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During the steelmaking and continuous casting process in the steel plant, it is common to encounter delays that affect initial planning. Furthermore, continuous casting machines themselves can lose much of their performance in the event of closure of one or more of their casting strands. The situation that is generated, far from being a planning problem, forces consideration of a vision of cost analysis when deciding changes in the planned sequences. This study presents a detailed analysis of the different circumstances that can cause strands closures or sequence breaks, their consequences and the different options available to minimize losses. Finally, an algorithm capable of analyzing the workshop situation and making the most favorable decision to optimize production is proposed, analyzed and compared with the efficiency of the original scheduling method in a real steel plant. The new algorithm proves its efficiency in all situations, with a time-saving average of 26.41 min per decision taken.
David García-Menéndez; Henar Morán-Palacios; Eliseo Vergara-González; Vicente Rodríguez-Montequín. Development of a Steel Plant Rescheduling Algorithm Based on Batch Decisions. Applied Sciences 2021, 11, 6765 .
AMA StyleDavid García-Menéndez, Henar Morán-Palacios, Eliseo Vergara-González, Vicente Rodríguez-Montequín. Development of a Steel Plant Rescheduling Algorithm Based on Batch Decisions. Applied Sciences. 2021; 11 (15):6765.
Chicago/Turabian StyleDavid García-Menéndez; Henar Morán-Palacios; Eliseo Vergara-González; Vicente Rodríguez-Montequín. 2021. "Development of a Steel Plant Rescheduling Algorithm Based on Batch Decisions." Applied Sciences 11, no. 15: 6765.
Increasingly demanding environmental regulations are forcing companies to reduce their impacts caused by their activity while defending the economic viability of their manufacturing processes, especially energy and carbon-intensive ones. Therefore, these challenges must be addressed by posing optimization problems that involve several objectives simultaneously, corresponding to different conditions, and often conflicting between. In this study, the residual gases of an integral steel factory were evaluated and modeled with the goal of developing an optimization problem considering two opposing objectives: CO2 emissions and profit. The problem was first approached in a mono-objective manner, optimizing profit through Mixed Integer Linear Programming (MILP), and then was extended to a bi-objective problem solved by means of the ε-constraint method, to find the Pareto front relating profit and CO2 emissions. The results show that multiobjective optimization is a very valuable resource for plant managers’ decision-making processes. The model makes it possible to identify inflection points from which the level of emissions would increase disproportionately. It gives priority to the consumption of less polluting fuels. The model also makes it possible to make the most of temporary buffers such as the gas holders, adapting to the hourly price of the electricity market. By applying this method, CO2 emissions decrease by more than 3%, and profit amounts up to 14.8% compared to a regular case under normal operating conditions. The sensitivity analysis of the CO2 price and CO2 constraints is also performed.
Sergio García; Vicente Montequín; Marina Piloñeta; Susana Lougedo. Multi-Objective Optimization of Steel Off-Gas in Cogeneration Using the ε-Constraint Method: A Combined Coke Oven and Converter Gas Case Study. Energies 2021, 14, 2741 .
AMA StyleSergio García, Vicente Montequín, Marina Piloñeta, Susana Lougedo. Multi-Objective Optimization of Steel Off-Gas in Cogeneration Using the ε-Constraint Method: A Combined Coke Oven and Converter Gas Case Study. Energies. 2021; 14 (10):2741.
Chicago/Turabian StyleSergio García; Vicente Montequín; Marina Piloñeta; Susana Lougedo. 2021. "Multi-Objective Optimization of Steel Off-Gas in Cogeneration Using the ε-Constraint Method: A Combined Coke Oven and Converter Gas Case Study." Energies 14, no. 10: 2741.
One of the fundamental maintenance tasks of ports is the periodic dredging of them. This is necessary to guarantee a minimum draft that will enable ships to access ports safely. The determination of bathymetries is the instrument that determines the need for dredging and permits an analysis of the behavior of the port bottom over time, in order to achieve adequate water depth. Satellite data processing to predict environmental parameters is used increasingly. Based on satellite data and using different machine learning algorithm techniques, this study has sought to estimate the seabed in ports, taking into account the fact that the port areas are strongly anthropized areas. The algorithms that were used were Support Vector Machine (SVM), Random Forest (RF) and the Multi-Adaptive Regression Splines (MARS). The study was carried out in the ports of Candás and Luarca in the Principality of Asturias. In order to validate the results obtained, data was acquired in situ by using a single beam provided. The results show that this type of methodology can be used to estimate coastal bathymetry. However, when deciding which system was best, priority was given to simplicity and robustness. The results of the SVM and RF algorithms outperform those of the MARS. RF performs better in Candás with a mean absolute error (MAE) of 0.27 cm, whereas SVM performs better in Luarca with a mean absolute error of 0.37 cm. It is suggested that this approach is suitable as a simpler and more cost-effective rough resolution alternative, for estimating the depth of turbid water in ports, than single-beam sonar, which is labor-intensive and polluting.
Vanesa Mateo-Pérez; Marina Corral-Bobadilla; Francisco Ortega-Fernández; Vicente Rodríguez-Montequín. Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms. Energies 2021, 14, 2486 .
AMA StyleVanesa Mateo-Pérez, Marina Corral-Bobadilla, Francisco Ortega-Fernández, Vicente Rodríguez-Montequín. Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms. Energies. 2021; 14 (9):2486.
Chicago/Turabian StyleVanesa Mateo-Pérez; Marina Corral-Bobadilla; Francisco Ortega-Fernández; Vicente Rodríguez-Montequín. 2021. "Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms." Energies 14, no. 9: 2486.
One of the fundamental tasks in the maintenance of port operations is periodic dredging. These dredging operations facilitate the elimination of sediments that the coastal dynamics introduce. Dredging operations are increasingly restrictive and costly due to environmental requirements. Understanding the condition of the seabed before and after dredging is essential. In addition, determining how the seabed has behaved in recent years is important to consider when planning future dredging operations. In order to analyze the behavior of sediment transport and the changes to the seabed due to sedimentation, studies of littoral dynamics are conducted to model the deposition of sediments. Another methodology that could be used to analyze the real behavior of sediments would be to study and compare port bathymetries collected periodically. The problem with this methodology is that it requires numerous bathymetric surveys to produce a sufficiently significant analysis. This study provides an effective solution for obtaining a dense time series of bathymetry mapping using satellite data, and enables the past behavior of the seabed to be examined. The methodology proposed in this work uses Sentinel-2A (10 m resolution) satellite images to obtain historical bathymetric series by the development of a random forest algorithm. From these historical bathymetric series, it is possible to determine how the seabed has behaved and how the entry of sediments into the study area occurs. This methodology is applied in the Port of Luarca (Principality of Asturias), obtaining satellite images and extracting successive bathymetry mapping utilizing the random forest algorithm. This work reveals how once the dock was dredged, the sediments were redeposited and the seabed recovered its level prior to dredging in less than 2 months.
Vanesa Mateo-Pérez; Marina Corral-Bobadilla; Francisco Ortega-Fernández; Vicente Rodríguez-Montequín. Analysis of the Spatio-Temporal Evolution of Dredging from Satellite Images: A Case Study in the Principality of Asturias (Spain). Journal of Marine Science and Engineering 2021, 9, 267 .
AMA StyleVanesa Mateo-Pérez, Marina Corral-Bobadilla, Francisco Ortega-Fernández, Vicente Rodríguez-Montequín. Analysis of the Spatio-Temporal Evolution of Dredging from Satellite Images: A Case Study in the Principality of Asturias (Spain). Journal of Marine Science and Engineering. 2021; 9 (3):267.
Chicago/Turabian StyleVanesa Mateo-Pérez; Marina Corral-Bobadilla; Francisco Ortega-Fernández; Vicente Rodríguez-Montequín. 2021. "Analysis of the Spatio-Temporal Evolution of Dredging from Satellite Images: A Case Study in the Principality of Asturias (Spain)." Journal of Marine Science and Engineering 9, no. 3: 267.
Many organizations are currently face significant challenges in terms of sustainability and technological development. Achieving sustainability in business activities, interweaving social, economic, and environmental perspectives, is one of the most challenging goals for companies. On the other hand, as technology advances exponentially, organizations grow in a linear way. This fact causes a gap which increases over the time. Models and tools have been developed to try to solve both problems separately; on one side to make the organization grow exponentially, and on the other side to incorporate sustainability into the business model. However, they do not allow enough time to know if the actions carried out really achieve their aim. The model presented provides a solution to both problems by monitoring the evolution of organizations towards an exponential structure through the analysis of the project portfolio. The main objective is to know how the orientation of ongoing projects has changed during the last period, in order to position them in terms of achieving the desired sustainability-oriented transformation. With the model designed, it is possible to know if the actions developed by the company are really heading towards a sustainable model and exponential growth. With the aim of validating the model, it has been applied in an innovation organization. With this model, the level of exponential progress of the organization was determined, as well as the goals that have been attained best and worst so far.
Marina Díaz-Piloneta; Francisco Ortega-Fernández; Henar Morán-Palacios; Vicente Rodríguez-Montequín. Monitoring the Implementation of Exponential Organizations through the Assessment of Their Project Portfolio: Case Study. Sustainability 2021, 13, 464 .
AMA StyleMarina Díaz-Piloneta, Francisco Ortega-Fernández, Henar Morán-Palacios, Vicente Rodríguez-Montequín. Monitoring the Implementation of Exponential Organizations through the Assessment of Their Project Portfolio: Case Study. Sustainability. 2021; 13 (2):464.
Chicago/Turabian StyleMarina Díaz-Piloneta; Francisco Ortega-Fernández; Henar Morán-Palacios; Vicente Rodríguez-Montequín. 2021. "Monitoring the Implementation of Exponential Organizations through the Assessment of Their Project Portfolio: Case Study." Sustainability 13, no. 2: 464.
Corrosion is the main mechanism of the degradation of steel structures buried in the soil. Due to its aggressiveness, the material gradually loses thickness until the structure fails, which may cause serious environmental problems. The lack of a clearly established method in the design leads to the need for conservative excess thicknesses to ensure their useful life. This implies inefficient use of steel and an increase in the cost of the structure. In this paper, four quantitative and multivariate models were created to predict the loss of buried steel as a function of time. We developed a basic model, as well as a physical and an electrochemical one, based on multivariate adaptive regression spline (MARS), and a simpler model for comparative purposes based on clusters with Euclidean distance. The modeling was synthesized in a computer tool where the inputs were the characteristics of the soil and the time and the outputs were the loss of thickness of each predictive model and the description of the most similar real tests. The results showed that in all models, for relative errors of 10%, over 90% of predictions were correct. In addition, a real example of the operation of the tool was defined, where it was found that the estimates of the models allow the necessary optimization of steel to fulfill its useful life.
Lorena-De Arriba-Rodríguez; Vicente Rodríguez-Montequín; Joaquín Villanueva-Balsera; Francisco Ortega-Fernández. Design of Predictive Models to Estimate Corrosion in Buried Steel Structures. Sustainability 2020, 12, 9879 .
AMA StyleLorena-De Arriba-Rodríguez, Vicente Rodríguez-Montequín, Joaquín Villanueva-Balsera, Francisco Ortega-Fernández. Design of Predictive Models to Estimate Corrosion in Buried Steel Structures. Sustainability. 2020; 12 (23):9879.
Chicago/Turabian StyleLorena-De Arriba-Rodríguez; Vicente Rodríguez-Montequín; Joaquín Villanueva-Balsera; Francisco Ortega-Fernández. 2020. "Design of Predictive Models to Estimate Corrosion in Buried Steel Structures." Sustainability 12, no. 23: 9879.
Recommending the identity of bidders in public procurement auctions (tenders) has a significant impact in many areas of public procurement, but it has not yet been studied in depth. A bidders recommender would be a very beneficial tool because a supplier (company) can search appropriate tenders and, vice versa, a public procurement agency can discover automatically unknown companies which are suitable for its tender. This paper develops a pioneering algorithm to recommend potential bidders using a machine learning method, particularly a random forest classifier. The bidders recommender is described theoretically, so it can be implemented or adapted to any particular situation. It has been successfully validated with a case study: an actual Spanish tender dataset (free public information) which has 102,087 tenders from 2014 to 2020 and a company dataset (nonfree public information) which has 1,353,213 Spanish companies. Quantitative, graphical, and statistical descriptions of both datasets are presented. The results of the case study were satisfactory: the winning bidding company is within the recommended companies group, from 24% to 38% of the tenders, according to different test conditions and scenarios.
Manuel J. García Rodríguez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín M. Villanueva Balsera. Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain. Complexity 2020, 2020, 1 -20.
AMA StyleManuel J. García Rodríguez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín M. Villanueva Balsera. Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain. Complexity. 2020; 2020 ():1-20.
Chicago/Turabian StyleManuel J. García Rodríguez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín M. Villanueva Balsera. 2020. "Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain." Complexity 2020, no. : 1-20.
This communication describes the developed web system by which companies of the industrial and service sectors can access their energy consumption data (electricity, gas, and water) and perform energy efficiency analysis. Data is collected using measuring devices installed in the clients’ premises and transmitted to the analysis platform. Each client of this service, after being authenticated, can access and consult his historical consumption data, displayed in the form of graphs and personalized reports. The user can obtain consumption performance patterns through advanced data analysis.
V. Rodríguez; J. García; H. Morán; B. Martínez. The Use of the Cloud Platform to Register and Perform Intelligent Analysis of Energy Consumption Parameters in the Service and Industrial Sectors. Lecture Notes in Management and Industrial Engineering 2020, 505 -515.
AMA StyleV. Rodríguez, J. García, H. Morán, B. Martínez. The Use of the Cloud Platform to Register and Perform Intelligent Analysis of Energy Consumption Parameters in the Service and Industrial Sectors. Lecture Notes in Management and Industrial Engineering. 2020; ():505-515.
Chicago/Turabian StyleV. Rodríguez; J. García; H. Morán; B. Martínez. 2020. "The Use of the Cloud Platform to Register and Perform Intelligent Analysis of Energy Consumption Parameters in the Service and Industrial Sectors." Lecture Notes in Management and Industrial Engineering , no. : 505-515.
Off-gas is one of the by-products of the steelmaking process. Its potential energy can be transformed into heat and electricity by means of cogeneration. A case study using a coke oven and Linz–Donawitz converter gas is presented. This work addresses the gas allocation problem for a cogeneration system producing steam and electricity. In the studied facility, located in northern Spain, the annual production of the plant requires 95,000 MWh of electrical energy and 525,000 MWh of thermal energy. The installed electrical and thermal power is 20.4 MW and 81 MW, respectively. A mixed integer linear programming model is built to optimize gas allocation, thus maximizing its benefits. This model is applied to a 24-h scenario with real data from the plant, where gas allocation decision-making was performed by the plant operators. Application of the model generated profit in a scenario where there were losses, increasing benefits by 16.9%. A sensitivity analysis is also performed. The proposed model is useful not only from the perspective of daily plant operation but also as a tool to simulate different design scenarios, such as the capacity of gasholders.
Sergio García García; Vicente Rodríguez Montequín; Henar Morán Palacios; Adriano Mones Bayo. A Mixed Integer Linear Programming Model for the Optimization of Steel Waste Gases in Cogeneration: A Combined Coke Oven and Converter Gas Case Study. Energies 2020, 13, 3781 .
AMA StyleSergio García García, Vicente Rodríguez Montequín, Henar Morán Palacios, Adriano Mones Bayo. A Mixed Integer Linear Programming Model for the Optimization of Steel Waste Gases in Cogeneration: A Combined Coke Oven and Converter Gas Case Study. Energies. 2020; 13 (15):3781.
Chicago/Turabian StyleSergio García García; Vicente Rodríguez Montequín; Henar Morán Palacios; Adriano Mones Bayo. 2020. "A Mixed Integer Linear Programming Model for the Optimization of Steel Waste Gases in Cogeneration: A Combined Coke Oven and Converter Gas Case Study." Energies 13, no. 15: 3781.
The prioritization of factors has been widely studied applying different methods from the domain of the multiple-criteria decision-making, such as for example the Analytic Hierarchy Process method (AHP) based on decision-makers’ pairwise comparisons. Most of these methods are subjected to a complex analysis. The Bradley-Terry model is a probability model for paired evaluations. Although this model is usually known for its application to calculating probabilities, it can be also extended for ranking factors based on pairwise comparison. This application is much less used; however, this work shows that it can provide advantages, such as greater simplicity than traditional multiple-criteria decision methods in some contexts. This work presents a method for ranking the perspectives and indicators of a balance scorecard when the opinion of several decision-makers needs to be combined. The data come from an elicitation process, accounting for the number of times a factor is preferred to others by the decision-makers in a pairwise comparisons. No preference scale is used; the process just indicates the winner of the comparison. Then, the priority weights are derived from the Bradley-Terry model. The method is applied in a Financial Software Factory for demonstration and validation. The results are compared against the application of the AHP method for the same data, concluding that despite the simplifications made with the new approach, the results are very similar. The study contributes to the multiple-criteria decision-making domain by building an integrated framework, which can be used as a tool for scorecard prioritization.
Vicente Rodríguez Montequín; Joaquín Manuel Villanueva Villanueva Balsera; Marina Díaz Piloñeta; César Álvarez Pérez. A Bradley-Terry Model-Based Approach to Prioritize the Balance Scorecard Driving Factors: The Case Study of a Financial Software Factory. Mathematics 2020, 8, 276 .
AMA StyleVicente Rodríguez Montequín, Joaquín Manuel Villanueva Villanueva Balsera, Marina Díaz Piloñeta, César Álvarez Pérez. A Bradley-Terry Model-Based Approach to Prioritize the Balance Scorecard Driving Factors: The Case Study of a Financial Software Factory. Mathematics. 2020; 8 (2):276.
Chicago/Turabian StyleVicente Rodríguez Montequín; Joaquín Manuel Villanueva Villanueva Balsera; Marina Díaz Piloñeta; César Álvarez Pérez. 2020. "A Bradley-Terry Model-Based Approach to Prioritize the Balance Scorecard Driving Factors: The Case Study of a Financial Software Factory." Mathematics 8, no. 2: 276.
Crowdfunding is a response to the financing problem of innovative projects in an environment of severe economic crisis. Its competitive advantage lies in its independence from banking institutions and the distribution of risk among a certain number of funders. Since its inception, the number of successfully completed projects has grown to a point where it has started to suffer a downturn that puts its sustainability at risk. This study concerns this particular period of downturn, in order to identify attributes that characterize it, and to define behavioral stereotypes that may be associated with new projects. On a wide data set from sufficiently contrasted projects, and through the use data mining techniques, we extracted the most influential factors in determining the success or failure of the projects, that will subsequently be grouped together using clustering techniques. Six groups of projects have been identified, each with their own characteristics that define them, two of them clearly guide the projects to success and another one allows the modification its characteristics to move away from failure. This achieved strategy allows us to estimate which potential group would be the result of a new project.
Aladino Fernandez-Blanco; Joaquin Villanueva-Balsera; Vicente Rodriguez-Montequin; Henar Moran-Palacios. Key Factors for Project Crowdfunding Success: An Empirical Study. Sustainability 2020, 12, 599 .
AMA StyleAladino Fernandez-Blanco, Joaquin Villanueva-Balsera, Vicente Rodriguez-Montequin, Henar Moran-Palacios. Key Factors for Project Crowdfunding Success: An Empirical Study. Sustainability. 2020; 12 (2):599.
Chicago/Turabian StyleAladino Fernandez-Blanco; Joaquin Villanueva-Balsera; Vicente Rodriguez-Montequin; Henar Moran-Palacios. 2020. "Key Factors for Project Crowdfunding Success: An Empirical Study." Sustainability 12, no. 2: 599.
Smart Manufacturing is a goal to be achieved, and the most advanced manufacturing approaches are being used to pursue this objective. Within this context, industry development aims to attain an intelligent manufacturing using, for example, virtual models that simulate production lines. This paper presents the architecture of a Digital Twin for emulating the rolls replacement process within a wire rod rolling mill. The model is developed in Python, using a backtracking algorithm to select the suitable set of rolls as a first basic approach for the validation of the system. It may be used in the future to improve the production system automating the decision for the replacement of rolls as alternative to the current human-decision process.
Ana María Valdeón Junquera; Javier García González; Joaquín Manuel Villanueva Balsera; Vicente Rodríguez Montequín. A Wire Rod Rolling Mill Digital Twin for the Simulation of the Rolls Replacement Process. Proceedings 2020, 63, 13 .
AMA StyleAna María Valdeón Junquera, Javier García González, Joaquín Manuel Villanueva Balsera, Vicente Rodríguez Montequín. A Wire Rod Rolling Mill Digital Twin for the Simulation of the Rolls Replacement Process. Proceedings. 2020; 63 (1):13.
Chicago/Turabian StyleAna María Valdeón Junquera; Javier García González; Joaquín Manuel Villanueva Balsera; Vicente Rodríguez Montequín. 2020. "A Wire Rod Rolling Mill Digital Twin for the Simulation of the Rolls Replacement Process." Proceedings 63, no. 1: 13.
The largest project managers and adjudicators of a country, both by number of projects and by cost, are public procurement agencies. Therefore, knowing and characterising public procurement announcements (tenders) is fundamental for managing public resources well. This article presents the case of public procurement in Spain, analysing a dataset from 2012 to 2018: 58,337 tenders with a cost of 31,426 million euros. Many studies of public procurement have been conducted globally or theoretically, but there is a dearth of data analysis, especially regarding Spain. A quantitative, graphical, and statistical description of the dataset is presented. Mainly, the analysis is of the relation between the award price and the bidding price. An award price estimator is proposed that uses the random forest regression method. A good estimator would be very useful and valuable for companies and public procurement agencies. It would be a key tool in their project management decision making. Finally, a similar analysis, employing a dataset from European countries, is presented to compare and generalise the results and conclusions. Hence, this is a novel study which fills a gap in the literature.
Manuel J. García Rodríguez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín M. Villanueva Balsera. Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning. Complexity 2019, 2019, 1 -20.
AMA StyleManuel J. García Rodríguez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín M. Villanueva Balsera. Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning. Complexity. 2019; 2019 ():1-20.
Chicago/Turabian StyleManuel J. García Rodríguez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín M. Villanueva Balsera. 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning." Complexity 2019, no. : 1-20.
The following paper proposes a study about the existing solutions for dealing with uncertainty while solving the planning and scheduling problem at steel industry manufacturing processes. The different techniques designed to cope with uncertainty in manufacturing scheduling are discussed, along with the main uncertainty factors affecting the scheduling. The paper proposes a classification for the main uncertainties affecting the steelmaking process and analyzes the existing literature about solutions for the scheduling with uncertainty in the steel sector in terms of approaches followed and uncertainty types considered. Finally, the main remarks and future challenges within this field are presented.
Miguel Iglesias-Escudero; Joaquín Villanueva-Balsera; Francisco Ortega-Fernandez; Vicente Rodriguez-Montequín. Planning and Scheduling with Uncertainty in the Steel Sector: A Review. Applied Sciences 2019, 9, 2692 .
AMA StyleMiguel Iglesias-Escudero, Joaquín Villanueva-Balsera, Francisco Ortega-Fernandez, Vicente Rodriguez-Montequín. Planning and Scheduling with Uncertainty in the Steel Sector: A Review. Applied Sciences. 2019; 9 (13):2692.
Chicago/Turabian StyleMiguel Iglesias-Escudero; Joaquín Villanueva-Balsera; Francisco Ortega-Fernandez; Vicente Rodriguez-Montequín. 2019. "Planning and Scheduling with Uncertainty in the Steel Sector: A Review." Applied Sciences 9, no. 13: 2692.
Steel making processes dispose large volumes of waste gases whose potential energy can be transformed into heat and electricity by means of cogeneration. A case study using coke oven and Linz-Donawitz converter gas is presented here. The data is obtained from an existing plant located in Northern Spain. The engines are adapted for its operation with converter gas, and steam is generated in boilers that consume coke gas, converter gas and natural gas in the absence of waste gases. The actual influence on the environmental behaviour of the process is analysed considering the benefits but also the drawbacks derived from the gases low calorific value, toxicity and polluting. The work constitutes the first study in an installation with these characteristics and burning this combination of gases. The functional unit is represented by 1 MWh of thermal energy. The analysis has been developed mainly on the bases of the following sources: site-specific measured or calculated data directly from the process of cogeneration, life-cycle inventory databases and bibliographical information. Operating parameters, as well as production data (thermal and electric energy), emissions of NOX, SO2 and CO2, discharges and wastes associated with this process are exposed. The system boundaries were considered gate to gate, so the results are useful for the integration with other global scenarios. Midpoint and endpoint characterisation factors for humans, ecosystems and resources are given. The main effects are related to Climate change, Ionising radiation, Human toxicity and Fossil and Ozone depletion. The results indicate that the usage of these gases implies an environmental benefit. The operational data belonging to 2014 shows a reduction of more than 100 points in the global impact of the functional unit. In the best scenario, 169.42 Nm3/MWh of natural gas may be saved with the consequent reduction in natural resources and ozone depletion.
Sergio García García; Vicente Rodríguez Montequín; Rocío Luiña Fernández; Francisco Ortega Fernández. Evaluation of the synergies in cogeneration with steel waste gases based on Life Cycle Assessment: A combined coke oven and steelmaking gas case study. Journal of Cleaner Production 2019, 217, 576 -583.
AMA StyleSergio García García, Vicente Rodríguez Montequín, Rocío Luiña Fernández, Francisco Ortega Fernández. Evaluation of the synergies in cogeneration with steel waste gases based on Life Cycle Assessment: A combined coke oven and steelmaking gas case study. Journal of Cleaner Production. 2019; 217 ():576-583.
Chicago/Turabian StyleSergio García García; Vicente Rodríguez Montequín; Rocío Luiña Fernández; Francisco Ortega Fernández. 2019. "Evaluation of the synergies in cogeneration with steel waste gases based on Life Cycle Assessment: A combined coke oven and steelmaking gas case study." Journal of Cleaner Production 217, no. : 576-583.
Open Data in Public Administrations and, in particular, the publications of public procurement (tenders) is a source of valuable information for the decision-making procedure. The analysis of public tenders can provide valuable information for the different stakeholders: politicians, public managers, project managers, executives and, indirectly, citizens. Open Data allows the application of Business Intelligent and massive data processing techniques. This study presents the current situation of the Spanish Public Procurement processes and its open data sources available for citizens. The focus of the study is the Request-For-Proposal (RFP) and tender submission related data. The European and Spanish legislation which applies to this topic is collected. The Spanish Public Sector Contracting Platform, which is the web platform where public procurement announcements and their resolutions are published, is explained. The information can be very useful for researchers who want to carry out studies applying massive data processing techniques. A use case is presented using the open data of that web platform with different approaches to demonstrate its usefulness in a Business Intelligent context. Examples describing quantitative, geographic, sectorial competitiveness and interregional mobility analysis are presented illustrating possible applications.
Manuel J. García Rodríguez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. Spanish Public Procurement: legislation, open data source and extracting valuable information of procurement announcements. Procedia Computer Science 2019, 164, 441 -448.
AMA StyleManuel J. García Rodríguez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín Villanueva Balsera. Spanish Public Procurement: legislation, open data source and extracting valuable information of procurement announcements. Procedia Computer Science. 2019; 164 ():441-448.
Chicago/Turabian StyleManuel J. García Rodríguez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 2019. "Spanish Public Procurement: legislation, open data source and extracting valuable information of procurement announcements." Procedia Computer Science 164, no. : 441-448.
In the field of project management, complexity is closely related to project outcomes and hence project success and failure factors. Subjectivity is inherent to these concepts, which are also influenced by sectorial, cultural, and geographical differences. While theoretical frameworks to identify organizational complexity factors do exist, a thorough and multidimensional account of organizational complexity must take into account the behavior and interrelatedness of these factors. Our study is focused on analyzing the combinations of failure factors by means of self-organizing maps (SOM) and clustering techniques, thus getting different patterns about the project managers perception on influencing project failure causes and hence project complexity. The analysis is based on a survey conducted among project manager practitioners from all over the world to gather information on the degree of influence of different factors on the projects failure causes. The study is cross-sectorial. Behavioral patterns were found, concluding that in the sampled population there are five clearly differentiated groups (clusters) and at least three clear patterns of answers. The prevalent order of influence is project factors, organization related factors, project manager and team members factors, and external factors.
Vicente Rodríguez Montequín; Joaquín Villanueva Balsera; Sonia María Cousillas Fernández; Francisco Ortega Fernández. Exploring Project Complexity through Project Failure Factors: Analysis of Cluster Patterns Using Self-Organizing Maps. Complexity 2018, 2018, 1 -17.
AMA StyleVicente Rodríguez Montequín, Joaquín Villanueva Balsera, Sonia María Cousillas Fernández, Francisco Ortega Fernández. Exploring Project Complexity through Project Failure Factors: Analysis of Cluster Patterns Using Self-Organizing Maps. Complexity. 2018; 2018 ():1-17.
Chicago/Turabian StyleVicente Rodríguez Montequín; Joaquín Villanueva Balsera; Sonia María Cousillas Fernández; Francisco Ortega Fernández. 2018. "Exploring Project Complexity through Project Failure Factors: Analysis of Cluster Patterns Using Self-Organizing Maps." Complexity 2018, no. : 1-17.
This paper presents a case study of how a Spanish financial software factory (FSF) has determined the weights of the indicators and objectives included in their strategy map with the aim of ensuring its business sustainability. A strategy map is a graphical representation of the cause-effect relationships between strategic objectives and indicators of a balanced scorecard (BSC). The detailed description of the strategy map development and deployment is not part of the aim of this work as it was described in a former paper. In this study, FAHP, a multicriteria decision-making (MCDM) method using the concepts of fuzzy set theory and hierarchical structure analysis, was used to calculate the weights. The analysis was carried out considering the points of view of different groups of stakeholders (shareholders, top management, middle managers, other employees, customers and some experts in the field of software factories) and the results are presented grouped by role to get a better understanding of the preferences of each kind of stakeholder. The conclusions of this study give a better insight of the corporative sustainability strategies of this kind of firms as well as the different vision of each stakeholder, what could be very valuable to the software factory managers for the decision-making and the strategic management of their organizations.
César Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. Integration of Balanced Scorecard (BSC), Strategy Map, and Fuzzy Analytic Hierarchy Process (FAHP) for a Sustainability Business Framework: A Case Study of a Spanish Software Factory in the Financial Sector. Sustainability 2017, 9, 527 .
AMA StyleCésar Álvarez Pérez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín Villanueva Balsera. Integration of Balanced Scorecard (BSC), Strategy Map, and Fuzzy Analytic Hierarchy Process (FAHP) for a Sustainability Business Framework: A Case Study of a Spanish Software Factory in the Financial Sector. Sustainability. 2017; 9 (4):527.
Chicago/Turabian StyleCésar Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 2017. "Integration of Balanced Scorecard (BSC), Strategy Map, and Fuzzy Analytic Hierarchy Process (FAHP) for a Sustainability Business Framework: A Case Study of a Spanish Software Factory in the Financial Sector." Sustainability 9, no. 4: 527.
A balanced scorecard (BSC) framework for a factory that develops software for banking was proposed by us at the end of 2015 to ensure its sustainability, and was focused on improving its productivity and cost. Based on this framework, the aim of this study is to construct an approach using the analytic hierarchy process (AHP) and BSC for evaluating a factory’s performance in order for it to become a sustainable business. In this study, AHP is proposed to prioritise and determine weights for the perspectives and indicators included in the BSC for a financial software factory (FSF). The combination of these weights with different indicator measures produces a model that provides an effective assessment tool for FSF managers. The results of the study, which are shown both globally and disaggregated according to the different roles of FSF stakeholders, show that user satisfaction is the main pillar for making decisions. In addition, the result considering roles shows differences according to the relationship of each stakeholder with the software factory. The current study has been validated in a Spanish factory that develops software for several financial entities.
César Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. Integrating Analytic Hierarchy Process (AHP) and Balanced Scorecard (BSC) Framework for Sustainable Business in a Software Factory in the Financial Sector. Sustainability 2017, 9, 486 .
AMA StyleCésar Álvarez Pérez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín Villanueva Balsera. Integrating Analytic Hierarchy Process (AHP) and Balanced Scorecard (BSC) Framework for Sustainable Business in a Software Factory in the Financial Sector. Sustainability. 2017; 9 (4):486.
Chicago/Turabian StyleCésar Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 2017. "Integrating Analytic Hierarchy Process (AHP) and Balanced Scorecard (BSC) Framework for Sustainable Business in a Software Factory in the Financial Sector." Sustainability 9, no. 4: 486.
V.R. Montequin; S.M. Cousillas; V. Alvarez; J. Villanueva. Success Factors and Failure Causes in Projects: Analysis of Cluster Patterns Using Self-organizing Maps. Procedia Computer Science 2016, 100, 440 -448.
AMA StyleV.R. Montequin, S.M. Cousillas, V. Alvarez, J. Villanueva. Success Factors and Failure Causes in Projects: Analysis of Cluster Patterns Using Self-organizing Maps. Procedia Computer Science. 2016; 100 ():440-448.
Chicago/Turabian StyleV.R. Montequin; S.M. Cousillas; V. Alvarez; J. Villanueva. 2016. "Success Factors and Failure Causes in Projects: Analysis of Cluster Patterns Using Self-organizing Maps." Procedia Computer Science 100, no. : 440-448.