Nuortio et al. [48] describe the zoning of a service territory in
Eastern Finland by determining vehicle routes and schedules for
the collection of municipal solid waste. The problem is modeled as
a Stochastic Periodic Vehicle Routing Problem with Time Windows
and a limited number of vehicles, which is solved by the Guided
Variable Neighborhood Thresholding (GVNT) metaheuristic [49].
The stochasticity lies in the accumulation rate of waste in each
container type, and in the travel times. The proposed approach is
made up of two phases. In the first phase, a feasible solution is
created with a hybrid insertion heuristic. Then, in the subsequent
phase, the GVNT metaheuristic is used to try to improve the initial
solution. GVNT is based on three well known metaheuristic
principles, namely guided local search [50], variable neighborhood
search [51], and threshold accepting [52]. When compared to the
current solution of the waste management company, it is shown
that significant improvements (up to 46%) can be obtained.
Nuortio et al. [48] describe the zoning of a service territory inEastern Finland by determining vehicle routes and schedules forthe collection of municipal solid waste. The problem is modeled asa Stochastic Periodic Vehicle Routing Problem with Time Windowsand a limited number of vehicles, which is solved by the GuidedVariable Neighborhood Thresholding (GVNT) metaheuristic [49].The stochasticity lies in the accumulation rate of waste in eachcontainer type, and in the travel times. The proposed approach ismade up of two phases. In the first phase, a feasible solution iscreated with a hybrid insertion heuristic. Then, in the subsequentphase, the GVNT metaheuristic is used to try to improve the initialsolution. GVNT is based on three well known metaheuristicprinciples, namely guided local search [50], variable neighborhoodsearch [51], and threshold accepting [52]. When compared to thecurrent solution of the waste management company, it is shownthat significant improvements (up to 46%) can be obtained.
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