Route Optimisation for Solid Waste Management Using GeoInformatics

Abstract

Due to rapid industrialisation, population of Chandrapur city of Maharashtra state of India is increasing day by day, hence increasing the solid waste generation. Around 0.25 kg /person/day solid waste is generated and around 80 ton wastes are dumped in dumping site daily. Collection and transportation is one of the important functional components of solid waste management. Thus, to keep city clean and environmentally healthy, the management of solid waste has now becoming an important issue. Geo-informatics is proposed to determine the minimum cost/distance efficient collection paths for transporting the solid wastes to the dumping site. This uses information on population density, waste generation capacity, road network, storage bins and collection vehicles etc. This paper attempted to optimise the route for collection and transportation of municipal solid waste of the city using geo-informatics technique. Initially all ancillary data have been collected from various sources. Using hand held GPS, the position of the entire dust bin in the city are collected and the data are converted into shape file. The roads are digitised from the road network map of the municipal corporation. From the shape file of existing road network, network database for the entire road are prepared using Arc-GIS 9.2. Taking capacity of solid waste carrying vehicle into consideration, the routes are optimised for each trip of vehicle and total distance to be travelled is calculated for each trip. Finally cost is compared for solid waste disposal using this technique with the existing system of Municipal Corporation. From the analysis and results, it is concluded that the Geo-informatics technique gives better accuracy for route optimisation and can be used as a decision support tool by municipal authorities for efficient management of the daily operations for transporting solid wastes, load balancing within vehicles, managing fuel consumption and generating work schedules for the workers and vehicles for overall cost minimisation.