Nikolaos V. Karadimas

Municipal Waste Collection Of Large Items Optimized With ArcGis Network Analyst

In the present paper the ArcGIS Network Analyst Algorithm is introduced for best routing identification applied in municipal waste collection of large items. The proposed application takes into account all the required parameters for the waste collection of large items so as its desktop users to be able to model realistic network conditions and scenarios. In this case, the simulation consists of scenarios of visiting loading spots in the municipality of Athens, in order to collect large items that couldn’t be collected by the standard waste collection trucks, due to size and other prohibitive obstacles. The Network Analyst is used to estimate interrelations between the dynamic factors, like network traffic changes (closed roads due to natural or technical causes, for example, fallen trees, car accidents, etc) in the area under study and to produce optimized solutions. The user is able to define or modify all the required dynamic factors for the creation of an initial scenario, and by modifying these particular parameters, alternative scenarios can be generated leading to several solutions. Finally, the optimal solution is identified by a function that takes into consideration various parameters, for example the shortest distance, road network as well as social and environmental implications.

Genetic Algorithms for Municipal Solid Waste Collection and Routing Optimization

In the present paper, the Genetic Algorithm (GA) is used for the identification of optimal routes in the case of Municipal Solid Waste (MSW) collection. The identification of a route for MSW collection trucks is critical since it has been estimated that, of the total amount of money spent for the collection, transportation, and disposal of solid waste, approximately 60–80% is spent on the collection phase. Therefore, a small percentage improvement in the collection operation can result to a significant saving in the overall cost. The proposed MSW management system is based on a geo-referenced spatial database supported by a geographic information system (GIS). The GIS takes into account all the required parameters for solid waste collection. These parameters include static and dynamic data, such as the positions of waste bins, the road network and its related traffic, as well as the population density in the area under study. In addition, waste collection schedules, truck capacities and their characteristics are also taken into consideration. Spatiotemporal statistical analysis is used to estimate inter-relations between dynamic factors, like network traffic changes in residential and commercial areas. The user, in the proposed system, is able to define or modify all of the required dynamic factors for the creation of alternative initial scenarios. The objective of the system is to identify the most cost-effective scenario for waste collection, to estimate its running cost and to simulate its application.

Optimised information system reliability techniques for knowledge society acceptance

Two important obstacles averting the widespread adoption of knowledge society and e-government technologies in communities are distrust in their reliability and fear of the costs to be incurred. In order to overcome these obstacles, algorithmic advances are required that improve reliability of information systems without increasing their cost. In this paper a new approach to increase the reliability and effectiveness of transient error detection using checksum calculations is proposed, that is based on the optimisation of coding with special differential Boolean transformations. Two methods for obtaining such transformations are developed and examples of coding for checksum functions are given. The new methods are equivalent in terms of computational cost as classical checksum approaches.

Fuzzy Representation of Land Cover Classes Using Digital Geo-Data

The earth surface and human activities are interrelated in a complicated manner, and therefore it is somehow difficult to be monitored and managed. On this context it is required to develop effective directives of policies for environmental protection and management. In order to exploit context in imagery, a methodological approach employed data processing techniques was proposed in this work This approach was applied on a LANDSAT-TM satellite image and other digital geo-data (such as scanned geologic maps, topographic maps, etc) of a selected study area in Greece. The proposed scheme generally consisted of the following stages: (1) remote sensing methods and analysis for extracting the required thematic information to be used as input data and (2) fuzzy representation of the intrinsic land cover classes in the geodata through an object-oriented knowledge base design. The inherent land cover classes were represented by their intrinsic spectral and geometric attributes, texture, spatial context and association and fuzzy membership functions.