Airport runway scheduling

Airport runway optimization is an ongoing challenge for air traffic controllers. Since demand for air-transportation is predicted to increase, there is a need to realize additional take-off and landing slots through better runway scheduling. In this paper, we review the techniques and tools of operational research and management science that are used for scheduling aircraft landings and take-offs. The main solution techniques include dynamic programming, branch and bound, heuristics and meta-heuristics.     

Airport runway scheduling

Airport runway optimization is an ongoing challenge for air traffic controllers. Since demand for air-transportation is predicted to increase, there is a need to realize additional take-off and landing slots through better runway scheduling. In this paper, we review the techniques and tools of operational research and management science that are used for scheduling aircraft landings and take-offs. The main solution techniques include dynamic programming, branch and bound, heuristics and meta-heuristics.     

Numerical optimization of a conical cavity as a radiation-focused concentrator

Conical enclosures rely on the conical cavity and can be used as radiation concentrators. Two circular cross-section baffles were used to improve the heat transfer of this geometry. By changing the rigid fins to porous, it could improve the heat transfer. Al₂O₃/water nanofluid was also employed to enhance the heat transfer performance of the cavity. For this purpose, numerical analysis of three-dimensional natural convection heat transfer was performed in a conical cavity with two types of fins. The best combination of fins arrangement for the next step was selected using the differential evolutionary optimization method (D.E). In this case study, a new combination of laminar and turbulence methods was employed for the first time to increase the accuracy of the natural convection solution. This combination is based on the laminar solution by suppressing the perturbation parameter in the turbulence method which led to more accurate results. The analysis results showed that a conical cavity with optimized fin geometry can lead to a 23% increase in Nu. The best porosity for the inner fin was calculated 40% in the case of constant porosity. Ascending porosity along the fin, whose increase was more intense near the base and slower near the cone's tip, was the best variable porosity for the inner fin.