In this paper, we consider the problem of scheduling aircraft (plane) landings at an airport. This problem is one of deciding a landing time for each plane such that each plane lands within a predetermined time window and that separation criteria between the landing of a plane and the landing of all successive planes are respected. We present a mixed-integer zero–one formulation of the problem for the single runway case and extend it to the multiple runway case. We strengthen the linear programming relaxations of these formulations by introducing additional constraints. Throughout, we discuss how our formulations can be used to model a number of issues (choice of objective function, precedence restrictions, restricting the number of landings in a given time period, runway workload balancing) commonly encountered in practice. The problem is solved optimally using linear programming-based tree search. We also present an effective heuristic algorithm for the problem. Computational results for both the heuristic and the optimal algorithm are presented for a number of test problems involving up to 50 planes and four runways. 180 In this paper, we consider the problem of scheduling

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This report documents the results of our efforts and the four unifying metrics we recommend for the AATT program. They are as follows:

This report describes an integrated model of air traffic management (ATM) tools under development in two National Aeronautics and Space Administration (NASA) programs---Terminal Area Productivity (TAP) and Advanced Air Transport Technologies (AATT). The model is made by adjusting parameters of LMINET, a queuing network model of the National Airspace System (NAS), which the Logistics Management Institute (LMI) developed for NASA. Operating LMINET with models of various combinations of TAP and AATT will give quantitative information about the effects of the tools on operations of the NAS. An extension of economic models developed by the Institute for NASA maps the technologies' impacts on NAS operations into cross-comparable benefits estimates for technologies and sets of technologies. An application of the Aviation Systems Analysis Capability (ASAC) Air Carrier Investment Model (ACIM), developed for NASA by the Institute, gives estimates of the ways in which the NASA tools impact NAS throughput, as measured by revenue passenger miles (RPMs), enplanements, and operations. Following this overview chapter, Chapter 2 describes LMINET and its constituent models in some detail. This information will help readers unfamiliar with LMINET to understand ATM models made with LMINET parameters. For completeness, we have included in this report material from three other reports and an LMI white paper, all of which were prepared for NASA by the Institute. [1,2,3,4] Those familiar with LMINET's components need only consider the material in the sections "Input-Stream Effects" and "Taxi-Delay Queues" in Chapter 2. The first describes a new LMINET parameter, developed to account for the fact that terminal radar approach control (TRACON) controllers may present airport controllers with arri...