Abstract
Effective planning of solid waste collection and recycling programs is challenging for current solid waste management (SWM) systems in urban regions. The process usually requires evaluating many assignment alternatives of collection vehicles, bins and recycling drop-off stations, as well as appropriate scheduling of vehicles and labor that can be optimally allocated or dispatched with respect to a suite of physical, technical, and economic constraints. This chapter begins with the simulation-driven approach to support optimization analysis. It uses a Geographical Information System (GIS) for siting the recycling drop-off stations in a fast-growing urban district in the City of Kaohsiung, Taiwan. A heuristic algorithm was employed with respect to the dynamics of population growth and shipping distance required by collection vehicles in the beginning. The chapter formulates a multiobjective, nonlinear mixed-integer programming model to replace the heuristic algorithm to achieve the same goal by applying genetic algorithms in the same GIS environment.
Published Version
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