Search

In this paper, we present a novel data-driven optimization approach for trajectory-based air traffic flow management (ATFM). A key aspect of the proposed approach is the inclusion of airspace users' trajectory preferences, which are computed from traffic data by combining clustering and classification techniques. Machine learning is also used to extract consistent trajectory options, whereas optimization is applied to resolve demand-capacity imbalances by means of a mathematical programming model that judiciously assigns a feasible four-dimensional trajectory and a possible ground delay to each flight. The methodology has been tested on instances extracted from the Eurocontrol data repository. With more than 32,000 flights considered, we solve the largest instances of the ATFM problem available in the literature in short computational times that are reasonable from the practical point of view. As a by-product, we highlight the trade-off between preferences and delays as well as the potential benefits. Indeed, computing efficient solutions to the problem facilitates a consensus between the network manager and airspace users. In view of the level of accuracy of the solutions and the excellent computational performance, we are optimistic that the proposed approach can make a significant contribution to the development of the next generation of air traffic flow management tools. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.09.012 Byline: Guglielmo Lulli (a), Suvrajeet Sen (b) Keywords: Heuristic algorithm; Successive approximation; Stochastic integer programming Abstract: In this paper, we propose a successive approximation heuristic which solves large stochastic mixed-integer programming problem with complete fixed recourse. We refer to this method as the Scenario Updating Method, since it solves the problem by considering only a subset of scenarios which is updated at each iteration. Only those scenarios which imply a significant change in the objective function are added. The algorithm is terminated when no such scenarios are available to enter in the current scenario subtree. Several rules to select scenarios are discussed. Bounds on heuristic solutions are computed by relaxing some of the non-anticipativity constraints. The proposed procedure is tested on a multistage stochastic batch-sizing problem. Author Affiliation: (a) Dipartimento di Matematica Pura ed Applicata, Universita di Padova, 35123 Padova, Italy (b) Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ 85721 USA

The research develops a two-level hierarchical architecture for air traffic management problems with related mathematical models. The research further includes a computational analysis that results in both exact and heuristic solutions.

The article focuses on air transportation industry operates under pervasive long- and short-term uncertainty. Topics inlcude the U.S. Department of Transportation's Bureau of Transportation Statistics has reported in 2019, the passenger flights in the U.S. has delayed by more than fifteen minutes were either cancelled or diverted, and growing interest in explicitly considering uncertainty when developing planning for air transport operations has reflected in the academic literature.

In this paper, we present the state-transition formulation for the unit commitment (UC) problem. This formulation uses new decision variables that capture the state transitions of the generators, instead of their on/off statuses. We show that this new approach produces a formulation which naturally includes valid inequalities, commonly used to strengthen other formulations. We demonstrate the performance of the state-transition formulation and observe that it leads to improved solution times especially in longer time-horizon instances. As an important consequence, the new formulation allows us to solve realistic instances in less than 12 minutes on an ordinary desktop PC, leading to a speed-up of a factor of almost two, in comparison to the nearest contender. Finally, we demonstrate the value of considering longer planning horizons in UC problems. [ABSTRACT FROM PUBLISHER]

The high cost of flight delays for airlines has motivated scientific research in air traffic flow management (ATFM). The majority of ATFM models in the literature are deterministic and do not take into account stochastic factors such as weather. In this paper, new stochastic programming models for ATFM are proposed. The models include as tactical control options: ground holding, airborne holding and rerouting. To solve the models, a new heuristic method that takes advantage of the problem structure is derived and illustrated. Computational results show that the heuristic method provides practical computation times. Furthermore, the value of the stochastic solution is up to 14% for cases where adverse weather affects a significant part of the network. This implies that using the proposed approach to make air traffic flow decisions can lead to tangible monetary benefits for airlines. [ABSTRACT FROM AUTHOR]

We review some complexity results and present a viable heuristic approach based on the Variable Neighborhood Search (VNS) framework for multimode extension of combinatorial optimization problems, such as the the Set Covering Problem (SCP) and the Covering Location Problem (CLP). [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a methodology for making sense of large, multiple time-series data sets arising in expression analysis. Specifically, we present a mathematical model to release a reduced and coherent regulatory system given a putative regulatory network. We give two equivalent formulations of the problem and prove that the problem is NP-complete. For solving large scale instances we implemented an Ant Colony Optimization procedure. A computational analysis on randomly generated test instances validates the proposed algorithm and the computations on real data concerning Saccharomyces cerevisiae show the practicability of the proposed methodology. [Copyright &y& Elsevier]