Organización Industrial y Gestión de Empresas II

URI permanente para esta comunidadhttps://hdl.handle.net/11441/11406

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A brief review on vertical transportation research and open issue
(2016) Cortés, Pablo; Onieva, Luis; Guadix Martín, José; Muñuzuri, Jesús; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; TEP127: Ingeniería de Organización
Vertical transportation refers to the movements of people in buildings. High-rise buildings have emerged as a common construction nowadays. In such buildings, the vertical transportation is extremely difficult to manage, specially, when the people arrive at the same time at specific floors wanting to travel to other floors. To solve such situations, the installation of elevator group control systems (EGCS) is a usual practice. EGCS are used to manage multiple elevators in a building to efficiently transport passengers. EGCSs need to meet the demands by assigning an elevator to each landing call while optimizing several criteria. This paper reviews the most relevant contributions in vertical transportation industry
A comprehensive framework to efficiently plan short and long-term investments in water supply and sewer networks
(Elsevier, 2022-03) Ramos Salgado, Cristóbal; Muñuzuri, Jesús; Aparicio Ruiz, Pablo; Onieva, Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; EMASESA (Empresa Metropolitana de Abastecimiento y Saneamiento de Aguas de Sevilla) grant number 273/17; EMASESA (Empresa Metropolitana de Abastecimiento y Saneamiento de Aguas de Sevilla) grant number 286/17; Universidad de Sevilla. TEP127: Ingeniería de Organización
Water supply and sewer networks are critical infrastructures that provide a basic service to society. However, these systems constantly age and degrade over time. In addition, since network infrastructures are so extensive in length, they require a significant investment in maintenance tasks. Hence, within the context of infrastructure asset management (IAM), accurately defining the most efficient investment planning possible is essential to ensure their long-term sustainability. This paper presents an original five-step comprehensive framework to successfully implement an infrastructure asset management strategy and plan long-term investments. Moreover, this methodology integrates innovative and relevant operational and convenience factors that, while provide the problem both with realism and practicality, have not been addressed so far. To illustrate the usefulness and applicability of this methodology, the case study of a large water company in Spain is presented.
A decision support system to design water supply and sewer pipes replacement intervention programs
(Elsevier Ltd, 2021) Ramos Salgado, Cristóbal; Muñuzuri, Jesús; Aparicio Ruiz, Pablo; Onieva, Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II
Asset management in hydraulic infrastructures aims for the long-term sustainability of water distribution and wastewater networks. Strategic maintenance planning has thus been deeply analyzed in the literature for indi vidual water and sewer pipes. However, water utilities do not plan and perform replacement activities on in dividual elements, but rather on coherent aggregations of neighboring pipes. We have developed a decision support system (DSS) to help water utilities design intervention programs for hydraulic infrastructures. It in tegrates a two-stage algorithm that groups water supply and sewer pipes into practical and efficient replacement works, based upon their proximity and their priority of renewal. A multi-objective genetic algorithm optimizes the work programs configurations while integrating the water company’s strategic policy into an innovative multi-objective function. We have applied our methodology to a large water company in Spain and illustrated this application with a sensitivity analysis to determine how the company’s strategic criteria influences the resulting work configurations.
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.
A evolutionary algorithm for dynamically optimisation of drayage operations
(IEEE, 2010) Muñuzuri, Jesús; Guadix Martín, José; Onieva, Luis; Escudero Santana, Alejandro; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I
Proper planning of drayage operations is fundamental in the quest for the economic viability of intermodal freight transport. The work we present here is a dynamic optimization model which uses real-time knowledge of the fleet's position, permanently enabling the planner to reallocate tasks as the problem conditions change. Stochastic trip times are considered, both in the completion of each task and between tasks.
A field study on adaptive thermal comfort in Spanish primary classrooms during summer season
(Elsevier, 2021-10) Aparicio Ruiz, Pablo; Barbadilla Martín, Elena; Guadix Martín, José; Muñuzuri, Jesús; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
The assessment of indoor thermal comfort in schools has become an essential object of study; however, applying existing thermal comfort criteria would assume children and adults have a similar range of thermal comfort, without considering discrepancies regarding their level of activity or their behavioural adaptation. Therefore, the objective of the present study was to investigate the thermal comfort in a school building based on an adaptive thermal comfort field study in Seville, in the southwest of Spain, during a summer season. In this study, 2 free-running and 1 air-conditioned classroom were analysed; 67 students aged 10–11 years participated and 2010 thermal questionnaires were collected. A discrepancy was observed between the predicted mean vote and the thermal sensation vote, showing the former is not a good predictor of thermal perception. Thermoneutrality was not always the desired sensation for children; a preference for coolness was detected. A neutral temperature was observed at an average indoor temperature of 24–27 °C and a widening in the thermal comfort range was detected compared with international standards. Regarding adaptive strategies, they showed a preference towards opening windows and doors over using fans or changing clothes. The results suggest that the application of the current models for adults would not be suitable for estimating the thermal comfort of children, and these data could be used to promote natural strategies for assessing thermal comfort over conditioning systems in schools, with the aim of both space ventilation and energy efficiency.
A genetic algorithm for controlling elevator group systems
(Springer, 2003) Cortés, Pablo; Larrañeta Astola, Juan Carlos; Onieva, Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II
The efficient performance of elevator group system controllers becomes a first order necessity when the buildings have a high utilisation ratio of the elevators, such as in professional buildings. We present a genetic algorithm that is compared with traditional controller algorithms in industry applications. An ARENA simulation scenario is created during heavy lunchpeak traffic conditions. The results allow us to affirm that our genetic algorithm reaches a better performance attending to the system waiting times than THV algorithm.
A Genetic Algorithm for Real-Time Optimisation of Drayage Operations
(2009) Escudero Santana, Alejandro; Muñuzuri, Jesús; Onieva, Luis; Gutiérrez Moya, Miguel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
Proper planning of drayage operations is fundamental in the quest for the economic viability of intermodal freight transport. The work we present here is a dynamic optimization model which uses real-time knowledge of the fleet’s position, permanently enabling the planner to reallocate tasks as the problem conditions change. Stochastic trip times are considered, both in the completion of each task and between tasks.
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.
A Hybrid Metaheuristic for the Omnichannel Multiproduct Inventory Replenishment Problem
(MDPI, 2022-04) Lorenzo Espejo, Antonio; Muñuzuri, Jesús; Guadix Martín, José; Escudero Santana, Alejandro; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
In the current paradigm for the retail industry, which is experiencing a rapid evolution, especially in textile companies, the generic problem of product allocation in a distribution and supply chain consisting of one main warehouse and several locations, belonging to different sales channels, is a challenge. The omnichannel replenishment process focuses on dynamically optimizing a shop or intermediate warehouse inventory for a wide range of products based on a forecast of sales, in order to fulfill the demand of all of the channels considered. In this context, the aims of this work were (a) to optimize inventory replenishment for multiple channels and products that are not perishable but devalue over time, and (b) to implement a methodology that combines the benefits of the Particle Swarm Optimization metaheuristic and Simulated Annealing. This study was carried out for different sales periods, channels and product configurations by performing a sensitivity analysis between the way new solutions are updated and the degree of intensification used in local search.
A machine learning approach to predict pipe failures in water distribution networks
(2022-02-18) Robles-Velasco, Alicia; Cortés, Pablo; Muñuzuri, Jesús; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II
This PhD thesis addresses the problem of the appearance of unexpected pipe failures in water distribution networks. Specifically, it seeks to predict such failures using machine learning based techniques. An in depth literature review on the subject informs that although there are studies that have tested certain machine learning techniques for the aforementioned purpose, this is a novel issue that has not been fully explored yet. Consequently, this work proposes several machine learning models, some of which have not been applied to this problem before and analyses the most significant aspects of data processing and evaluation of the results. The nature and characteristics of the data are key points on the design of a machine learning system. For the development of this thesis, the company that manages the water distribution network of Seville (Spain) called EMASESA has provided an extensive database. Concretely, the database consists of a seven year pipe failure history, from 2012 to 2018, and includes various factors related to each of the pipes that compose the more than 3800 kilometres network. The first strategy has been to forecast pipe failures one year in advance, since companies generally decide their maintenance and replacement plans annually. Therefore, and according to the characteristics of the problem and the available data, the following machine learning techniques are proposed: discriminant analysis, logistic regression, support vector machines, random forests, artificial neural networks and evolutionary fuzzy logic. All these models can work as classifiers, being the main part of a supervised classification machine learning system. In this case, the output of the system is defined as a binary variable that takes the value 1 when a pipe fails in the period of study, and 0 otherwise. Secondly, the initial focus of this thesis was extended to multi label classification, which allows predicting more than one output variable at the same time. The aim of this new approach was to predict pipe failures over longer time periods based on currently available data, specifically, over several consecutive years. This long term information is really valuable for companies to improve their strategic decisions. The study of the different data processing strategies has been one of the challenges of this work as it is an essential phase for the correct development of a machine learning system. For this purpose, a descriptive analysis of the database has been performed to discover possible anomalies such as missing values, outliers, etc., as well as other processing needs. Moreover, the relationships between different factors (pipe material, diameter, length of the section, age, previous failures, etc.) have been analysed through the correlation matrix, scatter plots and histograms. In addition, potential connections between the factors and the breakage are examined. It should be noted that on many occasions descriptive analysis in big data applications helps to find hidden patterns that are imperceptible to humans. Therefore, it is a valuable source of information without the need to generate predictions, being an almost mandatory step before designing a predictive system. As previously mentioned, the predictions’ accuracy depends to a great extent on the data processing. Each data requires a different treatment according to its nature, for instance, if it is a continuous or integer number, a category or even an audio visual content. A relevant aspect of this work has been the study of sampling strategies since the database is totally unbalanced. This is a common characteristic of classification problems where one class has a much higher presence than the others. The imbalance problem can cause machine learning models to prioritize the forecast of the majority class (the non failures), disregarding the correct prediction of the minority class (the pipe failures). Specifically, the use of under and over sampling techniques is evaluated and the adaptation of these strategies to the case of multi label classification. Python is the programming language used to read and process the data, as well as to implement the models and analyse the results. This programming language offers multiple open source libraries that are really useful to develop machine learning systems. First, the models are calibrated in order to enhance their performance and to adjust their hyperparameters to the study problem. The results are then evaluated using specific quality metrics such as the confusion matrix or the receiver operating characteristic (ROC) curve. The analysis of the results proves that 34.5% of the annual pipe failures could be avoided by replacing only 5% of the water distribution network pipes. Furthermore, this value is a lower threshold that increases when the time period to predict for grows by using the multi label classification approach. This study highlights the importance of having robust and reliable databases. Among all the factors used in the study, the pipe material, the section length and the frequency of failures have demonstrated to be the most influential variables in the occurrence of new failures. Although the currently available data allow obtaining high quality predictions, adding new factors such as those related to weather conditions, could be a substantial improvement. For this reason, water network management companies are encouraged to periodically review and take care of their data storage and management policy. The proposed methodology has a direct application in the industry as the models provide scores associated with each pipe section that can be understood as failure probabilities. Consequently, a future line of research should be the integration of the proposed approach with the geographic information systems (GIS) in order to develop an infrastructure asset management tool able to generate efficient maintenance and replacement plans of pipes considering economic and social limitations. For this purpose, it would be necessary to include additional factors related to the consequences of pipe failures such as the number of people affected, whether or not the pipe supplies water to sensitive clients like hospitals, schools, etc., as well as the possible environmental damage.
A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems
(Elsevier, 2013) Bolat, Berna; Altun, Oğuz; Cortés, Pablo; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II
High-rise buildings require the installation of complex elevator group control systems (EGCS). In vertical transportation, when a passenger makes a hall call by pressing a landing call button installed at the floor and located near the cars of the elevator group, the EGCS must allocate one of the cars of the group to the hall call. We develop a Particle Swarm Optimization (PSO) algorithm to deal with this car-call allocation problem. The PSO algorithm is compared to other soft computing techniques such as genetic algorithm and tabu search approaches that have been proved as efficient algorithms for this problem. The proposed PSO algorithm was tested in high-rise buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars. Results from trials show that the proposed PSO algorithm results in better average journey times and computational times compared to genetic and tabu search approaches.
A PLS multigroup analysis of the role of businesswomen in the tourism sector in Andalusia
(Dąbrowa Górnicza, 2020) García-Machado, Juan J.; Barbadilla Martín, Elena; Gutiérrez Rengel, Cristina; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
The objective of this study is to determine the factors that influ-ence “business performance” or the “role of entrepreneurs”, as well as to analyse whether gender functions as a moderating variable, based on a survey conducted on a sample of 127 com-panies. Most of the literature in the tourism sector only considers hotel accommodation; thus, it is still limited in the case of non-hotel accommodation. Our research fills this gap by focusing on both types of establishments. An explanatory and confirmatory model has been carried out based on a PLS-SEM approach, taking the factors that determine business performance into ac-count. Moreover, a MICOM and a multi-group analysis have been undertaken in order to check whether gender acts as a modera-tor. Our findings reveal that there are no significant differences between tourist accommodation companies run by women and men in Andalusia; hence, gender is not a moderating variable regarding business performance. Furthermore, as full measure-ment invariance has been established, a comparison between groups emphasises that the influence of the environment, and the resources and capabilities of the entrepreneur, are factors that affect women more than men, and the number of employ-ees, work experience, and the occupancy rate are more impor-tant for businesswomen, whereas the distance to the nearest airport, check-in, and the occupancy rate are more important for businessmen. This empirical study has practical implications for hospitality industry professionals and concerned authorities which are responsible for designing strategies and policies related to this sector in Andalusia.
A satellite navigation system to improve the management of intermodal drayage
(Elsevier, 2011) Escudero Santana, Alejandro; Muñuzuri, Jesús; Arango Pastrana, Carlos Alberto; Onieva, Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II
The intermodal transport chain can become more efficient by means of a good organization of the drayage movements. Drayage in intermodal container terminals involves the pick up or delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. The literature shows some works on centralised drayage management, but most of them consider the problem only from a static and deterministic perspective, whereas the work we present here incorporates the knowledge of the real-time position of the vehicles, which permanently enables the planner to reassign tasks in case the problem conditions change. This exact knowledge of position of the vehicles is possible thanks to a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that this additional data can be used to dynamically improve the solution.
A state of the art on the most relevant patents in vertical transportation in buildings
(Bentham Science Publishers, 2009) Cortés, Pablo; Guadix Martín, José; Muñuzuri, Jesús; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Junta de Andalucía; Universidad de Sevilla. TEP127: Ingeniería de Organización
Nowadays, the building industry and its associated technologies are experiencing a period of rapid growth, which requires an equivalent growth regarding technologies in the field of vertical transportation. Therefore, the installation of synchronised elevator groups in modern buildings is a common practice in order to govern the dispatching, allocation and movement of the cars shaping the group. So, elevator control and management has become a major field of application for Artificial Intelligence approaches. Methodologies such as fuzzy logic, artificial neural networks, genetic algorithms, ant colonies, or multiagent systems are being successfully proposed in the scientific literature, and are being adopted by the leading elevator companies as elements that differentiate them from their competitors. In this sense, the most relevant companies are adopting strategies based on the protection of their discoveries and inventions as registered patents in different countries throughout the world. This paper presents a comprehensive state of the art of the most relevant recent patents on computer science applied to vertical transportation
A tabu search algorithm for dynamic routing in ATM cell-switching networks
(Elsevier, 2011) Cortés, Pablo; Muñuzuri, Jesús; Fernández Valverde, Joaquín Rodrigo; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. Departamento de Ingeniería Telemática
This paper deals with the dynamic routing problem in ATM cell-switching networks. We present a mathematical programming model based on cell loss and a Tabu Search algorithm with short-term memory that is reinforced with a long-term memory procedure. The estimation of the quality of the solutions is fast, due to the specific encoding of the feasible solutions. The Tabu Search algorithm reaches good quality solutions, outperforming other approaches such as Genetic Algorithms and the Minimum Switching Path heuristic, regarding both cell loss and the CPU time consumption. The best results were found for the more complex networks with a high number of switches and links.
A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings
(2012-11) Cortés, Pablo; Onieva, Luis; Muñuzuri, Jesús; Guadix Martín, José; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II
Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.
A viral system to optimise the daily drayage problem
(Inderscience, 2015) Escudero Santana, Alejandro; Cortés, Pablo; Muñuzuri, Jesús; Aparicio Ruiz, Pablo; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; European Union (UE)
The intermodal transport chain can become more efficient by means of a good organisation of the drayage movements. Drayage in intermodal container terminals involves the pick up or delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. This paper focuses on a new approach to tackle the daily drayage problem by the use of viral system (VS). VS is a novel bio-inspired approach that makes use of a virus-infection biological analogy that is producing very satisfactory results when dealing with complex problems with huge feasibility region.
Agrupamiento borroso paralelo de partes y máquinas para fabricación celular [poster]
(1998) Dobado-Berrios, D.; Lozano Segura, Sebastián; Guerrero López, Fernando; Onieva, Luis; Larrañeta Astola, Juan Carlos; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I
In this work some deficiencies of using Fuzzy c-means (FCM) for grouping parts into part families in cellular manufacturing are discussed and an extension of the algorithm to overcome them is proposed. Toe modified FCM (MFCM) algorithm groups components and machines conjointly and unlike FCM gives rise to a crisp assignment directly.
AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors
(Taylor and Francis, 2022-01) Guadix Martín, José; Lilic, Milica; Rosales Martínez, Marina; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors brings together examples from the "Innovative Ecosystem with Artificial Intelligence for Andalusia 2025" project at the University of Seville, a series of sub-projects composed of research groups and different institutions or companies that explore the use of Artificial Intelligence in a variety of high-impact sectors to lead innovation and assist in decision-making. Key Features Includes chapters on health and social welfare, transportation, digital economy, energy efficiency and sustainability, agro-industry, and tourism Great diversity of authors, expert in varied sectors, belonging to powerful research groups from the University of Seville with proven experience in the transfer of knowledge to the productive sector and agents attached to the Andalucía TECH Campus.

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