Research And Design Of Optimal Dispatching System For Operation Of Sanitation Vehicles

With the annual growth of municipal solid waste output and the comprehensive implementation of the waste classification policy,the urban sanitation work bears great pressure.The operation of sanitation vehicles is an important part of urban domestic waste treatment.Whether the collection of sanitation vehicles is comprehensive and timely directly affects the living standards of residents,and whether the operation track of sanitation vehicles is reasonable and efficient affects the cost of urban domestic waste treatment.The existing research on municipal solid waste(MSW)mainly focuses on the source control of MSW and the process control of MSW treatment plant,while the research on intermediate collection and operation of MSW is less.Aiming at the problems existing in the collection and transportation of municipal solid waste,combining with the application of Internet of things in the sanitation system,this paper puts forward the research and design of the operation optimization scheduling system of sanitation vehicles.This paper studies the optimization of the operation path of sanitation vehicles at home and abroad and the application of modern technology in the operation and scheduling system of sanitation vehicles,and briefly expounds the collection,transportation,treatment of domestic urban domestic garbage and the scheduling of sanitation vehicles.The application of Internet of things technology in the equipment and facilities of sanitation vehicle in each operation stage is proposed for the scheduling problem of sanitation vehicle,and the framework of sanitation operation scheduling system based on Internet of things is established.This paper analyzes the existing vehicle scheduling algorithms,selects the second generation of non dominated sorting genetic algorithm(NSGA-II)as the basic algorithm to solve the first level operation scheduling model of sanitation vehicles,considering that the urban garbage collection points are discrete points with great differences,and proposes to add the density peak clustering algorithm to the second generation of non dominated sorting genetic algorithm(DPCA-NSGA-?)in NSGA-II based on the density peak clustering The coding,fitness evaluation,selection,crossover and mutation of the algorithm are optimized to improve the accuracy of the solution.The ant colony optimization(ACO)algorithm is selected as the basic algorithm to solve the two-level operation scheduling model of sanitation vehicles.Considering the slow convergence speed and easy to fall into the local part of ACO algorithm,this paper proposes the regional damage reconstruction of the ant colony optimization(RARACO)algorithm by adding the regional damage reconstruction operator and the 2-opt operator and optimizing the pheromone transfer mode to improve the ACO algorithm,which improves the convergence speed and has a higher accuracy at the same time And better robustness.The simulation results show that the convergence accuracy of DPCA-NSGA-? and RARACO algorithm is significantly improved.Through the qualitative and quantitative analysis of the distribution of garbage collection points,garbage collection,operation and treatment equipment in the study area,combined with the existing problems of the sanitation vehicle scheduling system.A multi-objective vehicle routing problem(VRP)model with time window,combined operation of garbage classification and other constraints,minimum operation cost,minimum number of vehicles and uniform number of vehicles is established.A single objective VRP sanitation vehicle operation scheduling model with dynamic time window and classified operation is established.According to the different operation modes and constraints of the established operation model of sanitation vehicle,DPCA-NSGA-? algorithm is used for the first level operation model of sanitation vehicle,and RARACO tree algorithm is used for the second level operation model of sanitation vehicle for simulation.The simulation results show that,compared with the traditional experience,the number of sanitation vehicles,driving distance,waste operation cost and efficiency are significantly improved.