Solving Scheduling Problems with Genetic Algorithms using a Priority Encoding Scheme

Scheduling problems are very hard computational tasks with several applications in multitude of domains. In this work we solve a practical problem motivated by a real industry situation, in which we apply a genetic algorithm for finding an acceptable solution in a very short time interval. The main novelty introduced in this work is the use of a priority based chromosome codification that determines the precedence of a task with respect to other ones, permitting to introduce in a very simple way all problem constraints, including setup costs and workforce availability. Results show the suitability of the approach, obtaining real time solutions for tasks with up to 50 products.Universidad de Málaga.Campus de Excelencia Internacional Andalucía Tech

Una comparativa entre redes neuronales artificiales y métodos clásicos para la predicción de la movilidad entre zonas de transporte. Aplicación práctica en el Campo de Gibraltar, España

Los problemas de tráfico son cada vez más frecuentes debido al gran desarrollo tecnológico de la humanidad siendo, además, esencial su control para optimizar la infraestructura y el transporte público. Para lograr este objetivo, es necesario hacer una estimación de la demanda de los viajeros. Un método alternativo basado en redes neuronales artificiales (RNAs) se analiza en este trabajo, en comparación con las técnicas de predicción tradicionales. El objetivo es obtener un procedimiento de estimación usando variables de entrada sencillas y económicas, que son fáciles de encontrar. A diferencia de los modelos tradicionales, el modelo alternativo funciona mejor con los datos de entrada utilizados, ajustando mejor los resultados esperados. Los resultados son altamente prometedores y por tanto se demuestra la capacidad de las redes neuronales artificiales para realizar una estimación de la movilidad entre zonas.Traffic issues are more common every day due to the great technological development of humanity. Therefore, the control is essential to optimize infrastructure and public transport. To achieve this goal, it is necessary to make an estimate of the demand of the mobility. An alternative method, based on Artificial Neural Networks (ANNs), has been analyzed in this work comparing to traditional prediction techniques. The aim is to obtain an estimation procedure using simple, economical input variables which are easy to find. Unlike traditional models. These new models are able to perform a better fitting of input-output mapping. The results are encouraging and therefore the ability of ANNs is shown to estimate mobility between zones

Evaluation of Paris MoU Maritime Inspections Using a STATIS Approach

Port state control inspections implemented under the Paris Memorandum of Understanding (MoU) have become known as one of the best instruments for maritime administrations in European Union (EU) Member States to ensure that the ships docked in their ports comply with all maritime safety requirements. This paper focuses on the analysis of all inspections made between 2013 and 2018 in the top ten EU ports incorporated in the Paris MoU (17,880 inspections). The methodology consists of a multivariate statistical information system (STATIS) analysis using the inspected ship's characteristics as explanatory variables. The variables used describe both the inspected ships (classification society, flag, age and gross tonnage) and the inspection (type of inspection and number of deficiencies), yielding a dataset with more than 600,000 elements in the data matrix. The most important results are that the classifications obtained match the performance lists published annually by the Paris MoU and the classification societies. Therefore, the approach is a potentially valid classification method and would then be useful to maritime authorities as an additional indicator of a ship's risk profile to decide inspection priorities and as a tool to measure the evolution in the risk profile of the flag over time.This research was funded by University of Cadiz

A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities

An accurate prediction of freight volume at the sanitary facilities of seaports is a key factor to improve planning operations and resource allocation. This study proposes a hybrid approach to forecast container volume at the sanitary facilities of a seaport. The methodology consists of a three-step procedure, combining the strengths of linear and non-linear models and the capability of a clustering technique. First, a self-organizing map (SOM) is used to decompose the time series into smaller clusters easier to predict. Second, a seasonal autoregressive integrated moving averages (SARIMA) model is applied in each cluster in order to obtain predicted values and residuals of each cluster. These values are finally used as inputs of a support vector regression (SVR) model together with the historical data of the cluster. The final prediction result integrates the prediction results of each cluster. The experimental results showed that the proposed model provided accurate prediction results and outperforms the rest of the models tested. The proposed model can be used as an automatic decision-making tool by seaport management due to its capacity to plan resources in advance, avoiding congestion and time delays

Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model's performance, especially for t + 4 (rho approximate to 0.68 to rho approximate to 0.74) and t + 8 (rho approximate to 0.59 to rho approximate to 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases

Air pollution relevance analysis in the bay of Algeciras (Spain)

The aim of this work is to accomplish an in-depth analysis of the air pollution in the two main cities of the Bay of Algeciras (Spain). A large database of air pollutant concentrations and weather measurements were collected using a monitoring network installed throughout the region from the period of 2010-2015. The concentration parameters contain nitrogen dioxide (NO2), sulphur dioxide (SO2) and particulate matter (PM10). The analysis was developed in two monitoring stations (Algeciras and La Linea). The higher average concentration values were obtained in Algeciras for NO2 (28.850 mu g/m(3)) and SO2 (11.966 mu g/m(3)), and in La Linea for PM10 (30.745 mu g/m(3)). The analysis shows patterns that coincide with human activity. One of the goals of this work is to develop a useful virtual sensor capable of achieving a more robust monitoring network, which can be used, for instance, in the case of missing data. By means of trends analysis, groups of equivalent stations were determined, implying that the values of one station could be substituted for those in the equivalent station in case of failure (e.g., SO2 weekly trends in Algeciras and Los Barrios show equivalence). On the other hand, a calculation of relative risks was developed showing that relative humidity, wind speed and wind direction produce an increase in the risk of higher pollutant concentrations. Besides, obtained results showed that wind speed and wind direction are the most important variables in the distribution of particles. The results obtained may allow administrations or citizens to support decisions

Ejercicios resueltos de programación en C.

Ayudar a la repoblación forestal, contribuir a la explosión demográfica, y dejar algún legajo, son algunas de las obligaciones que todo ser civilizado tiene para con este mundo. Sobre el primer deber, estoy seguro que los autores algo habrán plantado en algún sitio, aunque sea un jaramago. En relación con la segunda, lo del hijo, es sabido de uno de ellos, Pepe Galindo. De Pedro, por lo menos no consta. Algunos comparan la segunda de las obligaciones con la tercera. Hablan de un libro como si fuera un hijo del autor o autores. Si comparamos el libro, por ejemplo, con Patricia, la hija de Pepe, (con la necesaria e inestimable colaboración de su esposa), pierde el libro seguro. Un ángel rubio, mucho más importante que cualquier conjunto de reglas y símbolos que conforman un libro de un lenguaje de programación. Si alguien piensa que el C es más importante, es que no la conoce.211 págs

Time analysis of the containerized cargo flow in the logistic chain using simulation tools: the case of the Port of Seville (Spain)

[EN] The logistic chain that connects the capital of Spain (Madrid) with the Canary Islands has the Port of Seville as the port node. This port node makes possible to switch from one transport mode (railway) to another (maritime) at the container terminal of the port. Some constraints, such as the operational time window that restricts the freight train access into the port in a certain time-slot or the need of the reversal of the train before entering into port, lead to generate important time delays in the intermodal chain. A time analysis of the process is necessary in order to check the critical points. A simulation of the whole process from the goods departing the origin station by train until they leave the port of Seville by ship to the Canary Islands is performed. To this aim, a queuing model network was developed in order to simulate the travel time of the cargo. The database is composed of daily departures of goods train and daily departures of vessels (including times of docking, berthing or load/unload cargo). The final objective of this work is twofold: firstly, to provide a validated model of the containerized cargo flow and secondly, to demonstrate that this kind of queuing models can become a powerful supporting tool in making decisions about future investments.Ruiz Aguilar, J.; Turias, I.; Cerbán, M.; Jiménez Come, MJ.; González, M.; Pulido, Á. (2016). Time analysis of the containerized cargo flow in the logistic chain using simulation tools: the case of the Port of Seville (Spain). En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 1509-1517. https://doi.org/10.4995/CIT2016.2015.3083OCS1509151

Prediction of container filling for the selective waste collection in Algeciras (Spain)

14th Conference on Transport Engineering: 6th – 8th July 2021The aim of this study is to create an intelligent system that improves the efficiency of garbage collection, (cardboard waste, in this particular case). The number of cardboard containers to be collected each day will be determined based on a prediction made on the filled volume recorded in each container. It will be reflected in the cost and fuel savings, reducing emissions and contributing to environmental sustainability. These results will allow planning the sequence of waste removal, which means the optimal collection route considering restrictive parameters such as the type of truck, the location of containers, collection times by zones, and the availability of working staff. A filling prediction system is proposed based on real historical data provided by the current waste collection company in Algeciras (ARCGISA). To achieve this objective, an intelligent system is designed using predictive analytics and several methods based on machine learning, modelling the collection system as a classification model, comparing the results from a statistical point of view (using sensitivity, specificity, etc.). The results obtained with the best-Tested method indicate an improvement average rate of 26% in sensitivity performance index and 67% in specificity performance index. Currently, waste collection is carried out without predictive analysis. The relevance of an efficient waste collection system is becoming increasingly important. Achieving optimal waste collection will result in improved service to citizens, cost savings for the administration, and significant environmental improvements. © 2021 Elsevier B.V.. All rights reserve