Marcella Samà

A variable neighbourhood search for fast train scheduling and routing during disturbed railway traffic situations

This paper focuses on the development of metaheuristic algorithms for the real-time traffic management problem of scheduling and routing trains in complex and busy railway networks. This key optimization problem can be formulated as a mixed integer linear program. However, since the problem is strongly NP-hard, heuristic algorithms are typically adopted in practice to compute good quality solutions in a short computation time. This paper presents a number of algorithmic improvements implemented in the AGLIBRARY optimization solver in order to improve the possibility of finding good quality solutions quickly. The optimization solver manages trains at the microscopic level of block sections and at a precision of seconds. The solver outcome is a detailed conflict-free train schedule, being able to avoid deadlock situations and to minimize train delays. The proposed algorithmic framework starts from a good initial solution for the train scheduling problem with fixed routes, obtained via a truncated branch-and-bound algorithm. Variable neighbourhood search or tabu search algorithms are then applied to improve the solution by re-routing some trains. The neighbourhood of a solution is characterized by the set of candidate trains to be re-routed and the available routes. Computational experiments are performed on railway networks from different countries and various sources of disturbance. The new algorithms often outperform a state-of-the-art tabu search algorithm and a commercial solver in terms of reduced computation times and/or train delays. HighlightsVariable neighbourhood search algorithms are proposed for efficient railway traffic control.New neighbourhood search strategies for real-time train re-routing are developed.Practical railway test cases from various European countries are investigated.The new algorithms compute good quality solutions in a short computation time.The new algorithms often outperform a state-of-the-art tabu search algorithm.

Air traffic optimization models for aircraft delay and travel time minimization in terminal control areas

This paper addresses the real-time aircraft routing and scheduling problem at a busy terminal control area (TCA) in case of traffic congestion. The problem of effectively managing TCA operations is particularly challenging, since there is a continuous growth of traffic demand and the TCAs are becoming the bottleneck of the entire air traffic control system. The resulting increase in airport congestion, economic and environmental penalties can be measured in terms of several performance indicators, including take-off and landing aircraft delays and energy consumption. This work addresses this problem via the development of mixed-integer linear programming formulations that incorporate the safety rules with high modeling precision and objective functions of practical interest based on the minimization of the total travel time and the largest delay due to potential aircraft conflicts. Computational experiments are performed on real-world data from Roma Fiumicino, the largest airport in Italy in terms of passenger demand. Traffic disturbances are generated by simulating sets of random landing/take-off aircraft delays. Near-optimal solutions of practical-size instances are computed in a short time via a commercial solver. The computational analysis enables the selection of those solutions offering the best compromise among the different objectives.

A multi-criteria decision support methodology for real-time train scheduling

This work addresses the real-time optimization of train scheduling decisions at a complex railway network during congested traffic situations. The problem of effectively managing train operations is particularly challenging, since it is necessary to incorporate the safety regulations into the optimization model and to consider key performance indicators. This paper deals with the development of a multi-criteria decision support methodology to help dispatchers in taking more informed decisions when dealing with real-time disturbances. Optimal train scheduling solutions are computed with high level precision in the modeling of the safety regulations and with consideration of state-of-the-art performance indicators. Mixed-integer linear programming formulations are proposed and solved via a commercial solver. For each problem instance, an iterative method is proposed to establish an efficient-inefficient classification of the best solutions provided by the formulations via a well-established non-parametric benchmarking technique: data envelopment analysis. Based on this classification, inefficient formulations are improved by the generation of additional linear constraints. Computational experiments are performed for practical-size instances from a Dutch railway network with mixed traffic and several disturbances. The method converges after a limited number of iterations, and returns a set of efficient solutions and the relative formulations.

Rescheduling Railway Traffic Taking into Account Minimization of Passengers’ Discomfort

Optimization models for railway traffic rescheduling in the last decade tend to develop along two main streams. One the one hand, train scheduling models strives to incorporate any relevant detail of the railway infrastructure having an impact on the feasibility and quality of the solutions from the viewpoint of operations managers. On the other hand, delay management models focus on the impact of rescheduling decisions on the quality of service perceived by the passengers. Models in the first stream are mainly microscopic, while models in the second stream are mainly macroscopic. This paper aims at merging these two streams of research by developing microscopic passenger-centric models, solution algorithms and lower bounds. Fast iterative algorithms are proposed, based on a decomposition of the problem and on the exact resolution of the sub-problems. A new lower bound is proposed, consisting of the resolution of a set of min-cost flow problems with activation constraints. Computational experiments, based on a real-world Dutch railway network, show that good quality solutions and lower bounds can be found within a limited computation time.

Metaheuristics for Real-Time Near-Optimal Train Scheduling and Routing

This paper focuses on metaheuristic algorithms for the real-time traffic management problem of scheduling and routing trains in a complex and busy railway network. Since the problem is strongly NP-hard, heuristic algorithms are developed to compute good quality solutions in a short computation time. In this work, a number of algorithmic improvements are implemented in the AGLIBRARY optimization solver, that manages trains at the microscopic level of block sections and block signals and at a precision of seconds. The solver outcome is a detailed conflict-free train schedule, being able to avoid deadlocks and to minimize train delays. The proposed algorithmic framework starts from a good initial solution for the train scheduling problem with fixed routes, obtained via a truncated branch-and-bound algorithm. Variable neighbourhood search and tabu search metaheuristics are then applied to improve the solution by re-routing some trains. Computational experiments are performed on a UK railway network with dense traffic in order to compare the two types of studied metaheuristics.

Optimizing hybrid operations at large-scale automated container terminals

This work tackles the problem of controlling operations at an automated container terminal. In the context of large supply chains, there is a growing trend for increasing productivity and economic efficiency. New models and algorithms are provided for scheduling and routing equipment that is moving containers in a quay area, loading/unloading ships, transporting them via Automated Guided Vehicles (AGVs) to Automated Stacking Cranes (ASCs), organizing them in stacks. In contrast with the majority of the approaches in the related literature, this work tackles two dynamics of the system, a discrete dynamic, characteristic of the maximization of operations efficiency, by assigning the best AGV and operation time to a set of containers, and a continuous dynamic of the AGV that moves in a geographically limited area. As an assumption, AGVs can follow free range trajectories that minimize the error of the target time and increase the responsiveness of the system. A novel solution framework is proposed and some computational result are reported to show the feasibility of the proposed approach.

Comparing Centralized and Rolling Horizon Approaches for Optimal Aircraft Traffic Control in Terminal Areas

This work addresses the real-time problem of managing takeoff and landing operations during traffic disturbances at a busy terminal control area (TCA). An important objective of traffic controllers is the minimization of delay propagation, which may reduce the aircraft travel time and the energy consumption. To improve the effectiveness of air traffic monitoring and control in a busy TCA, this paper presents an advanced optimization-based decision support system and compares centralized and rolling horizon approaches. The possible aircraft conflict detection and resolution actions were viewed as aircraft timing and routing decisions. The problem was modeled by an alternative graph formulation (i.e., a detailed model of air traffic flows in the TCA) and solved by scheduling and rerouting algorithms. The paper also proposes a new mixed-integer linear programming (MILP) formulation to compute (near) optimal scheduling and routing solutions. The paper compares the first-in, first-out (FIFO) rule, used as a surrogate for the dispatchers’ behavior, a truncated branch and bound algorithm for aircraft scheduling with fixed routes, a tabu search algorithm for combined aircraft scheduling and rerouting, and the MILP formulation solved via a commercial solver. Computational experiments are presented for practical-sized instances from Milano Malpensa Airport in Milan, Italy. Disturbed traffic situations were generated by simulating various sets of delayed landing and departing aircraft. A detailed analysis of the experimental results demonstrates that the solutions produced by the optimization algorithms are of a remarkably better quality compared with the FIFO rule, in relation to delay and travel time minimization.

Optimal aircraft scheduling and flight trajectory in terminal control areas

This paper deals with the Terminal Control Area Aircraft Scheduling Problem and the Aircraft Trajectory Optimization Problem for landing operations in a busy terminal control area. The first problem requires to compute a conflict-free schedule for all aircraft minimizing the overall aircraft delays, while the second deals with the computation of a landing trajectory for each aircraft which minimizes either the travel time or the fuel consumption. Due to the lack of integrated solving approaches considering both problems, we propose a framework for the lexicographic optimization of the two problems. The computational experiments, performed on Milano Malpensa airport instances, show the existence of performance gaps between the optimized indicators of the two problems when different lexicographic optimization approaches are considered.

Ant colony optimization for train routing selection: Operational vs tactical application

Railway traffic is often perturbed by unexpected events. To effectively cope with these events, the real-time railway traffic management problem (rtRTMP) seeks for train routing and scheduling methods which minimize delay propagation. The size of rtRTMP instances is strongly affected by the number of routing alternatives available to each train. Performing an initial selection on which routings to use during the solution process is a common practice to simplify the problem. The train routing selection problem (TRSP) reduces the number of routings available for each train to be used in the rtRTMP. This paper describes an Ant Colony Optimization (ACO) algorithm for the TRSP, and analyses its application in two different contexts: at tactical level, based on historical data and with abundant computation time, or at operational level, based on the specific traffic state and with a limited computation time. Promising results are obtained on the instances of the Lille terminal station area, in France, based on realistic traffic disturbance scenarios.

Microscopic delay management: Minimizing train delays and passenger travel times during real-time railway traffic control

Optimization models for railway traffic rescheduling in the last decade tend to develop along two main streams. On the one hand, train scheduling models strive to incorporate any relevant detail of the railway infrastructure having an impact on the feasibility and quality of the solutions from the viewpoint of infrastructure managers. On the other hand, delay management models focus on the impact of rescheduling decisions on the quality of service perceived by the passengers, and in the interest of the train operating company. Models in the first stream are mainly microscopic, while models in the second stream are mainly macroscopic. This paper aims at merging these two streams of research by developing microscopic delay management approaches. A variety of solution algorithms are proposed, that compute a solution to the studied problem; the obtained solutions correspond to Nash equilibria of the strategic interaction of the players (train operating company and infrastructure manager). Computational results based on a real-world Dutch railway network quantify the trade-off between the minimization of train delays and passenger travel times, and show that good compromise solutions can be found within a limited computation time.