Guglielmo Lulli

An Integer Optimization Approach to Large-Scale Air Traffic Flow Management

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.

The European Air Traffic Flow Management Problem

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 and 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.

A new hierarchical architecture for Air Traffic Management: Optimisation of airway capacity in a Free Flight scenario

We describe a new two-level hierarchical architecture for air traffic management problems with corresponding mathematical models. The first level represents the air route network, and its solutions provide the air traffic flows on each arc of the network. This level interacts with the second one, which represents the single airway and its own air traffic flows. This latter model allows us to assign the optimal air traffic route to each aircraft and to optimise the airways capacity. Furthermore, for the airway optimisation model we have also carried out a computational analysis, providing both exact and heuristic solutions, for problem instances based on real data. These are obtained with the Cplex solver exploiting the mixed integer mathematical formulation and with a proposed heuristic algorithm for problems of larger size, respectively. The heuristic solutions obtained are within a maximum gap of 13% from the LP relaxation.

Design of quality-based composite web services

One of the key factors for successful SOC-based systems is the ability to assure the achievement of Quality of Services. The knowledge and the enforcement of the Quality of Services allows for the definition of agreements that are the basis for any business process. In this paper we discuss a method for the evaluation of qualities associated with services. This method is based on a set of quality evaluation rules that state the relations between Web services quality dimensions and process structure. The method is part of a design methodology that addresses quality issues along the service life-cycle. A case study in the e-placement field is presented to illustrate a practical use of the approach.

Dynamic resource allocation : a flexible and tractable modeling framework

This paper presents a binary optimization framework for modeling dynamic resource allocation problems. The framework (a) allows modeling flexibility by incorporating different objective functions, alternative sets of resources and fairness controls; (b) is widely applicable in a variety of problems in transportation, services and engineering; and (c) is tractable, i.e., provides near optimal solutions fast for large-scale instances. To justify these assertions, we model and report encouraging computational results on three widely studied problems – the Air Traffic Flow Management, the Aircraft Maintenance Problems and Job Shop Scheduling. Finally, we provide several polyhedral results that offer insights on its effectiveness.

An aggregate stochastic programming model for air traffic flow management

In this paper, we present an aggregate mathematical model for air traffic flow management (ATFM), a problem of great concern both in Europe and in the United States. The model extends previous approaches by simultaneously taking into account three important issues: (i) the model explicitly incorporates uncertainty in the airport capacities; (ii) it also considers the trade-off between airport arrivals and departures, which is a crucial issue in any hub airport; and (iii) it takes into account the interactions between different hubs. The level of aggregation proposed for the mathematical model allows us to solve realistic size instances with a commercial solver on a PC. Moreover it allows us to compute solutions which are perfectly consistent with the Collaborative Decision-Making (CDM) procedure in ATFM, widely adopted in the USA and which is currently receiving a lot of attention in Europe. In fact, the proposed model suggests the number of flights that should be delayed, a decision that belongs to the ATFM Authority, rather than assigning delays to individual aircraft.

Planning activities in a network of logistic platforms with shared resources

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%.

The multimode covering location problem

In this paper we introduce the Multimode Covering Location Problem. This is a generalization of the Maximal Covering Location Problem that consists in locating a given number of facilities of different types with a limitation on the number of facilities sharing the same site.The problem is challenging and intrinsically much harder than its basic version. Nevertheless, it admits a constant factor approximation guarantee, which can be achieved combining two greedy algorithms. To improve the greedy solutions, we have developed a Variable Neighborhood Search approach, based on an exponential-size neighborhood. This algorithm computes good quality solutions in short computational time. The viability of the approach here proposed is also corroborated by a comparison with a Heuristic Concentration algorithm, which is presently the most effective approach to solve large instances of the Maximal Covering Location Problem. HighlightsWe present a multimode generalization of the Maximal Covering Location Problem.We propose two greedy approximation algorithms for the new problem.We develop a Variable Neighborhood Search approach, whose local search procedure is based on Very Large Scale Neighborhood Search.We compare this approach with a basic VNS approach based on a polynomial sized neighborhood and with a Heuristic Concentration approach, which exploit general purpose exact solvers.

A multi-period TSP with stochastic regular and urgent demands

In this paper, we study the multi-period TSP problem with stochastic urgent and regular demands. Urgent demands have to be satisfied immediately while regular demands can be satisfied either immediately or the day after. Demands appear stochastically at nodes. The objective is to minimize the average long-run delivery costs, knowing the probabilities governing the demands at nodes. The problem is cast as a Markov Process with Costs and, at least in principle, can be solved using an approach originally proposed by Howard [R.A. Howard, Dynamic Programming and Markov Processes, MIT, Cambridge, USA, 1960] for Markov Processes with Rewards. However, the number of states of the Markov Process grows exponentially with the number of nodes in the network which pose a limit on the dimension of the instances that are computationally tractable. We suggest a second Markov approach considering a system (Aggregate Model) whose number of states grows only polynomially with the number of nodes in the network. The important relations between the optimal solutions of the original and the aggregate models will be discussed. Finally, we also propose a hybrid procedure which combines both models. The viability of the proposed methodology is shown by applying the procedure to a numerical example.

Service Network Design for Freight Railway Transportation: The Italian Case

In this paper, we present a case study on freight railway transportation in Italy, which is a by-product of research collaboration with a major Italian railway company. We highlight the main features of the Italian reality and propose a customized mathematical model to design the service network, that is, the set of origin-destination connections. More specifically, the model suggests the services to provide, the number of trains travelling on each connection, the number of cars and their type. We consider both full and empty freight car movements and take handling costs into account. All decisions are taken in order to minimize the total costs. The quality of service is guaranteed by satisfying all the transportation demand and by implicitly minimizing the waiting time of cars at intermediate railway stations. Our approach yields to a multi-commodity network design problem with a concave cost function. To solve this problem, we implement a specialized tabu search procedure. Computational results on realistic instances show a significant improvement over current practice.