Drop-off Stations Research Articles

Strategic planning of recycling options by multi-objective programming in a GIS environment

The twentieth century saw a dramatic increase in the production of urban solid waste, reflecting unprecedented global levels of economic activity. Most of the urban cities are facing problems of solid-waste collection, treatment, and disposal due to increasing population, rapid urbanization, industrialization, and commercialization. Learning lessons from the failures of the solid-waste management systems, it is felt that a systematic approach of solid-waste management is a desirable solution, which can be achieved only by a strategic planning approach for achieving a clean, healthy, and sustainable environment. Effective planning of solid-waste recycling programs is a substantial challenge for the current solid-waste management systems in developing countries like India. Due to the rapid depletion of landfill space and the continuing delay in construction programs of municipal incinerators, solid-waste management strategies have to be reorganized in the light of the success of recycling, recovery, and reuse of secondary materials. One of these efforts is how to effectively allocate recycling drop-off stations of appropriate size and how to design efficient collection-vehicle routing and scheduling programs in the solid-waste collection network. The present paper describes a multi-objective evaluation of the trade-off between the number and size of drop-off stations, population covered in the service network, average walking distance to drop-off stations by the population, and the distance traveled by collection vehicles. It is illustrated by the use of algorithms in a geographical information system (GIS) platform. A GIS-based model is proposed for the design of solid-waste system considering the waste generation, allocation, recycling options, and locating drop-off stations.

Siting recycling drop-off stations in urban area by genetic algorithm-based fuzzy multiobjective nonlinear integer programming modeling

Due to the rapid depletion of landfill space and the time-consuming process for siting and building new municipal incinerators, solid waste management strategies have to be reorganized in light of the success of recycling, recovery and reuse of secondary materials. Effective planning of solid waste recycling programs, however, is currently a substantial challenge in many solid waste management systems. One of such efforts is how to effectively allocate the recycling drop-off stations with appropriate size in the solid waste collection network to maximize the recycling achievement with minimum expense. This paper illustrates a new approach with a view to optimizing siting and routing aspects using a fuzzy multiobjective nonlinear integer programming model as a means that is particularly solved by a genetic algorithm. The case study, based on one of the administrative districts in the city of Kaohsiung in Taiwan, presents the application potential of such a planning methodology.

Strategic Planning of Recycling Drop-Off Stations and Collection Network by Multiobjective Programming.

/ Effective planning of solid-waste recycling programs is a substantial challenge to the current solid-waste management systems in Taiwan. Due to the rapid depletion of landfill space and the continuing delay in construction programs of municipal incinerators, solid-waste management strategies have to be reorganized in light of the success of recycling, recovery, and reuse of secondary materials. One of these efforts is how to effectively allocate recycling drop-off stations of appropriate size and how to design efficient collection-vehicle routing and scheduling programs in the solid waste collection network. This management strategy is particularly important in the privatized system with recycling containers and material recovery facilities (MRFs) owned by one agency. This research seeks multiobjective evaluation of the trade-off between the number and size of drop-off stations, the population covered in the service network, the average walking distance to drop-off stations by the population, and the distance traveled by collection vehicles. It also illustrates the use of the multiobjective nonlinear mixed integer programming model to achieve such goals that are solved by the genetic algorithms (GA) in a geographical information system (GIS) platform. The case study shows the application potential of such a methodology in the city of Kaohsiung in Taiwan.KEY WORDS: Optimization analysis; Recycling; Solid waste managementhttp://link.springer-ny.com/link/service/journals/00267/bibs/24n2p247.html