Abstract

Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads’ length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO2 emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario. Document type: Article

Abstract

In Southeast Asian cities, it is common for logistic companies to operate a heterogeneous fleet of delivery vehicles with motorcycles being the preferred vehicle to handle the final phase of delivery. In such scenarios, heterogeneous fleet vehicle routing problem (HFVRP) is generally applied to plan an optimal delivery. However, in many downtown cores of large and rapidly developing Southeast Asian cities, HFVRP is neither viable nor reliable because of road usage restrictions. The purpose of this article is to develop and test a different approach that accurately takes these restrictions into account and provides viable and more sustainable results. Restrictions in this paper refer to situations of urban areas in Vietnam where (i) certain vehicle types are prohibited in specified areas or where narrow alleyways limit the utilization of vehicles that exceed the road capacity and (ii) certain roads are exclusive to certain vehicle types. In networks, limited access and exclusive lanes are represented as links, or arcs, exclusive to one or another. Taking these limitations into consideration, we have developed a unique model, which we have termed Vehicle Routing Problem with Exclusive Links (VRP-EL). The model was validated and tested for its performance on scenarios with varying ratios of exclusive links. Scenarios up to 500 customers were tested on a meta-heuristic algorithm, simulated annealing. VRP-EL produces realistic outcomes. Limiting certain links to be selected according to vehicle types increases overall travel distance. However, this increase outweighs the cost of re-planning and rerouting had they not been constrained initially. The reduction in traveling distance leads to fossil fuel reduction for the overall system. The estimation of reduced carbon emissions through applying the proposed model is presented. Considering the severe traffic congestion and carbon emissions caused by motorcycles in Vietnam, the proposed model leads to a sustainable road environment. Document type: Article

Abstract

Generating realistic configurations of urban models is a vital part of the modeling process, especially if these models are used for evaluation and analysis. In this work, we address the problem of assigning objects to their storage locations inside a warehouse which has a great impact on the quality of operations within a warehouse. Existing storage policies aim to improve the efficiency by minimizing travel time or by classifying the items based on some features. We go beyond existing methods as we analyze warehouse layout network in an attempt to understand the factors that affect traffic within the warehouse. We use simulated annealing based sampling to assign items to their storage locations while reducing traffic congestion and enhancing the speed of order picking processes. The proposed method enables a range of applications including efficient storage assignment, warehouse reliability evaluation and traffic congestion estimation. Document type: Part of book or chapter of book

Abstract

The introduction of electricity markets and integration of Distributed Generation (DG) have been influencing the power system’s structure change. Recently, the smart grid concept has been introduced, to guarantee a more efficient operation of the power system using the advantages of this new paradigm. Basically, a smart grid is a structure that integrates different players, considering constant communication between them to improve power system operation and management. One of the players revealing a big importance in this context is the Virtual Power Player (VPP). In the transportation sector the Electric Vehicle (EV) is arising as an alternative to conventional vehicles propel by fossil fuels. The power system can benefit from this massive introduction of EVs, taking advantage on EVs’ ability to connect to the electric network to charge, and on the future expectation of EVs ability to discharge to the network using the Vehicle-to-Grid (V2G) capacity. This thesis proposes alternative strategies to control these two EV modes with the objective of enhancing the management of the power system. Moreover, power system must ensure the trips of EVs that will be connected to the electric network. The EV user specifies a certain amount of energy that will be necessary to charge, in order to ensure the distance to travel. The introduction of EVs in the power system turns the Energy Resource Management (ERM) under a smart grid environment, into a complex problem that can take several minutes or hours to reach the optimal solution. Adequate optimization techniques are required to accommodate this kind of complexity while solving the ERM problem in a reasonable execution time. This thesis presents a tool that solves the ERM considering the intensive use of EVs in the smart grid context. The objective is to obtain the minimum cost of ERM considering: the operation cost of DG, the cost of the energy acquired to external suppliers, the EV users payments and remuneration and penalty costs. This tool is directed to VPPs that manage specific network areas, where a high penetration level of EVs is expected to be connected in these areas. The ERM is solved using two methodologies: the adaptation of a deterministic technique proposed in a previous work, and the adaptation of the Simulated Annealing (SA) technique. With the purpose of improving the SA performance for this case, three heuristics are additionally proposed, taking advantage on the particularities and specificities of an ERM with these characteristics. A set of case studies are presented in this thesis, considering a 32 bus distribution network and up to 3000 EVs. The first case study solves the scheduling without considering EVs, to be used as a reference case for comparisons with the proposed approaches. The second case study evaluates the complexity of the ERM with the integration of EVs. The third case study evaluates the performance of scheduling with different control modes for EVs. These control modes, combined with the proposed SA approach and with the developed heuristics, aim at improving the quality of the ERM, while reducing drastically its execution time. The proposed control modes are: uncoordinated charging, smart charging and V2G capability. The fourth and final case study presents the ERM approach applied to consecutive days. A introdução dos mercados de electricidade e integração da produção distribuída tem causado alterações na estrutura e no modo de operação dos sistemas eléctricos de energia. Recentemente, o conceito de SmartGrid foi introduzido com o objectivo de garantir uma operação mais eficiente dos sistemas eléctricos de energia. Basicamente, uma SmartGrid é uma estrutura que envolve as diferentes entidades, e considera uma constante interacção e comunicação entre as mesmas, para melhorar a operação do sistema eléctrico de energia. Umas das entidades com grande relevância neste contexto são os Virtual Power Players (VPP). Os Veículos Eléctricos (VE) têm surgido no sector dos transportes como uma alternativa aos veículos convencionais abastecidos por combustíveis fósseis. O sistema eléctrico de energia pode beneficiar dessa introdução massiva de VEs, aproveitando a sua capacidade de ligação à rede eléctrica para a carga dos veículos, e numa perspectiva mais vanguardista para descarga, fornecendo energia à rede. Nesta tese são propostas estratégias de gestão das cargas/descargas do VE com o objectivo de melhorar a operação do sistema eléctrico de energia. No processo de gestão de recursos, o operador da rede ou agregadores deverão considerar as necessidades dos utilizadores dos VEs, nomeadamente garantir a existência de energia suficiente nas baterias para que os utilizadores possam efectuar as viagens que tem programadas. A introdução dos veículos eléctricos nos sistemas eléctricos de energia torna a gestão de recurso energéticos num ambiente de SmartGrid, um problema complexo que pode levar vários minutos ou horas para se obter uma solução. Considerando o período para o qual é necessário efectuar o escalonamento, que normalmente é para o dia seguinte ou para os 15 minutos seguintes no caso de escalonamento em tempo real, é necessário desenvolver algoritmos que permitam a resolução dos problemas em tempos muito razoáveis. Nesta tese é apresentada uma ferramenta que permite a resolução do problema do escalonamento de recursos, considerando o uso intensivo de VEs no contexto das SmartGrid. O objectivo é obter o custo mínimo de operação, considerando: o custo de operação da produção distribuída, o custo da energia adquirida a fornecedores externos, a remuneração do uso de VEs e os custos associados ao incumprimento de condições contratuais. A ferramenta desenvolvida é direccionada para a utilização pelos VPPs que gerem áreas de rede específicas, com um nível de penetração elevado de VEs. O escalonamento de recursos é resolvido usando duas metodologias: a adaptação de uma técnica determinista proposta em trabalhos anteriores, e a adaptação da técnica de Simulated Annealing, sendo propostas três abordagens para melhorar a solução obtida através da técnica de Simulated Annealing. Diversos casos de estudo são apresentados, considerando uma rede de distribuição com 32 barramentos e cenários de evolução da penetração de VE até 3000 veículos. O primeiro caso de estudo, usado como um caso de referência para comparações com as abordagens propostas, resolve o escalonamento de recursos sem considerar VEs. O segundo caso avalia a complexidade do escalonamento de recursos com a integração de VEs, permitindo testar as técnicas propostas. O terceiro caso avalia o desempenho do escalonamento com diferentes modos de controlo de EVs. O quarto caso de estudo apresenta a abordagem aplicada ao escalonamento de recursos em diversos dias consecutivos.

Abstract

With the increasing usage of QoS-based applications, traffic engineering in communication networks has become more crucial. For the optimal utilization of network resources, service providers need to consider multiple criteria that can be customer- or traffic-oriented. Multiprotocol Label Switching (MPLS) has been developed to apply more convenient traffic engineering in autonomous systems. This thesis focuses on the multiobjective optimization of Label Switched Path design problem in MPLS networks. Minimal routing cost, optimal load-balance in the network, and minimal splitting of traffic form the objectives. The problem is formulated as a zero-one mixed integer program and aims at exploring the trade-offs among the objectives. The integer constraints make the problem NP-hard. In the thesis first both the exact and heuristic multiobjective optimization approaches are discussed, and then a heuristic framework based on simulated annealing is developed. Various search strategies within the framework are investigated and experimental studies are carried out for a performance comparison.

Abstract

International audience; Air transportation traffic is progressively increasing over the years and dealing with it is an essential task to guarantee fluid flights in the future. Several works already indexed multiple aspects of aviation, among them, the E-MAN system. It introduced the sequencing of arriving traffic, starting from early stages of the En-route phase. This change facilitated the work for the approach controllers but also increased the workload of the En-route controllers. To handle that workload, controllers are now assisted by tools that consider the new constraints introduced by the arrival management system and propose advisories. From that same perspective, our project focuses on an algorithm for a helper tool that will combine both aspects of traffic sequencing in the En-route phase and conflict resolution. With this novel approach, we automatically generate near-to-optimal flight decisions, given that we can modify the speed and the flight level to respect the sequencing constraints and cut down potential conflicts. We categorize the problem as a mathematical optimization case. Thus, we describe a detailed mathematical model which covers all the aspects of the problem. This model gives a basis for the implementation of the flight optimizer. Later, we propose a solution based on a sliding window simulated annealing algorithm which reduces the complexity and takes into account uncertainties. Finally, we successfully test an implementation of the solution with real-life traffic data. It corresponds to flights within France going towards Paris CDG airport over a period of 24 hours. The results demonstrate a total conflicts resolution with satisfying compliance with sequencing constraints.

Abstract

International audience; Air transportation traffic is progressively increasing over the years and dealing with it is an essential task to guarantee fluid flights in the future. Several works already indexed multiple aspects of aviation, among them, the E-MAN system. It introduced the sequencing of arriving traffic, starting from early stages of the En-route phase. This change facilitated the work for the approach controllers but also increased the workload of the En-route controllers. To handle that workload, controllers are now assisted by tools that consider the new constraints introduced by the arrival management system and propose advisories. From that same perspective, our project focuses on an algorithm for a helper tool that will combine both aspects of traffic sequencing in the En-route phase and conflict resolution. With this novel approach, we automatically generate near-to-optimal flight decisions, given that we can modify the speed and the flight level to respect the sequencing constraints and cut down potential conflicts. We categorize the problem as a mathematical optimization case. Thus, we describe a detailed mathematical model which covers all the aspects of the problem. This model gives a basis for the implementation of the flight optimizer. Later, we propose a solution based on a sliding window simulated annealing algorithm which reduces the complexity and takes into account uncertainties. Finally, we successfully test an implementation of the solution with real-life traffic data. It corresponds to flights within France going towards Paris CDG airport over a period of 24 hours. The results demonstrate a total conflicts resolution with satisfying compliance with sequencing constraints.