Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

Comparison of the Anthropogenic Emission Inventory for CMIP6 Models with a Country-Level Inventory over China and the Simulations of the Aerosol Properties

Anthropogenic emission inventory for aerosols and reactive gases is crucial to the estimation of aerosol radiative forcing and climate effects.Here,the anthropogenic emission inventory for AerChemMIP,endorsed by CMIP6,is briefly introduced.The CMIP6 inventory is compared with a country-level inventory(i.e.,MEIC)over China from 1986 to 2015.Discrepancies are found in the yearly trends of the two inventories,especially after 2006.The yearly trends of the aerosol burdens simulated by CESM2 using the two inventories follow their emission trends and deviate after the mid-2000s,while the simulated aerosol optical depths(AODs)show similar trends.The difference between the simulated AODs is much smaller than the difference between model and observation.Although the simulated AODs agree with the MODIS satellite retrievals for country-wide average,the good agreement is an offset between the underestimation in eastern China and the overestimation in western China.Low-biased precursor gas of SO_(2),overly strong convergence of the wind field,overly strong dilution and transport by summer monsoon circulation,too much wet scavenging by precipitation,and overly weak aerosol swelling due to low-biased relative humidity are suggested to be responsible for the underestimated AOD in eastern China.This indicates that the influence of the emission inventory uncertainties on simulated aerosol properties can be overwhelmed by model biases of meteorology and aerosol processes.It is necessary for climate models to perform reasonably well in the dynamical,physical,and chemical processes that would influence aerosol simulations.