Supply-demand allocation is important for supporting emergency food material management and decision making.This study proposed a supply-demand allocation method for market-supplied materials.The method considers the ...Supply-demand allocation is important for supporting emergency food material management and decision making.This study proposed a supply-demand allocation method for market-supplied materials.The method considers the constraint that market supply reserve depots(MSDs) need to preferentially supply emergency food materials to original demand points,which is mostly neglected in traditional methods.The constraint enables the method to provide a more rational allocation scheme of MSDs.Based on the supply-demand allocation method,an emergency material distribution path planning method under flood scenarios was further developed.Unlike the traditional methods,which mostly neglect simultaneous consideration of the travel time and path reliability factors,this method comprehensively achieves two critical objectives:the shortest path travel time and highest path reliability.The heuristic algorithms are used to solve the optimal path.It can enhance the safety and reliability of food material distributions.Three criteria-degree,squares clustering coefficient,and road design daily traffic volume-are integrated to evaluate the reliability of each road section based on the real road networks,and the impact of the flood on travel time is fully considered.A case study in Fengxian District,Shanghai,China,was conducted to demonstrate the feasibility of the method.Three categories of supplies-rice,drinking water,and infant milk-were chosen to represent the food supplies.The results of the case study can support decision making for emergency rescue and relief efforts of relevant government departments.The methods proposed provide methodological references for related studies in other similar regions.展开更多
Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited reso...Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.展开更多
基金funded by the National Natural Science Foundation of China (Grant Nos. 72074151, 42101314, and 42171282)supported by the Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, Shanghai, China
文摘Supply-demand allocation is important for supporting emergency food material management and decision making.This study proposed a supply-demand allocation method for market-supplied materials.The method considers the constraint that market supply reserve depots(MSDs) need to preferentially supply emergency food materials to original demand points,which is mostly neglected in traditional methods.The constraint enables the method to provide a more rational allocation scheme of MSDs.Based on the supply-demand allocation method,an emergency material distribution path planning method under flood scenarios was further developed.Unlike the traditional methods,which mostly neglect simultaneous consideration of the travel time and path reliability factors,this method comprehensively achieves two critical objectives:the shortest path travel time and highest path reliability.The heuristic algorithms are used to solve the optimal path.It can enhance the safety and reliability of food material distributions.Three criteria-degree,squares clustering coefficient,and road design daily traffic volume-are integrated to evaluate the reliability of each road section based on the real road networks,and the impact of the flood on travel time is fully considered.A case study in Fengxian District,Shanghai,China,was conducted to demonstrate the feasibility of the method.Three categories of supplies-rice,drinking water,and infant milk-were chosen to represent the food supplies.The results of the case study can support decision making for emergency rescue and relief efforts of relevant government departments.The methods proposed provide methodological references for related studies in other similar regions.
基金supported by National Natural Science Foundation of China(No.62072436 and No.62202449)National Key Research and Development Program of China(2021YFB2900102).
文摘Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.
