This　work　investigates　a　new　dynamic　berth　and　quay　crane　allocation　problem　by　comprehensively　considering　practical　factors　such　as　tidal　effects　and　constraints　on　the　berthing　areas　of　different　types　of　vessels,　aiming　to　optimally　determine　the　berthing　time,　berthing　location,　and　specific　quay　crane　allocation　for　different　types　of　vessels　to　minimize　the　costs　of　vessel　delay　and　berth　deviation.　First,　we　formulate　the　problem　into　a　mixed-integer　linear　programming　model.　Then,　we　address　complex　constraints　such　as　non-overlapping　spatial　and　temporal　intervals　for　vessels　and　non-crossing　of　quay　cranes　through　a　dynamic　time-berth-quay　table　and　a　quay　crane　numbering　directed　acyclic　graph.　Based　on　this,　an　adaptive　variable　neighborhood　search　algorithm　(AVNS)　includes　neighborhood　structure　operators　based　on　the　number　of　quay　cranes　and　types　of　berths,　and　integrates　a　vessel　berthing　sequence　perturbation　operator　to　enhance　the　algorithm’s　optimization　capability.　Extensive　numerical　experiments　based　on　the　real-world　case　of　Jiangyin　Port　in　Fuzhou　show　that　the　AVNS　achieves　an　average　deviation　of　3.24%　from　the　best　solution　obtained　by　the　commercial　solver　CPLEX.　For　large-scale　cases,　the　AVNS　on　average　requires　only　33.51　s　to　obtain　high-quality　near-optimal　solutions.　Compared　to　the　solutions　obtained　using　the　variable　neighborhood　descent　algorithm,　the　proposed　AVNS　achieves　an　average　cost　saving　of　38.69%.　©　2025　Northeast　University.　All　rights　reserved.