The United States National Airspace System is currently operating at a level close to its maximum potential. The workload on the system, however, is only going to increase with the influx of unmanned aerial vehicles and soon, commercial space transportation systems. The traffic flow management is currently managed based on the flight path requests by the airline operators; while the minimum separation assurance between flights is handled strategically by air traffic control personnel. A more tactical approach would be to plan for a longer time horizon which is non-trivial given the uncertainties in the airspace due to weather. In this work, we consider a simplified model of the airspace as a grid of sectors and the uncertainties in the airspace are modeled as blocked sectors. In the modeled airspace with uncertainties, we schedule multiple flights using a dynamic shortest path algorithm. A novel cost function based on potential energy fields is proposed for use in the path planning algorithm to handle blocked sectors. A priority-based contention resolution scheme is proposed to extend the solution to multiple flights. We then demonstrate the proposed framework using a simulated test case.