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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]

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]

This paper introduces the multi-mode set covering problem, which consists of a plurality of set covering problems linked by cardinality constraints. We propose a variable neighborhood search algorithm and a greedy randomized adaptive search procedure based on a common local search routine. This routine applies a penalized relaxation of the covering constraints, tuned by self-adapting parameters, and visits a sequence of neighborhoods in a nested strategy. We compare the two heuristics with each other and with a time-limited run of a general-purpose integer linear programming solver, on a benchmark set of instances with heterogeneous structure. Both heuristics outperform the solver, though with interesting differences with respect to the various classes of instances. In particular, the variable neighborhood search algorithm proves more effective and less dependent on the specific features of the instances. [ABSTRACT FROM AUTHOR]

This paper presents a new integer programming (IP) model for large-scale instances of the air traffic flow management (ATFM) problem. The model covers all the phases of each flight--i.e., takeoff, en route cruising, and landing--and solves for an optimal combination of flow management actions, including ground-holding, rerouting, speed control, and airborne holding on a flight-by-flight basis. A distinguishing feature of the model is that it allows for rerouting decisions. This is achieved through the imposition of sets of "local" conditions that make it possible to represent rerouting options in a compact way by only introducing some new constraints. Moreover, three classes of valid inequalities are incorporated into the model to strengthen the polyhedral structure of the underlying relaxation. Computational times are short and reasonable for practical application on problem instances of size comparable to that of the entire U.S. air traffic management system. Thus, the proposed model has the potential of serving as the main engine for the preliminary identification, on a daily basis, of promising air traffic flow management interventions on a national scale in the United States or on a continental scale in Europe. [ABSTRACT FROM AUTHOR]

Air traffic flow management in Europe has to deal as much with capacity constraints in en route airspace as with the more usual capacity constraints at airports. The en route sector capacity constraints, in turn, generate complex interactions among traffic flows. We present a deterministic optimization model for the European air traffic flow management (ATFM) problem. The model designs flow management strategies involving combinations of ground and airborne holding. The paper illustrates the complex nature of European (EU) ATFM solutions, the benefits that can be obtained by purposely assigning airborne holding delays to some flights, and the issues of equity that arise as a result of the interactions among traffic flows. In particular, we show that, in certain circumstances, it is better, in terms of total delay arid delay cost, to assign to a flight a more expensive airborne holding delay than a ground delay. We also show that in the EU ATFM context, fundamental conflicts may often arise between the objectives of efficiency and equity (or "fairness"). This finding may have profound implications for the possibility of developing a "collaborative decision-making" environment for air traffic flow management in Europe. [ABSTRACT FROM AUTHOR]

This paper has been motivated by the study of a real application, the transshipment container terminal of Gioia Tauro in Italy. The activities in a container terminal concern with the movement of containers from/to mother vessels and feeders and with the handling and storage of containers in the yard. For such type of applications both operational (e.g., scheduling) and tactical (e.g., planning) models, currently available in the literature, are not useful in terms of operations management and resources optimization. Indeed, the former models are too detailed for the complexity of the systems, while the latter are not able to capture the operational constraints in representing those activities which limit the nominal capacity. Herein, the container terminal, or more in general a service or production system, is represented as a network of complex substructures or platforms. The idea is to formalize the concept of platform capacity, which is used to represent the operational aspects of the container terminal in a mathematical model for the tactical planning. The problem, which consists in finding an allocation of resources in each platform in order to minimize the total delay on the overall network and on the time horizon, is modelled by a mathematical programming formulation for which we carry out a computational analysis using CPLEX-MIP solver. Moreover, we present a dynamic programming based heuristic to solve larger instances in short computational time. On all but one of the smaller instances, the heuristic solutions are also optimal. On the larger instances, the maximum gap, i.e. the percentage deviation, between the heuristic solutions and the best solutions computed by CPLEX-MIP within the time limit of 3600 s, has been 6.3%. [ABSTRACT FROM AUTHOR]

In this paper, we present a branch-and-price method to solve special structured multistage stochastic integer programming problems. We validate our method on two different versions of a multistage stochastic batch-sizing problem (SBSP). One version adopts a recourse formulation, and the other is based on probabilistic constraints. Our algorithmic approach is applicable to both formulations. Our computational results suggest that both classes of problems can be solved using relatively few nodes of a branch-and-price tree. The success of our approach calls for extensions in methodology as well as applications. [ABSTRACT FROM AUTHOR]