The　multi-point　dynamic　aggregation(MPDA)problem　is　a　challenging　real-world　problem.In　the　MPDA　problem,the　demands　of　tasks　keep　changing　with　their　inherent　incremental　rates,while　a　heterogeneous　robot　fleet　is　required　to　travel　between　these　tasks　to　change　the　time-varying　state　of　each　task.The　robots　are　allowed　to　collaborate　on　the　same　task　or　work　separately　until　all　tasks　are　completed.It　is　challenging　to　generate　an　effective　task　execution　plan　due　to　the　tight　coupling　between　robots＇abilities　and　tasks＇incremental　rates,and　the　complexity　of　robot　collaboration.For　effectiveness　consideration,we　use　the　variable　length　encoding　to　avoid　redundancy　in　the　solution　space.We　creatively　use　the　adaptive　large　neighborhood　search(ALNS)framework　to　solve　the　MPDA　problem.In　the　proposed　algorithm,high-quality　initial　solutions　are　generated　through　multiple　problem-specific　solution　construction　heuristics.These　heuristics　are　also　used　to　fix　the　broken　solution　in　the　novel　integrated　decoding-construction　repair　process　of　the　ALNS　framework.The　results　of　statistical　analysis　by　the　Wilcoxon　rank-sum　test　demonstrate　that　the　proposed　ALNS　can　obtain　better　task　execution　plans　than　some　state-of-the-art　algorithms　in　most　MPDA　instances.