Waste Collection Area Generation Using a 2 Stage Cluster Optimization Process and GIS Data

The division of a city into several waste collection areas can have a large influence on the workload distribution put in place for the waste collection personnel. Additionally, areas with rapid urban development can benefit from a systematic way to automate the collection area division given that there is geographical information containing stop locations with an approximate number of dwelling units. This paper proposes a 2-stage collection area optimization process using the weighted K-means algorithm paired with differential evolution to minimize the standard deviation of dwelling units across each collection area. Results from a case study in The City of Oshawa, Canada prove that the proposed clustering techniques can yield a set of collection areas with 87.75% improvement compared to the current arrangement in terms of balancing the dwelling units. Additionally, the same clustering techniques can be used to assign collection routes for the vehicles in each area. A combination of Dijkstra’s and Hierholzer’s algorithms is applied to generate a route simulation with accompanying statistics regarding the total distance travelled, collection time, travel time, and fuel consumed. Specific to the case study in The City of Oshawa, each day of the week has 2 collection areas, and a genetic algorithm is used to find the optimal collection area pairs. Results from the collection area pairing show that there is a 38.04% and 37.54% improvement of simulated statistics for Week 1 and 2 collections, respectively.