This　paper　is　an　attempt　to　explore　optimal　routes　and　aborting　strategies　of　trucks　and　drones　under　random　attacks.　Time　windows　of　targets,　aborting　strategies　of　trucks　and　random　attacks　are　considered　in　the　truck-drone　routes,　which　not　only　fixes　the　shortcomings　of　drones,　such　as　its　small　load　and　short　working　hours,　but　also　conducts　a　preliminary　survey　to　gain　disaster　or　enemy　information　in　the　field　of　emergency　management　or　military.　It　is　assumed　that　drones　and　trucks　may　be　attacked　in　the　mute　and　the　truck　may　abort　its　task　according　to　the　number　of　tasks　finished　and　the　number　of　attacks　suffered.　In　addition,　based　on　a　constraint　programming　model,　this　paper　proposes　a　hybrid　algorithm　named　ALNS-SP　algorithm　combined　with　the　adaptive　large　neighborhood　search　and　the　short-path　algorithm　to　solve　the　vehicle　routing　problem　with　trucks　and　drones　considering　random　attacks　problem　(VRP-TDA).　A　test　set　based　on　Solomon　datasets　is　designed　and　comparative　numerical　experiments　are　presented　to　illustrate　the　performance　of　the　ALNS-SP　algorithm.