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

A State Transition MIP Formulation for the Unit Commitment Problem

In this paper, we present the state-transition formulation for the unit commitment 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

On the statistical description of the inbound air traffic over Heathrow airport

We present a model to describe the inbound air traffic over a congested hub. We show that this model gives a very accurate description of the traffic by the comparison of our theoretical distribution of the queue with the actual distribution observed over Heathrow airport. We discuss also the robustness of our model

Multimode extensions of combinatorial optimization problems

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)

Predictive modeling of inbound demand at major European airports with Poisson and Pre-Scheduled Random Arrivals

This paper presents an exhaustive study of the arrivals process at eight major European airports. Using inbound traffic data, we define, compare, and contrast a data-driven in-homogeneous Poisson and Pre-Scheduled Random Arrivals (PSRA) point process with respect to their ability to predict future demand. As part of this analysis, we show the weaknesses and difficulties of using a non-homogeneous Poisson process to model the arrivals stream. On the other hand, our novel and simple data-driven (PSRA) model captures and predicts the main properties of the typical arrivals stream with good accuracy. These results have important implication for the modeling and simulation-based analyses of inbound traffic and can improve the use of available capacity, thus reducing air traffic delays. In a nutshell, the results lead to the conclusion that, in the European context, the (PSRA) model provides more accurate predictions

Freight Service Design for the Italian Railways Company

In this paper, we present a mathematical model to design the service network, that is the set of origin-destination connections. The resulting model considers both full and empty freight car movements, and takes into account handling costs. More specifically, the model suggests the services to provide, as well as the number of trains and the number and type of cars traveling on each connection. Quality of service, which is measured as total travel time, is established by minimizing the waiting time of cars at intermediate stations. Our approach yields a multi-commodity network design problem with concave arc cost functions. To solve this problem, we implement a tabu search procedure which adopts ``perturbing\u27\u27 mechanisms to force the algorithm to explore a larger portion of the feasible region. Computational results on realistic instances show a significant improvement over current practice

The airline long-haul fleet planning problem:The case of TAP service to/from Brazil

In this paper, we study a strategic fleet planning problem faced by TAP, the Portuguese legacy carrier. The study is intended to shed light on the aircraft models to select, as well as on the mix of aircraft to purchase (or financially lease) and the aircraft to operationally lease in order to cope with the forecasted passenger demand between Lisbon and Brazil in the year 2020. The approach we developed for addressing this problem is based on an optimization model that can be cast in the class of two-stage stochastic integer programs. In our opinion, the proposed approach can be a valuable alternative or complement to the methods currently used in practice. The results of the study provide clear insights on how TAP should renovate its fleet, obviously depending on the available resources. The leasing of aircraft is an option that should definitely be taken into consideration by TAP, since it allows the carrier to deal with demand uncertainty without investing large amount of resources in the purchase of new aircraft

Data analytics for trajectory selection and preference-model extrapolation in the European airspace

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

Solving Optimal Transmission Switching Problem via DC Power Flow Approximation

The objective of the Optimal Transmission Switching (OTS) problem is to identify a topology of the power grid that minimizes the total energy production costs, while satisfying the operational and physical constraints of the power system. The problem is formulated as a non-convex mixed-integer nonlinear program, which poses extraordinary computational challenges. A common approach to solve the OTS problem is to replace its non-convex non-linear constraints with some linear constraints that turn the original problem into a mixed-integer linear programming, named DC OTS. Although there is plenty of work studying solution methods for the DC OTS in the literature, whether and how solutions of the DC OTS are actually useful for the original OTS problem is often overlooked. In this work, we investigate to what extent DC OTS solutions can be used as a fast heuristic to compute feasible solutions for the original OTS problem. Computational experiments on a set of PGLib benchmark instances highlighted that the optimal solution of the DC OTS is rarely feasible for the original OTS problem, which is consistent with the literature. However, we also find that easy-to-implement modifications of the solution procedure help to address this issue. Therefore, we suggest using DC OTS solutions as a complementary option to state-of-the-art heuristics to compute feasible solutions of the original OTS problem