Microsoft has promoted ASP.NET’s new web services more than almost any other part of the.NET Framework. But despite their efforts, confusion is still widespread about what a web service is and, more importantly, what it’s meant to accomplish. This chapter introduces web services and explains their role in Microsoft’s vision of the programmable web. Along the way, you’ll learn about the open standards plumbing that allows web services to work and removes some of the confusion surrounding technical terms like WSDL (Web Service Description Language), SOAP, and UDDI (universal description, discovery, and integration).

Web Services Architecture

Solid waste management in European countries: a review of systems analysis techniques.

Image encryption process based on chaotic synchronization phenomena

We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem associated with collection of recycling waste from households, treated as nodes in a spatial network. For networks where the nodes are concentrated in separate clusters, the use of k-means clustering can greatly improve the efficiency of the solution. The ACS algorithm is extended to model the use of multi-compartment vehicles with kerbside sorting of waste into separate compartments for glass, paper, etc. The algorithm produces high-quality solutions for two-compartment test problems.

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An ant colony algorithm for the multi-compartment vehicle routing problem

An overview of the optimization modelling applications

Optimal solid waste collection routes identified by the ant colony system algorithm.

GIS-based modelling for the estimation of municipal solid waste generation and collection.

During the last decade, metaheuristics have become increasingly popular for effectively confronting difficult combinatorial optimization problems. In the present paper, two individual meatheuristic algorithmic solutions, the ArcGIS Network Analyst and the Ant Colony System (ACS) algorithm, are introduced, implemented and discussed for the identification of optimal routes in the case of Municipal Solid Waste (MSW) collection. Both proposed applications are based on a geo-referenced spatial database supported by a Geographic Information System (GIS). GIS are increasingly becoming a central element for coordinating, planning and managing transportation systems, and so in collaboration with combinatorial optimization techniques they can be used to improve aspects of transit planning in urban regions. Here, the GIS takes into account all the required parameters for the MSW collection (i.e. positions of waste bins, road network and the related traffic, truck capacities, etc) and its desktop users are able to model realistic network conditions and scenarios. In this case, the simulation consists of scenarios of visiting varied waste collection spots in the Municipality of Athens (MoA). The user, in both applications, 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. Finally, the optimal solution is estimated by each routing optimization algorithm, followed by a comparison between these two algorithmic approaches on the newly designed collection routes. Furthermore, the proposed interactive design of both approaches has potential application in many other environmental planning and management problems.

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Routing optimization heuristics algorithms for urban solid waste transportation management

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.

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Genetic Algorithms for Municipal Solid Waste Collection and Routing Optimization

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.

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Nikolaos V. Karadimas

Papers

Optimal solid waste collection routes identified by the ant colony system algorithm.

GIS-based modelling for the estimation of municipal solid waste generation and collection.

Routing optimization heuristics algorithms for urban solid waste transportation management

Genetic Algorithms for Municipal Solid Waste Collection and Routing Optimization

Municipal waste collection of large items optimized with arc gis Network Analyst