Local search for the undirected capacitated arc routing problem with profits

This paper deals with a recently introduced routing problem variant called the undirected capacitated arc routing problem with profits (UCARPP). The UCARPP model considered in the present study is primarily aimed at generating the route set which maximizes the profit collected from a set of potential customers, represented by edges of the examined transportation network. The secondary objective is to minimize the total route travel time. The generated routes are subject both to capacity and travel time constraints. To tackle the examined problem, we propose a local search metaheuristic development which explores two solution neighborhood structures. The conducted search is effectively diversified by means of the promises concept which is based on the aspiration criteria used in tabu search approaches. The proposed algorithm was tested on UCARPP benchmark instances taken from the literature. It efficiently produced high-quality results, improving several previously best known solutions.Arc routing with profits Metaheuristics Local search Aspiration criteria

Automatic identification of oil spills on satellite images

A fully automated system for the identification of possible oil spills present on Synthetic Aperture Radar (SAR) satellite images based on artificial intelligence fuzzy logic has been developed. Oil spills are recognized by experts as dark patterns of characteristic shape, in particular context. The system analyzes the satellite images and assigns the probability of a dark image shape to be an oil spill. The output consists of several images and tables providing the user with all relevant information for decision-making. The case study area was the Aegean Sea in Greece. The system responded very satisfactorily for all 35 images processed. The complete algorithmic procedure was coded in MS Visual C++ 6.0 in a stand-alone dynamic link library (dll) to be linked with any sort of application under any variant of MS Windows operating system. © 2004 Elsevier Ltd. All rights reserved

A Multidisciplinary Decision Support System for Forest Fire Crisis Management

A wildland fire is a serious threat for forest ecosystems in Southern Europe affecting severely and irreversibly regions of significant ecological value as well as human communities. To support decision makers during large-scale forest fire incidents, a multidisciplinary system has been developed that provides rational and quantitative information based on the site-specific circumstances and the possible consequences. The system's architecture consists of several distinct supplementary modules of near real-time satellite monitoring and fire forecast using an integrated framework of satellite Remote Sensing, GIS, and RDBMS technologies equipped with interactive communication capabilities. The system may handle multiple fire ignitions and support decisions regarding dispatching of utilities, equipment, and personnel that would appropriately attack the fire front. The operational system was developed, for the region of Penteli Mountain in Attika, Greece, one of the mountain areas in the country most hit by fires. Starting from a real fire incident in August 2000, a scenario is presented to illustrate the effectiveness of the proposed approach

Radial basis function neural networks classification using very high spatial resolution satellite imagery: An application to the habitat area of Lake Kerkini (Greece)

This study investigates the potential of applying the radial basis function (RBF) neural network architecture for the classification of multispectral very high spatial resolution satellite images into 13 classes of various scales. For the development of the RBF classifiers, the innovative fuzzy means training algorithm is utilized, which is based on a fuzzy partition of the input space. The method requires only a short amount of time to select both the structure and the parameters of the RBF classifier. The new technique was applied to the area of Lake Kerkini, which is a wetland of great ecological value, located in northern Greece. Eleven experiments were carried out in total in order to investigate the performance of the classifier using different input parameters (spectral and textural) as well as different window sizes and neural network complexities. For comparison purposes the same satellite scene was classified using the maximum likelihood (MLH) classification with the same set of training samples. Overall, the neural network classifiers outperformed the MLH classification by 10-17%, reaching a maximum overall accuracy of 78%. Analysis showed that the selection of input parameters is vital for the success of the classifiers. On the other hand, the incorporation of textural analysis and/or modification of the window size do not affect the performance substantially. © 2005 Taylor & Francis Group Ltd

A Multidisciplinary Decision Support System for Forest Fire Crisis Management

A wildland fire is a serious threat for forest ecosystems in Southern Europe affecting severely and irreversibly regions of significant ecological value as well as human communities. To support decision makers during large-scale forest fire incidents, a multidisciplinary system has been developed that provides rational and quantitative information based on the site-specific circumstances and the possible consequences. The system's architecture consists of several distinct supplementary modules of near real-time satellite monitoring and fire forecast using an integrated framework of satellite Remote Sensing, GIS, and RDBMS technologies equipped with interactive communication capabilities. The system may handle multiple fire ignitions and support decisions regarding dispatching of utilities, equipment, and personnel that would appropriately attack the fire front. The operational system was developed, for the region of Penteli Mountain in Attika, Greece, one of the mountain areas in the country most hit by fires. Starting from a real fire incident in August 2000, a scenario is presented to illustrate the effectiveness of the proposed approach

Modeling and optimization of a tunnel grape dryer

A mathematical model of a tunnel dryer for the dehydration of grapes is presented and applied to the determination of optimal operating conditions of the dryer. The dryer is of semi-batch structure, operating with trucks and trays. The cycle period is determined by meeting appropriate quality specifications for the final product. The nominal conditions were evaluated by suitably minimizing the total fuel demand, expressed as fuel consumption to production capacity, under some constraints regarding the production rate of the dryer and the maximum permissible air temperature. An nominal air humidity value was evaluated suggesting a minimum cycle period value for the production capacity and fuel demand. The nominal conditions required operation of the dryer on the maximum permissible air temperature. The optimum operation was evaluated by maximizing the total profit resulting from the operation of the dryer. The optimization variables were temperature and humidity of the drying air stream. A characteristic case study of industrial grape was included to illustrate the effectiveness of the proposed approach

Design of Tray Dryers for Food Dehydration

A mathematical model for the semi-batch operation of industrial dryers with trucks and trays is presented and analysed. Design aspects are discussed concerning problems involving both single dryer and systems of parallel dryers. In both cases, optimum flowsheet configuration and operation conditions are sought and verified by appropriate formulation of design and optimization strategies. The optimization objective is the total annual cost of the plant, subjected to constraints imposed by the operation of the dryer, thermodynamics, and construction reasoning. The decision variables were the number of trucks and the drying air stream humidity for each dryer involved, as well as the total number of dryers. The MINLP nature of the design problem required mathematical programming techniques for its solution. The optimization was carried out for a wide range of production capacities, and the optimal points, where a new truck or a new dryer is introduced, were evaluated. The effect of market economic figures on the design results is illustrated. The analysis focused on the design of two commercial agricultural products -namely, raisins and currants. A characteristic case study is presented in order to demonstrate the effectiveness of the proposed approach. © 1997 Elsevier Science Limited