An evolutive approach for the delineation of local labour markets

This paper presents a new approach to the delineation of local labour markets based on evolutionary computation. The main objective is the regionalisation of a given territory into functional regions based on commuting flows. According to the relevant literature, such regions are defined so that (a) their boundaries are rarely crossed in daily journeys to work, and (b) a high degree of intra-area movement exists. This proposal merges municipalities into functional regions by maximizing a fitness function that measures aggregate intra-region interaction under constraints of inter-region separation and minimum size. Real results are presented based on the latest database from the Census of Population in the Region of Valencia. Comparison between the results obtained through the official method which currently is most widely used (that of British Travel-to-Work Areas) and those from our approach is also presented, showing important improvements in terms of both the number of different market areas identified that meet the statistical criteria and the degree of aggregate intra-market interaction.José M. Casado-Díaz has received financial support from the Spanish Department of Education and Science (ref. BEC2003-02391) through a program partly funded by the European Regional Development Fund (ERDF). Lucas Martínez-Bernabeu acknowledges financial support from the Spanish Dept. of Education and Science, the European Social Fund (ESF) and the University of Alicante

Study of the optimal waveforms for non-destructive spectral analysis of aqueous solutions by means of audible sound and optimization algorithms

Acoustic analysis of materials is a common non-destructive technique, but most efforts are focused on the ultrasonic range. In the audible range, such studies are generally devoted to audio engineering applications. Ultrasonic sound has evident advantages, but also severe limitations, like penetration depth and the use of coupling gels. We propose a biomimetic approach in the audible range to overcome some of these limitations. A total of 364 samples of water and fructose solutions with 28 concentrations between 0 g/L and 9 g/L have been analyzed inside an anechoic chamber using audible sound configurations. The spectral information from the scattered sound is used to identify and discriminate the concentration with the help of an improved grouping genetic algorithm that extracts a set of frequencies as a classifier. The fitness function of the optimization algorithm implements an extreme learning machine. The classifier obtained with this new technique is composed only by nine frequencies in the (3–15) kHz range. The results have been obtained over 20,000 independent random iterations, achieving an average classification accuracy of 98.65% for concentrations with a difference of ±0.01 g/L

The Importance of Scale and the MAUP for Robust Ecosystem Service Evaluations and Landscape Decisions

Spatial data are used in many scientific domains including analyses of Ecosystem Services (ES) and Natural Capital (NC), with results used to inform planning and policy. However, the data spatial scale (or support) has a fundamental impact on analysis outputs and, thus, process understanding and inference. The Modifiable Areal Unit Problem (MAUP) describes the effects of scale on analyses of spatial data and outputs, but it has been ignored in much environmental research, including evaluations of land use with respect to ES and NC. This paper illustrates the MAUP through an ES optimisation problem. The results show that MAUP effects are unpredictable and nonlinear, with discontinuities specific to the spatial properties of the case study. Four key recommendations are as follows: (1) The MAUP should always be tested for in ES evaluations. This is commonly performed in socio-economic analyses. (2) Spatial aggregation scales should be matched to process granularity by identifying the aggregation scale at which processes are considered to be stable (stationary) with respect to variances, covariances, and other moments. (3) Aggregation scales should be evaluated along with the scale of decision making (e.g., agricultural field, farm holding, and catchment). (4) Researchers in ES and related disciplines should up-skill themselves in spatial analysis and core paradigms related to scale to overcome the scale blindness commonly found in much research

Sviluppo di un algoritmo genetico per il bilanciamento multiobiettivo di una linea di montaggio manuale

Nella presente tesi si implementa un algoritmo genetico che ha come obiettivo l'ottimizzazione dei problemi di assemblaggio nelle linee di montaggio manuali. Nella prima parte del lavoro si cerca di definire lo stato dell’arte e gli elementi che costituiscono e influenzano i sistemi di assemblaggio, con particolare riferimento alle linee di produzione manuali. Dopodiché, con il fine di provare ad individuare la miglior soluzione di montaggio, si introduce uno dei tanti strumenti in grado di risolvere i problemi di ottimizzazione: l’algoritmo genetico. Questo ha ricevuto negli ultimi anni molta attenzione da parte dei ricercatori, in quanto considerato metodo in grado di trovare soluzioni ottime in tempi ragionevolmente brevi. Si passa perciò alla parte centrale del lavoro nella quale si implementa l'algoritmo tenendo conto di obiettivi che, sulla base di valide giustificazioni, si ritengono fondamentali per una gestione più completa possibile di ogni tipo di risorsa necessaria alla fase di assemblaggio. Si applica quindi tale algoritmo a due casi di studio e se ne variano i parametri per giudicarne il comportamento. L’ultima parte della tesi discute i risultati raggiunti sulla base degli obiettivi proposti e trae le conclusioni. L’implementazione dello strumento è stata realizzata in ambiente MATLAB, programmando prima l’esecuzione dell’intero algoritmo, e poi una serie di interfacce grafiche che facilitano in input l’inserimento di dati sia del prodotto che dell’algoritmo, e in output la visualizzazione dei risultati ottenuti

ORBIT PROPAGATION AND DETERMINATION ALGORITHMS FOR SATELLITE GROUND STATIONS

The satellite orbital parameters are essential for satellite operations. With these parameters, it is possible to estimate the satellite position in the recent past and near future, which is essential to effectively plan satellite operations and associate satellite telemetry with geographical locations.However, for small or medium satellite operators who do not possess the infrastructure required to track their satellites, the problem of determining the satellite orbit is problematic. To access the orbit for their satellites, these organizations have to rely on third parties such as Celestrak. These entities provide the service free of charge but do not provide orbital parameters with the required frequency. Furthermore, another problem may arise during the mission\u27s early phases. Suppose the satellite is launched together with a number of other satellites, as is often done for small satellites. In that case, it is also not known in the first days or weeks of the mission which orbital parameters are from which satellite launched in the group. This project aims to address the problem of orbital parameter determination by using GPS data, Kalman filters and AI (genetic algorithm)

A new genetic algorithm for the cell formation problem in group technology

Cellular Manufacturing System (CMS) is considered as a competent strategy for batch type production. The motive behind using CMS is to reduce lead time and increase machine utilization. Zero-one machine part incidence matrix based on the machine part routing information is frequently used to form machine cells. In this study, a genetic algorithm is proposed to efficiently solve the Cell Formation (CF) problem considering the machine part incidence matrix. The algorithm is tested by using two different fitness functions on 35 problems from the literature and its performance is benchmarked with the outcomes of the three recent studies. Results are promising in both fitness score perspectives. The algorithm is then applied to datasets obtained from two supplier companies