This study aims to enhance the Dandelion Algorithm with Variable Neighborhood Descent (DA-VND) for solving the Integrated Parallel Machine Scheduling and Vehicle Routing Problem (IPSVR). Two adaptive variants, DA-L2-VND and DA-L3-VND, were developed based on Hsu Che-lun’s adaptive sowing strategies and integrated into the DA-VND framework without modifying the baseline parameters from previous research. Computational experiments were conducted on benchmark instances of two scales: (20,5,5) and (50,15,15). Under a 300-second time limit, DA-L2-VND achieved the best performance on small instances due to improved local exploitation, whereas the original DA-VND significantly outperformed all variants on larger instances. Further analysis showed that this advantage was largely due to DA-VND’s much higher iteration speed. To verify this, all algorithms were re-evaluated under a fixed 1500-iteration setting. The performance gap between DA-VND and the adaptive variants narrowed substantially, indicating that L2 and L3 are competitive when given equal search effort. Overall, adaptive mechanisms improve accuracy on small to medium problems, while DA-VND remains more effective for large-scale IPSVR due to its stronger exploratory capability and computational efficiency.