This　paper　focuses　on　optimizing　the　secondary　allocation　of　arrival　and　departure　slots　in　the　context　of　airlines　autonomously　canceling　flights.　A　bi-objective　optimization　model　is　developed　to　minimize　both　the　total　delay　costs　of　airlines　and　the　total　delay　time　of　passengers,　effectively　addressing　the　needs　of　both　stakeholders.　A　parameter　λ　is　introduced　to　balance　the　discrepancies　in　the　delay　cost　functions　for　arrival　and　departure　flights.　Additionally,　constraints　are　implemented　to　differentiate　the　turnaround　time　for　flights　based　on　the　types　of　aircraft.　According　to　the　model＇s　characteristics,　real　number　coding　is　utilized,　and　the　gene　position　corresponding　to　the　canceled　flight　is　represented　by　-　1　.　Furthermore,　a　learning　mechanism　is　integrated　into　the　genetic　operations.　Distinct　crossover　and　mutation　probabilities　are　established　for　both　dominated　and　non-dominated　individuals,　and　a　Q-learning-driven　general　variable　neighborhood　search　strategy　is　implemented　for　elite　individuals.　The　experimental　results　from　three　sets　of　examples　indicate　that　the　solving　times　of　the　improved　algorithm　were　29.34　s,　58.61　s,　and　125.21　s,　resulting　in　9,　8,　and　8　optimal　schedules,　respectively.　In　comparison　to　the　first　come　first　served　(FCFS)　method,　the　total　delay　costs　of　airlines　were　reduced　by　14.85%,　8.47%,　and　9.18%.　Additionally,　the　total　delay　times　of　passengers　decreased　by　1.03%,　5.31%,　and　4.68%.　Compared　with　the　non-cancellation　strategy,　the　total　delay　costs　of　airlines　under　the　cancellation　strategy　decreased　by　7.04%,　9.38%　and　11.96%,　and　the　total　delay　time　of　passengers　increased　by　0.95%,　1.21%　and　1.70%,　respectively.　The　bi-objective　optimization　model　developed　in　this　paper,　along　with　the　proposed　enhanced　algorithm,　effectively　reduces　airlines＇　delay　costs　while　considering　passenger　interests.　This　approach　offers　valuable　decision-making　support　for　the　slot　secondary　allocation　of　arrival　and　departure　flights　under　autonomous　cancellations.　©　2025　Science　Press.　All　rights　reserved.