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In this paper, we compare two trajectory-centered Air Traffic Flow Management (ATFM) architectures that assign a trajectory and a departure time slot to each flight. The main feature of these architectures is the use of trajectories that are feasible from the operational point of view and are aligned as much as possible with airspace users’ preferences. The first architecture is inspired by the first-come-first-served principle and can be seen as a proxy of the approach currently used in practice. The second one—herein named “preference-aware ATFM architecture”—considers alternative trajectory options and explicitly takes the related preference scores into account: its main objective is to provide better trade-offs between user-preferred trajectories and system efficiency. The analysis herein carried out highlights the benefits of the latter architecture in terms of both system efficiency and satisfaction of airspace users’ preferences, thus paving the way to its potential use as a tool for the ATFM collaborative decision-making process.

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.

Representing airspace users’ preferences in Air Traffic Flow Management (ATFM) mathematical models is becoming of high relevance. ATFM models aim to reduce congestion (en-route and at both departure and destination airports) and maximize the Air Traffic Management (ATM) system efficiency by determining the best trajectory for each aircraft. In this framework, the a-priori selection of possible alternative trajectories for each flight plays a crucial role. In this work, we analyze initial trajectories queried from Eurocontrol DDR2 data source. Clustering trajectories yields groups that are homogeneous with respect to known (geometry of the trajectory, speed) and partially known or unknown factors (en-route charges, fuel consumption, weather, etc.). Associations between grouped trajectories and potential choice-determinants are successively explored and evaluated, and the predictive value of the determinants is finally validated. For a given origin-destination pair, this ultimately leads to determining a set of flight trajectories and information on related airspace users’ preferences.

The delay management problem arises in public transportation networks, often characterized by the necessity of connections between different vehicles. The attractiveness of public transportation networks is strongly related to the reliability of connections, which can be missed when delays or other unpredictable events occur. Given a single initial delay at one node of the network, the delay management problem is to determine which vehicles have to wait for the delayed ones, with the aim of minimizing the dissatisfaction of the passengers. In this paper, we present strengthened mixed integer linear programming formulations and new families of valid inequalities. The implementation of branch-and-cut methods and tests on a benchmark of instances taken from real networks show the potential of the proposed formulations and cuts., SCOPUS: ar.j, info:eu-repo/semantics/published

The knowledge of nucleotides chains that compose the double DNA chain of an individual has a relevant role in detecting diseases and studying populations. However, determining experimentally the single nucleotides chains that, paired, form a certain portion of the DNA is expensive and time-consuming. Mathematical programming approaches have been proposed instead, e.g. formulating the Haplotype Inference by Pure Parsimony problem (HIPP). Abstractly, we are given a set of genotypes (strings over a ternary alphabet {0,1,2}) and we want to determine the smallest set of haplotypes (binary strings over the set {0,1}) so that each genotype can be "generated" by some pair of haplotypes, meaning that they are compatible with the genotype and can fully explain its structure. A polynomial-sized Integer Programming model was proposed by Catanzaro, Godi and Labbé (2010), which is highly efficient but hardly scalable to instances with a large number of genotypes. In order to deal with larger instances, we propose a new model involving an exponential number of variables to be solved via column generation, where variables are dynamically introduced into the model by iteratively solving a pricing problem. We compared different ways of solving the pricing problem, based on integer programming, smart enumeration and local search heuristic. The efficiency of the approach is improved by stabilization and by a heuristic to provide a good initial solution. Results show that, with respect to the linear relaxations of both the polynomial and exponential-size models, our approach yields a tighter formulation and outperforms in both efficiency and effectiveness the previous model for instances with a large number of genotypes.

Passengers travelling in public transportation networks often have to use different lines to cover the trip from their origin to the desired destination. As a consequence, the reliability of connections between vehicles is a key issue for the attractiveness of the intermodal transportation network and it is strongly affected by some unpredictable events like breakdowns or vehicle delays. In such cases, a decision is required to determine if the connected vehicles should wait for the delayed ones or keep their schedule. The delay management problem (DMP) consists in defining the wait/depart policy which minimizes the total delay on the network. In this work, we present two equivalent mixed integer linear programming models for the DMP with a single initial delay, able to reduce the number of variables with respect to the formulations proposed by the literature. The two models are solved by a branch and cut procedure and by a constraint generation approach respectively, and preliminary computational results are presented. © 2006 Elsevier B.V. All rights reserved., SCOPUS: ar.j, info:eu-repo/semantics/published

info:eu-repo/semantics/published