Abstract

Abstract
This paper describes an integrated application that performs a geostatistical analysis of data acquired by an AUV in monitoring missions to sewage outfalls. This comes as an effort for automating the procedures of a monitoring campaign from data acquisition to data processing. This application is based on the R statistical software and uses the Gstat package for the geostatistical prediction. R is a console based application that uses software packages developed by the community. The application interfaces with R guiding the user through several steps that perform the geostatistical analysis. It was not our intention to cover all geostatistical procedures but only the ones that are needed for the data processing concerned. The major advantage of this application is that the user does not need to be familiar with methods and data structures associated with the base software, allowing the processing and analysis to be more simple, fast and efficient which is particularly important for routine monitoring. This software application also enables us to give a quicker response in case of contamination to near-by beaches.

Abstract
This paper describes an integrated application that automates the procedure for sea outfall discharges data acquisition with an Autonomous Underwater Vehicle (AUV). Since most applications for this type of technology are research related, the used software tends to be more technical, oriented for engineers. This fact, allied with the bad sea conditions usually encountered at the portuguese coast, cause the mission execution to be extremely difficult at times. Before starting operating the AUV, a wide range of operations must be completed: we need to get data to estimate plume position, calculate mission path, transfer the AUV and acoustic buoys to the water, test communications and configure a variety of systems. So clearly there is a need to develop an application that fully automates a monitoring mission, allowing the operator with little to no experience to conclude it efficiently. Ultimately, by automating the procedure, there is the possibility of expanding the use of AUV's across several fields of study since no prior knowledge about the its systems is required. In summary this guides the user through a series of tasks and provides visual and audio information.

Abstract
The main purpose of this study was to examine the applicability of geostatistical modeling to obtain valuable information for assessing the environmental impact of sewage outfall discharges. The data set used was obtained in a monitoring campaign to S. Jacinto outfall, located off the Portuguese west coast near Aveiro region, using an AUV. The Matheron's classical estimator was used the compute the experimental semivariogram, which was fitted to three theoretical models: spherical, exponential and Gaussian. The cross-validation procedure suggested the best semivariogram model and ordinary kriging was used to obtain the predictions of salinity at unknown locations. The generated map shows clearly the plume dispersion in the studied area, indicating that the effluent does not reach the nearby beaches. Our study suggests that an optimal design for the AUV sampling trajectory from a geostatistical prediction point of view, can help to compute more precise predictions and hence to quantify more accurately dilution. Moreover, since accurate measurements of plume's dilution are rare, these studies might be very helpful in the future for validation of dispersion models.

Abstract
We address a problem that often arises in industry, the multi-item capacitated-lot-sizing and scheduling problem with sequence-dependent setup times and costs. Powerful commercial solvers fail to solve even medium-sized instances of this NP-hard problem, therefore we employ a tabu search and a variable neighbourhood search meta-heuristic to solve it and compare the performance of these metaheuristics over time. In contrast to the majority of the literature on this topic, the solution representation explicitly considers production quantities and setup variables, which enables us to develop fast search heuristics. A comprehensive set of computational experiments shows the effectiveness and efficiency of the proposed approaches in solving medium-to large-sized problems.

Abstract
In this paper we present a genetic algorithm with new components to tackle capacitated lot sizing and scheduling problems with sequence dependent setups that appear in a wide range of industries, from soft drink bottling to food manufacturing. Finding a feasible solution to highly constrained problems is often a very difficult task. Various strategies have been applied to deal with infeasible solutions throughout the search. We propose a new scheme of classifying individuals based on nested domains to determine the solutions according to the level of infeasibility, which in our case represents bands of additional production hours (overtime). Within each band, individuals are just differentiated by their fitness function. As iterations are conducted, the widths of the bands are dynamically adjusted to improve the convergence of the individuals into the feasible domain. The numerical experiments on highly capacitated instances show the effectiveness of this computational tractable approach to guide the search toward the feasible domain. Our approach outperforms other state-of-the-art approaches and commercial solvers.