Research On The Optimization Of Dynamic Waste Collection Paths Based On Smart Bins

With the accelerated urbanization process,China’s urban population continues to increase.According to the National Bureau of Statistics,China’s urbanization rate is expected to reach 70% by 2030.There is a close relationship between the amount of urban waste and the urbanization rate,and the total amount of urban waste shows a trend of year-on-year increase.If urban waste is not properly disposed of,it will not only affect the appearance of the environmental but also threaten human health.However,there is a great deal of uncertainty about the amount of waste during the actual collection process.With the construction of smart cities and the gradual popularization of intelligent waste collection terminals,it is possible to monitor the time-varying filling level of bins.In this thesis,considering the randomness of waste generation,periodic optimization strategies(local optimization and global optimization)and a combination of periodic and continuous optimization strategy are proposed to solve the problem of dynamic waste collection path optimization.Firstly,considering the amount of waste,vehicle departure time,vehicle capacity constraints,and bin collection thresholds,we established a waste dynamic collection vehicle path optimization model to minimize environmental and economic costs.In the process of model construction,the objective function and constraints of the model are distinguished according to the differences in the solution process of the three strategies.Secondly,a two-stage algorithm is proposed to solve the problem of optimizing the dynamic collection path of waste where new demand points appear at any time during the collecting operation.A particle swarm algorithm is used to plan the initial collection path for the bin to be collected,and a local optimization periodicity strategy,a global optimization periodicity strategy,periodicity and continuity optimization strategy are proposed to dynamically adjust the vehicle collection path.Both the local optimization periodicity strategy and the global optimization periodicity strategy divide the working time into several periods and adjust the vehicle path at the end of the period.The difference is that the local optimization periodicity strategy inserts new bins into the existing path,while the global optimization periodicity strategy uses a particle swarm algorithm to optimize the path for the new bins together with the bins that have not been collected in the previous period.The periodicity and continuity optimization strategy will collect the new bins in time,which will reduce the risk of negative environmental effects caused by untimely waste removal.Finally,a sensitivity analysis is done on the collection threshold and distance decision threshold to determine their impact on economic costs,environmental costs and collection distances.Three optimization strategies are also used to compare and analyze the standard and simulation cases of different scales to rationalize the collection routes.The results show that the economic cost,environmental cost,and collection distance are better when the collection threshold is equal to 0.6 and the distance decision threshold is equal to 0.3.Compared to the local optimization periodicity strategy,the global optimization periodicity strategy solves small-scale cases with more significant improvement.The periodicity and continuity optimization strategy is superior to the two periodic strategies in solving the dynamic waste collection path optimization problem.