Abstract

We propose a method based on multi-objective combinatorial optimization to perform interdomain traffic engineering with minimal BGP configurations. Our method relies on an evolutionary algorithin that tries to minimize an objective function by finding the successive BGP filters to be applied on the BGP routes. We study the impact of the number of providers, the traffic aggregation in the AS-level topology and the objective function on the behavior of our algorithm. Document type: Part of book or chapter of book

Abstract

Our research deals with the problem of the charging scheduling of electric vehicles (EV). The variation in the total power available to load vehicles, user the behaviour constraints and the uncertainties of daily energy demands require an efficient and secure scheduling. We defined five industrial configurations: ACPF (1,2) and ACPV (1a, 1b and 2), each of which corresponds to a set of technical constraints. Studies on formulations, including a conjunctive and a disjunctive, are based on the analysis of the strength of their LP-relaxation. The matrix form of the mathematical formula is composed of a partitioned matrix, which is decomposable by the Dantzig-Wolfe principles. The latter allows us to develop a Branch-and-Price Algorithm for the exact solution of the problem. A deterministic constructive heuristic was then designed for the allocation of the resource, which is very efficient: a quick resolution (less than a second) for a car park with about thirty EVs. Finally, to implement all algorithms in the microprocessor, and to establish a forecasting model and an online scheduling, we have created a stand-alone scheduler, based on the predictive-reactive rescheduling. The research carried out is part of the problems of energy reasoning. They, therefore, can combine with other works, including the smart grid problems; Notre travail de recherche traite de la problématique de l’ordonnancement de recharge des véhicules électriques (VE). La variation de la puissance totale disponible pour charger des véhicules, les contraintes de comportement des utilisateurs et l'incertitude des demandes énergétiques journalières demandent un ordonnancement efficace et sécurisé. Nous avons défini cinq configurations industrielles : ACPF (1,2) et ACPV (1a, 1b et 2) qui correspondent chacune à un ensemble de contraintes techniques. Les études sur les formulations, dont une conjonctive et une disjonctive, reposent sur l’analyse de la force de leurs relaxation-LP. La forme matricielle de la formule mathématique est composée d’une matrice partitionnée, qui est décomposable par le principe de Dantzig-Wolfe. Cette dernière nous permets de développer un algorithme de type Branch-and-Price pour la résolution exacte du problème. Une heuristique constructive déterministe a ensuite été conçue pour l’allocation de la ressource, qui se trouve très efficace : une résolution rapide (moins d’une seconde) pour un parking d’une trentaine VEs. Finalement, pour implémenter tous les algorithmes dans le microprocesseur, et pour établir un modèle prévisionnel et un ordonnancement en temps réel, nous avons créé un planificateur autonome, qui se base sur le réordonnancement prédictif-réactif. Les recherches effectuées font partie des problèmes de raisonnement énergétique. Elles possèdent donc la capacité de se combiner avec d’autres travaux, notamment le problème de smart grid

Abstract

In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth's network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization. C1 [Baskan, Ozgur; Ceylan, Huseyin] Pamukkale Univ, Dept Civil Engn, Fac Engn, TR-20160 Denizli, Turkey. [Ozan, Cenk] Adnan Menderes Univ, Dept Civil Engn, Fac Engn, TR-09100 Aydin, Turkey. Document type: Article

Abstract

International audience; To sustain the rapidly increasing air traffic demand, the future air traffic management system will rely on a concept, called Trajectory-Based Operations (TBO), that will require aircraft to follow an assigned 4D trajectory (time-constrained trajectory) with high precision. TBO involves separating aircraft via strategic (long-term) trajectory deconfliction rather than the currently-practicing tactical (short-term) conflict resolution. In this context, this chapter presents a strategic trajectory planning approach aiming at minimizing the number of conflicts between aircraft trajectories for a given day. The proposed methodology allocates an alternative departure time, a horizontal flight path, and a flight level to each aircraft at a nation-wide scale.In real-life situations, aircraft may arrive at a given position with some uncertainties on its curvilinear abscissa due to external events. To ensure robustness of the strategic trajectory plan, the aircraft arrival time to any given position will be represented here by a probabilistic distribution over its nominal assigned arrival time.The proposed approach optimizes the 4D trajectory of each aircraft so as to minimize the probability of potential conflicts between trajectories. A hybrid-metaheuristic optimization algorithm has been developed to solve this large-scale mixed-variable optimization problem. The algorithm is implemented and tested with real air traffic data taking into account uncertainty over the French airspace for which a conflict-free and robust 4D trajectory plan is produced.

Abstract

Develop new strategies to design and operate the multilateration systems, used for air traffic control operations, in a more efficient way. The design strategies are based on the utilization of metaheuristic optimization techniques and they are intended to found the optimal spatial distribution of the system ground stations, taking into account the most relevant system operation parameters. The strategies to operate the systems are based on the development of new positioning methods which allow solving the problems of uncertainty position and poor accuracy that the current systems can present. The new strategies can be applied to design, deploy and operate the multilateration systems for airport surface surveillance as well as takeoff-landing, approach and enroute control. An important advance in the current knowledge of air traffic control is expected from the development of these strategies, because they solve several deficiencies that have been made clear, by the international scientific community, in the last years.

Abstract

Due to the regulation and laws concerning the emission of greenhouse gasses, carriers are starting to use electric vehicles for last-mile deliveries. The limited battery capacities of these vehicles necessitate visits to recharging stations during delivery tours of industry-typical length, which have to be considered in the route planning to avoid inefficient vehicle routes with long detours. This thesis seeks to propose new heuristic solution methods for Electric Vehicle Routing Problem with Time Windows (E-VRPTW), which incorporates the possibility of recharging at any of the available charging stations to minimize the total travel distance based on the Clarke and Wright (CW) Saving Heuristic. The solution method focuses on the construction of routes according to waiting time of vehicles, determined priority of customers with regard to their earliest starting service time, customers’ demands and etc. Moreover, the recharging rate, vehicle freight capacity, battery capacity, time windows and recharging time are considered to make it close to real-life logistics problems. Numerical tests are performed on newly designed instances by Schneider and performances of proposed methods are discussed. Keywords: Vehicle Routing Problem, Clarke and Wright Saving Heuristic, Green Logistic, Electric Vehicles, Combinatorial Optimization ÖZ : Sera gazlarının salınımı ile ilgili kural ve yasalardan dolayı taşımacılar şehir içi taşımalarını elektrikle çalışan araçlarla yapmaya başlamışlardır. Bu araçların sınırlı olan batarya kapasiteleri, taşıma sırasında zorunlu olarak batarya dolum istasyonlarında şarj edilmeleri gereksinimini doğurmakta ve bu durum da uzun ve verimsiz rotaların önlenmesi için rota planlamasında gözönüne alınmalıdır. Bu tezin amacı, alınan toplam mesafeyi enküçüklemek amacıyla şarjın tüm dolum istasyonlarında mümkün olduğu ve Clarke & Wright tasarruf algoritmasını esas alan Electric Vehicle Routing Problem with Time Windows (E-VRPTW) problemine yeni bir sezgisel çözüm yöntemi önermektir. Önerilen çözüm yöntemi öncelikle araçların bekleme sürelerine, müşterilerin zaman önceliklerine, taleplerine vs. göre rotalar oluşturmaya odaklanmaktadır. Problemin lojistik olarak daha da gerçekçi olmasını sağlamak üzere araçların taşıma kapasiteleri, batarya kapasiteleri, zaman aralıkları, şarj zamanları ve hızları da gözönüne alınmıştır. Schneider’in değerleri üzerinden sayısal deneyler de yapılmış ve önerilen yöntemlerin performansları da tartışılmıştır. Anahtar Kelimeler: Araç rotalama sorunu, Clarke ve Wright tasarruf sezgiseli, yeşil lojistik, elektrikli araçlar, birleşi eniyilemesi Master of Science in Industrial Engineering. Thesis (M.S.)--Eastern Mediterranean University, Faculty of Engineering, Dept. of Industrial Engineering, 2014. Co-Supervisor: Assoc. Prof. Dr. Bülent Çatay, Supervisor: Assist. Prof. Dr. Gökhan İzbırak.

Abstract

Traffic Engineering (TE) uses methods and models from a variety of mathematical fields, such as statistics and optimization, to improve the performance of telecommunication networks. In this thesis, we study TE problems dealing with networks that impose single-path routing. As the name infers, in this type of routing, the traffic flow of each "commodity" cannot be split in its path between its origin and destination. Given its cheap cost, single-path routing is widely used in today's data centers, where thousands of stored servers perform computations or host Internet services. One common case of single-path routing is the one enforced by the Spanning Tree Protocol (STP) in switched Ethernet networks. The STP requires the network to keep its activated links loop-free, while maintaining the other redundant links ready for back-up, in case of link failure. The Multiple Spanning Tree Protocol (MSTP) extends the STP by installing multiple virtual networks compliant with the STP, over a single physical topology. Therefore, the MSTP is greatly beneficial for network service providers, as it allows for a more efficient use of the existing resources.Network design problems dealing with the MSTP are generally highly combinatorial and very hard to solve. As such, TE literature mainly suggests heuristic methods, which can quickly produce reasonable designs. Notwithstanding, due to a scarce existence of lower bounds to the optimum values of such problems, there is little knowledge about the quality of the solutions provided by these heuristics.In this sense, we propose mathematical programming models and methods that can provide optimal designs for these networks, or at the very least, obtain valid lower bounds. Taking into mind the goal of avoiding congestion in the network, we focus on two problems that deal with the following load-balancing objectives: the minimization of the worst-case link utilization, and the minimization of flow costs given by piecewise linear functions that penalize heavily-loaded links. The study of both these problems yielded relevant by-products: the first is the study of a MSTP network design problem, where we minimize the total load, and the second is the study of a fundamental unsplittable multicommodity flow problem with piecewise linear costs.For all the considered problems, we provide studies of complexity, extensive polyhedral studies to compare the proposed formulations, and a wide array of computational experiments to evaluate the performance of the proposed models and methods. Doctorat en Sciences info:eu-repo/semantics/nonPublished

Abstract

xtabstractPassenger railway transportation plays a crucial role in the mobility in Europe. Since the privatization of the railway sector in the 90s, passenger satisfaction has become an important performance indicator in this sector. A key aspect for passengers is the reliability of transfers between trains. When a train arrives at the station with a delay, passengers might miss their connection if the next train departs on time. These passengers then prefer the connecting train to wait, but this introduces delays for many other passengers. Delay Management is a field in railway operations that deals with this situation. It determines whether a connecting train should wait for the passengers that arrive with a delayed train or should depart on time. In this thesis, we apply techniques from Operations Research to develop models and solution approaches for Delay Management. The objective in our models is the minimization of passenger delay. First, we extend the classical delay management model with passenger rerouting. This allows us to compute the exact delays for passengers. We develop an exact algorithm and several heuristics to solve this extension. Then, we incorporate the limited capacity of the stations in our models. Stations are the bottlenecks of the railway infrastructure, where delays of one train can easily propagate to other trains. When optimizing the wait-depart decisions, these secondary delays should be considered. We therefore develop an integrated model that includes headway constraints for trains on the same track in the station and an iterative approach that evaluates the timetable microscopically.

Abstract

xtabstract__Abstract__ Crew management in passenger rail transport is an important factor that contributes to both the quality of service to the railway passengers and to the operational costs of the train operating company. This thesis describes how the (railway) Crew Management process can be improved with the introduction of advanced decision support systems, based on advanced mathematical models and algorithms. We provide a managerial perspective on the change process, related to the introduction of these systems, and give an overview of the lessons learned. We have shown that introducing decision support can give substantial improvements in the overall performance of a railway company. Within NS, the support for the Crew Management process has led to a stable relationship between management and train crew. In addition, the lead-time of the planning process is shortened from months to hours and NS is now able to perform scenario analyses, e.g., to study effects of adjusting the labour rules. Also, NS can adjust their service when severe weather conditions are expected, by creating a specific winter timetable shortly before the day of operation. Finally, we also introduced a decision support system for real-time rescheduling of crew duties on the day of operations. This enables us to adapt the actual crew schedules very quickly. As a result, we reduce the number of cancelled trains and fewer trains will be delayed in case of unforeseen disruptions.