可以在Airflow DAG中设置参数max_active_runs和retries来控制任务的并发和重试次数。同时，可以使用Operator的set_upstream和set_downstream方法来控制任务之间的依赖关系，从而优化任务的执行顺序和并发度。代码示例如下：

from airflow import DAG
from datetime import datetime, timedelta
from airflow.operators.python_operator import PythonOperator

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2021, 1, 1),
    'retries': 3,
    'retry_delay': timedelta(minutes=5),
    'max_active_runs': 1
}

dag = DAG(
    'example_dag',
    default_args=default_args,
    catchup=False,
    schedule_interval=None
)

def long_running_task():
    # 长时间运行的任务
    pass

with dag:
    task_1 = PythonOperator(
        task_id='task_1',
        python_callable=long_running_task
    )

    task_2 = PythonOperator(
        task_id='task_2',
        python_callable=long_running_task
    )

    task_3 = PythonOperator(
        task_id='task_3',
        python_callable=long_running_task
    )

    task_1 >> task_2
    task_1 >> task_3