mercados (Marketing); Fondo Europeo de Desarrollo Regional (FEDER); Junta de Andalucía; Universidad de Sevilla. TEP127: Ingeniería de Organización
Drawing on the resource-based view of the firm (RBV) and resources and capabilities theory, this study develops a model that extends our understanding of the mechanisms through which strategic assets, capabilities, and green supply chain management practices (GSCMP) contribute to green image (GI). The model comprises (i) two new antecedents of GSCMP: relationship learning capability (RL) and strategic networking orientation (NO), and (ii) the direct and mediated impacts of GSCMP and their antecedents on firms' GI. To empirically study the proposed relationships, data were collected from 106 Spanish firms in the automotive industry and analyzed using partial least squares structural equation modelling (PLS-SEM). The results indicate that NO, RL capability, and GSCMP positively affect GI through a sequential mediation relationship. An important implication is the identification of a stream of research proposing that GSCMP act similarly to a lower-order capability and that it is the interaction with other ordinary capabilities that can contribute to improving the green image.
Optimal sizing of hybrid wind-photovoltaic plants: A factorial analysis
(Elsevier, 2023) González Ramírez, Juan; Arcos Vargas, Ángel; Núñez Hernández, Fernando; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia e Innovación (MICIN). España
This research attempts to determine the optimal size (in terms of profitability) of a photovoltaic (PV) plant that is going to be added to an existing wind installation. The analysis carried out is based on a real sample of 62 wind facilities located in 25 Spanish provinces in 2021. Given the hourly energy generated throughout the year by the wind facility and its grid capacity, the optimal power of the PV plant in the hybrid facility will be the one that allows maximising the net present value of the investment, i.e., the one that allows to better adjust (in economic terms) the PV production to the characteristics of the wind farm. For our empirical analysis we need data on the day-ahead energy market price, on the grid capacity and hourly production (in the day-ahead energy market) of the 62 wind farms analysed, and on the hourly PV production in the Spanish provinces where those wind farms are located. In average terms, to maximise the Net Present Value (NPV) per € invested, the optimal PV power to be added to an existing wind farm should be 8% of the wind power already installed. The averages of the financial indicators for optimal PV sizing are promising (discount rate of 7%): NPV per € invested is 1.89, NPV is M€ 23.5, discounted payback is 4.85 years, and the internal rate of return index is 25.6%. To conclude our empirical analysis, we estimate a multilevel regression model for the hourly wind production. The regression model shows, among other results, that one more MW of wind power translates into an increase in wind gen eration of 0.31 MWh. Our findings will help the design of hybrid plants without neglecting the economic aspect.
Estimación de flujos de entrada de vehículos al puerto de Algeciras durante el desarrollo de la Operación Paso del Estrecho basada en redes neuronales
(Asociación para el desarrollo de Ingeniería de Organización, 2023-04) Aparicio Ruiz, Pablo; Portillo García-Pintos, Jesús; Onieva, Luis; Escudero Santana, Alejandro; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
El desplazamiento migratorio de la comunidad magrebí desde diferentes países de Europa al norte de África durante el periodo estival se suele realizar por medio de vehículos privados, con un volumen de tránsitos de más de 730.000 vehículos en un periodo de tres meses. Esta situación requiere el despliegue de un Plan Estatal de Protección Civil denominado Operación Paso del Estrecho que garantice una adecuada coordinación y planificación de las actuaciones de los distintos servicios intervinientes (autoridades portuarias, fuerzas y cuerpos de seguridad del estado, servicios asistenciales, etc.). En este escenario, conocer con la suficiente anticipación los flujos de vehículos en determinados puntos críticos es esencial para el desarrollo de una adecuada planificación. Este artículo presenta como la aplicación de una red neuronal, puede ser una buena solución para conocer el flujo de llegada de vehículos al puerto de Algeciras en franjas de tiempo de 8 horas, con una antelación de 16 horas a la llegada de los viajeros.
A discrete particle swarm optimization to solve the put-away routing problem in distribution centres
(MDPI, 2020) Gómez Montoya, Rodrigo Andrés; Cano, José Alejandro; Cortés, Pablo; Salazar Arrieta, Fernando; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II
Put-away operations typically consist of moving products from depots to allocated storage locations using either operators or Material Handling Equipment (MHE), accounting for important operative costs in warehouses and impacting operations efficiency. Therefore, this paper aims to formulate and solve a Put-away Routing Problem (PRP) in distribution centres (DCs). This PRP formulation represents a novel approach due to the consideration of a fleet of homogeneous Material Handling Equipment (MHE), heterogeneous products linked to a put-away list size, depot location and multi-parallel aisles in a distribution centre. It should be noted that the slotting problem, rather than the PRP, has usually been studied in the literature, whereas the PRP is addressed in this paper. The PRP is solved using a discrete particle swarm optimization (PSO) algorithm that is compared to tabu search approaches (Classical Tabu Search (CTS), Tabu Search (TS) 2-Opt) and an empirical rule. As a result, it was found that a discrete PSO generates the best solutions, as the time savings range from 2 to 13% relative to CTS and TS 2-Opt for different combinations of factor levels evaluated in the experimentation.
Flexible Job Shop Scheduling Problem with Fuzzy Times and Due-Windows: Minimizing Weighted Tardiness and Earliness Using Genetic Algorithms
(MDPI, 2022-09-20) Campo, Emiro Antonio; Cano, José Alejandro; Gómez Montoya, R.A; Rodríguez Velasquez, Elkin; Cortés, Pablo; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización.
The current requirements of many manufacturing companies, such as the fashion, textile, and clothing industries, involve the production of multiple products with different processing routes and products with short life cycles, which prevents obtaining deterministic setup and processing times. Likewise, several industries present restrictions when changing from one reference to another in the production system, incurring variable and sequence-dependent setup times. Therefore, this article aims to solve the flexible job shop scheduling problem (FJSSP) considering due windows, sequence-dependent setup times, and uncertainty in processing and setup times. A genetic algorithm is proposed to solve the FJSSP by integrating fuzzy logic to minimize the weighted penalties for tardiness/earliness. The proposed algorithm is implemented in a real-world case study of a fabric finishing production system, and it is compared with four heuristics adapted to the FJSSP such as earliest due date, critical reason, shortest processing time, and Monte Carlo simulation. Results show that the performance of the proposed algorithm provides efficient and satisfactory solutions concerning the objective function and computing time since it overperforms (more than 30%) the heuristics used as benchmarks.

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