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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
21

Optimization and Decision Making under Uncertainty for Distributed Generation Technologies

Marino, Carlos Antonio 09 December 2016 (has links)
This dissertation studies two important models in the field of the distributed generation technologies to provide resiliency to the electric power distribution system. In the first part of the dissertation, we study the impact of assessing a Combined Cooling Heating Power system (CCHP) on the optimization and management of an on-site energy system under stochastic settings. These mathematical models propose a scalable stochastic decision model for large-scale microgrid operation formulated as a two-stage stochastic linear programming model. The model is solved enhanced algorithm strategies for Benders decomposition are introduced to find an optimal solution for larger instances efficiently. Some observations are made with different capacities of the power grid, dynamic pricing mechanisms with various levels of uncertainty, and sizes of power generation units. In the second part of the dissertation, we study a mathematical model that designs a Microgrid (MG) that integrates conventional fuel based generating (FBG) units, renewable sources of energy, distributed energy storage (DES) units, and electricity demand response. Curtailment of renewable resources generation during the MG operation affects the long-term revenues expected and increases the greenhouses emission. Considering the variability of renewable resources, researchers should pay more attention to scalable stochastic models for MG for multiple nodes. This study bridges the research gap by developing a scalable chance-constrained two-stage stochastic program to ensure that a significant portion of the renewable resource power output at each operating hour will be utilized. Finally, some managerial insights are drawn into the operation performance of the Combined Cooling Heating Power and a Microgrid.
22

A robust optimization approach for active and reactive power management in smart distribution networks using electric vehicles

Pirouzi, S., Agahaei, J., Latify, M.A., Yousefi, G.R., Mokryani, Geev 07 July 2017 (has links)
Yes / This paper presents a robust framework for active and reactive power management in distribution networks using electric vehicles (EVs). The method simultaneously minimizes the energy cost and the voltage deviation subject to network and EVs constraints. The uncertainties related to active and reactive loads, required energy to charge EV batteries, charge rate of batteries and charger capacity of EVs are modeled using deterministic uncertainty sets. Firstly, based on duality theory, the max min form of the model is converted to a max form. Secondly, Benders decomposition is employed to solve the problem. The effectiveness of the proposed method is demonstrated with a 33-bus distribution network.
23

Designing Two-Echelon Distribution Networks under Uncertainty / Design de réseaux de distribution à deux échelons sous incertitude

Ben Mohamed, Imen 27 May 2019 (has links)
Avec la forte croissance du e-commerce et l'augmentation continue de la population des villes impliquant des niveaux de congestion plus élevés, les réseaux de distribution doivent déployer des échelons supplémentaires pour offrir un ajustement dynamique aux besoins des entreprises au cours du temps et faire face aux aléas affectant l’activité de distribution. Dans ce contexte, les praticiens s'intéressent aux réseaux de distribution à deux échelons. Dans cette thèse, nous commençons par présenter une revue complète des problèmes de design des réseaux de distribution et souligner des caractéristiques essentielles de modélisation. Ces aspects impliquent la structure à deux échelons, l’aspect multi-période, l’incertitude et les méthodes de résolution. Notre objectif est donc, d’élaborer un cadre complet pour le design d’un réseau de distribution efficace à deux échelons, sous incertitude et multi-périodicité, dans lequel les produits sont acheminés depuis les plateformes de stockage (WP) vers les plateformes de distribution (DP) avant d'être transportés vers les clients. Ce cadre est caractérisé par une hiérarchie temporelle entre le niveau de design impliquant des décisions relatives à la localisation des plateformes et à la capacité allouée aux DPs sur une échelle de temps annuelle, et le niveau opérationnel concernant des décisions journalières de transport. % sur une base journalière.Dans une première étude, nous introduisons le cadre complet pour le problème de design de réseaux de distribution à deux échelons avec une demande incertaine, une demande et un coût variables dans le temps. Le problème est formulé comme un programme stochastique à plusieurs étapes. Il implique au niveau stratégique des décisions de localisation des DPs ainsi que des décisions d'affectation des capacités aux DPs sur plusieurs périodes de design, et au niveau opérationnel des décisions de transport sous forme d'arcs origine-destination. Ensuite, nous proposons deux modèles alternatifs basés sur la programmation stochastique à deux étapes avec recours, et les résolvons par une approche de décomposition de Benders intégrée à une technique d’approximation moyenne d’échantillon (SAA). Par la suite, nous nous intéressons à la livraison du dernier kilomètre dans un contexte urbain où les décisions de transport dans le deuxième échelon sont caractérisées par des tournées de véhicules. Un problème multi-période stochastique de localisation-routage à deux échelons avec capacité (2E-SM-CLRP) est défini, dans lequel les décisions de localisation concernent les WPs et les DPs. Le modèle est un programme stochastique à deux étapes avec recours en nombre entier. Nous développons un algorithme de décomposition de Benders. Les décisions de localisation et de capacité sont déterminées par la solution du problème maître de Benders. Le sous-problème résultant est un problème multi-dépôt de tournées de véhicule avec des dépôts et véhicules capacitaires qui est résolu par un algorithme de branch-cut-and-price.Enfin, nous étudions le cadre à plusieurs étapes proposé pour le problème stochastique multi-période de design de réseaux de distribution à deux échelons et évaluons sa tractabilité. Pour ceci, nous développons une heuristique à horizon glissant qui permet d’obtenir des bornes de bonne qualité et des solutions de design pour le modèle à plusieurs étapes. / With the high growth of e-commerce and the continuous increase in cities population contrasted with the rising levels of congestion, distribution schemes need to deploy additional echelons to offer more dynamic adjustment to the requirement of the business over time and to cope with all the random factors. In this context, a two-echelon distribution network is nowadays investigated by the practitioners.In this thesis, we first present a global survey on distribution network design problems and point out many critical modeling features, namely the two-echelon structure, the multi-period setting, the uncertainty and solution approaches. The aim, here, is to propose a comprehensive framework for the design of an efficient two-echelon distribution network under multi-period and stochastic settings in which products are directed from warehouse platforms (WPs) to distribution platforms (DPs) before being transported to customers. A temporal hierarchy characterizes the design level dealing with facility-location and capacity decisions over a set of design periods, while the operational level involves transportation decisions on a daily basis.Then, we introduce the comprehensive framework for the two-echelon distribution network design problem under uncertain demand, and time-varying demand and cost, formulated as a multi-stage stochastic program. This work looks at a generic case for the deployment of a retailer's distribution network. Thus, the problem involves, at the strategic level, decisions on the number and location of DPs along the set of design periods as well as decisions on the capacity assignment to calibrate DP throughput capacity. The operational decisions related to transportation are modeled as origin-destination arcs. Subsequently, we propose alternative modeling approaches based on two-stage stochastic programming with recourse, and solve the resulting models using a Benders decomposition approach integrated with a sample average approximation (SAA) technique.Next, we are interested in the last-mile delivery in an urban context where transportation decisions involved in the second echelon are addressed through multi-drop routes. A two-echelon stochastic multi-period capacitated location-routing problem (2E-SM-CLRP) is defined in which facility-location decisions concern both WPs and DPs. We model the problem using a two-stage stochastic program with integer recourse. To solve the 2E-SM-CLRP, we develop a Benders decomposition algorithm. The location and capacity decisions are fixed from the solution of the Benders master problem. The resulting subproblem is a capacitated vehicle-routing problem with capacitated multi-depot (CVRP-CMD) and is solved using a branch-cut-and-price algorithm.Finally, we focus on the multi-stage framework proposed for the stochastic multi-period two-echelon distribution network design problem and evaluate its tractability. A scenario tree is built to handle the set of scenarios representing demand uncertainty. We present a compact formulation and develop a rolling horizon heuristic to produce design solutions for the multi-stage model. It provides good quality bounds in a reasonable computational times.
24

Integer programming-based decomposition approaches for solving machine scheduling problems

Sadykov, Ruslan 26 June 2006 (has links)
The aim in this thesis is to develop efficient enumeration algorithms to solve certain strongly NP-hard scheduling problems. These algorithms were developed using a combination of ideas from Integer Programming, Constraint Programming and Scheduling Theory. In order to combine different techniques in one algorithm, decomposition methods are applied. The main idea on which the first part of our results is based is to separate the optimality and feasibility components of the problem and let different methods tackle these components. Then IP is ``responsible' for optimization, whereas specific combinatorial algorithms tackle the feasibility aspect. Branch-and-cut and branch-and-price algorithms based on this idea are proposed to solve the single-machine and multi-machine variants of the scheduling problem to minimize the sum of the weights of late jobs. Experimental research shows that the algorithms proposed outperform other algorithms available in the literature. Also, it is shown that these algorithms can be used, after some modification, to solve the problem of minimizing the maximum tardiness on unrelated machines. The second part of the thesis deals with the one-machine scheduling problem to minimize the weighted total tardiness. To tackle this problem, the idea of a partition of the time horizon into intervals is used. A particularity of this approach is that we exploit the structure of the problem to partition the time horizon. This particularity allowed us to propose two new Mixed Integer Programming formulations for the problem. The first one is a compact formulation and can be used to solve the problem using a standard MIP solver. The second formulation can be used to derive lower bounds on the value of the optimal solution of the problem. These lower bounds are of a good quality, and they can be obtained relatively fast.
25

Extensions of Multistage Stochastic Optimization with Applications in Energy and Healthcare

Kuznia, Ludwig Charlemagne 01 January 2012 (has links)
This dissertation focuses on extending solution methods in the area of stochastic optimization. Attention is focused to three specific problems in the field. First, a solution method for mixed integer programs subject to chance constraints is discussed. This class of problems serves as an effective modeling framework for a wide variety of applied problems. Unfortunately, chance constrained mixed integer programs tend to be very challenging to solve. Thus, the aim of this work is to address some of these challenges by exploiting the structure of the deterministic reformulation for the problem. Second, a stochastic program for integrating renewable energy sources into traditional energy systems is developed. As the global push for higher utilization of such green resources increases, such models will prove invaluable to energy system designers. Finally, a process for transforming clinical medical data into a model to assist decision making during the treatment planning phase for palliative chemotherapy is outlined. This work will likely provide decision support tools for oncologists. Moreover, given the new requirements for the usage electronic medical records, such techniques will have applicability to other treatment planning applications in the future.
26

Uma implementação em paralelo para decomposicção de benders aplicada a sistemas eixo raio com múltipla atribuição. / A parallel Benders decoposition implmentation for multiple hub and spoke system allocation.

Cabral, Raquel da Silva 23 February 2006 (has links)
Hub and Spoke systems, is a important research area in localization theory. This occur, because of these systems are very used in logistics problems, e.g., telecommunication networks and transport of passenger and load.To serve the demand of each pair source destination, basically, the Hub and Spoke system replaces direct connections between the pairs for a hubs network. These hubs group the traffic sharing the transportation medium. To get the best hubs configuration is necessary efficient methods, because this problem, hubs allocation, is a NP-problem. In this work was developed an parallel implementation of the Benders Decomposition method for the uncapacitated multiple allocation hub location problem. In our implementation we use the Skorin- Kapov model. The parallel implementation of Benders Decomposition for hub and spoke problem is not known in literature. The results show that the parallel approach is applicable and more efficient that nonparallel one. The experiments reveals that the parallel algorithm had a time execution 70% minor when compared with the nonparallel one. / Sistemas do tipo eixo raio, tornaram-se uma importante área de pesquisa da teoria de localização nas últimas décadas. Esse destaque deve-se em grande parte ao sucesso de sua utilização em sistemas logísticos, tanto de transporte de passageiros quanto de cargas, e em redes de telecomunicações. Ao invés de servir cada par origem destino de demanda com uma conexão direta, sistemas do tipo eixo raio substituem essas conexões diretas por uma rede de concentradores. Esses concentradores permitem que o tráfego seja agrupado e transportado através de um meio de transporte compartilhado, para ser então entregue aos respectivos destinos. Sendo um problema NP, é necessário o uso de métodos eficientes para sua resolução. Neste trabalho, é desenvolvida uma implementação em paralelo do método de Decomposição de Benders para o problema de localização de concentradores de alocação múltipla não capacitados. A implementação em paralelo do método de Decomposição de Benders para o problema eixo raio não é conhecido na literatura, entretanto os bons resultados obtidos pelo algoritmo paralelo desenvolvido revelam que a abordagem paralela é aplicável e mais eficiente. Nos experimentos realizados, o algoritmo paralelo apresentou um tempo de resposta até 70% menor que o tempo de resposta do algoritmo seqüencial.
27

Aplicação da técnica de decomposição de Benders para cálculo da reserva girante considerando a curva de capabilidade dos geradores

Aleixo, Marcelo de Souza 29 June 2018 (has links)
Submitted by Renata Lopes (renatasil82@gmail.com) on 2018-11-06T17:02:42Z No. of bitstreams: 1 marcelodesouzaaleixo.pdf: 2786878 bytes, checksum: dfbd57d9b8be2153917fe637209df9f0 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-11-23T13:19:16Z (GMT) No. of bitstreams: 1 marcelodesouzaaleixo.pdf: 2786878 bytes, checksum: dfbd57d9b8be2153917fe637209df9f0 (MD5) / Made available in DSpace on 2018-11-23T13:19:16Z (GMT). No. of bitstreams: 1 marcelodesouzaaleixo.pdf: 2786878 bytes, checksum: dfbd57d9b8be2153917fe637209df9f0 (MD5) Previous issue date: 2018-06-29 / Este trabalho apresenta uma metodologia para cálculo da reserva girante do sistema elétrico de potência. A proposta inclui a curva de capabilidade dos geradores bem como cenários de carga e de geração eólica bem como falhas no sistema de geração e transmissão. O fluxo de potência ótimo é utilizado para determinar as condições iniciais do sistema, caso base. A partir daí o problema é resolvido de forma interativa entre dois estágios em umaestruturamestreeescravo: Oproblemamestreutilizaprogramaçãolinearinteirapara determinar o número mínimo de unidades de geração disponíveis (ligadas); O problema escravo verifica se o número de unidades ligadas é suficiente para operação do sistema. Este problema corresponde ao Fluxo de Potência Ótimo com o objetivo de obter o mínimo corte de carga para cada condição operativa. O corte de Benders é produzido para cada cenário, altenativamente, será investigado a utilização da média dos cortes de Benders. O processo termina quando não ocorrer corte de carga. A metodologia é testada utilizando um sistema teste de 4 barras como exemplo tutorial e também aplicada nos sistemas IEEE 14, 39 e 118 barras. / This work presents a methodology for calculating the spinning reserve of the electric power system. The proposal includes the capability curve of the generators as well as errors of prediction of load, wind generation, failures in the system of generation and transmission. The optimal power flow is used to determine the initial conditions of the system, base case. From this point the problem is solved interactively between two stages in a master and slave structure: The first uses integer linear programming to determine the minimum number of available (turn on) generation units; The second corresponds to the Optimum Power Flow with the purpose of obtaining the minimum load shedding for each operative condition. The cut of Benders is produced for each scenario, alternatively, the use of the average of cuts will be investigated. The process ends when there is no load shedding. The methodology is tested using a 4-bar test system as a tutorial example and also applied in IEEE systems 14, 39 and 118 bars.
28

Models and Algorithms for Some Combinatorial Optimization Problems: University Course Timetabling, Facility Layout and Integrated Production-Distribution Scheduling

Wang, Yuqiang 24 August 2007 (has links)
In this dissertation, we address three different combinatorial optimization problems (COPs), each of which has specific real-life applications. Owning to their specific nature, these problems are different from those discussed in the literature. For each of these problems, we present a mathematical programming formulation, analyze the problem to determine its useful, inherent structural properties, and develop an efficient methodology for its solution by exploiting these properties. The first problem that we address is the course timetabling problem encountered at Virginia Tech. The course timetabling problem for a university is a difficult problem and has been studied by many researchers over the years. As a result, a plethora of models and approaches have been reported in the literature. However, most of these studies have focused on applications pertaining to course scheduling for a single or at most few departments of a university. The sheer size of the university-wide timetabling problem that we address, involving thousands of courses to be scheduled in hundreds of classrooms in each semester, makes it a challenging problem. We employ an appropriate decomposition technique that relies on some inherent structural properties of the problem both during the modeling and algorithmic development phases. We show the superiority of the schedules generated by our methodology over those that are currently being used. Also, our methodology requires only a reasonable amount of computational time in solving this large-size problem. A facility layout problem involving arbitrary-shaped departments is the second problem that we investigate in this dissertation. We designate this problem as the arbitrary-shaped facility layout problem (ASFLP). The ASFLP deals with arranging a given set of departments (facilities, workstations, machines) within the confines of a given floor space, in order to optimize a desired metric, which invariably relates to the material handling cost. The topic of facility planning has been addressed rather extensively in the literature. However, a major limitation of most of the work reported in the literature is that they assume the shape of a department to be a rectangle (or even a square). The approach that relies on approximating an arbitrary-shaped department by a rectangle might result in an unattractive solution. The key research questions for the ASFLP are: (1) how to accurately model the arbitrary-shaped departments, and (2) how to effectively and efficiently determine the desired layout. We present a mixed-integer programming model that maintains the arbitrary shapes of the departments. We use a meta-heuristic to solve the large-size instances of the ASFLP in a reasonable amount of time. The third problem that we investigate is a supply chain scheduling problem. This problem involves two stages of a supply chain, specifically, a manufacturer and one or more customers. The key issue is to achieve an appropriate coordination between the production and distribution functions of the manufacturer so as to minimize the sum of the shipping and job tardiness costs. We, first, address a single customer problem, and then, extend our analysis to the case of multiple customers. For the single-customer problem, we present a polynomial-time algorithm to solve it to optimality. For the multiple-customer problem, we prove that this problem is NP-hard and solve it by appropriately decomposing it into subproblems, one of which is solvable in polynomial time. We propose a branch-and-bound-based methodology for this problem that exploits its structural properties. Results of an extensive computational experimentation are presented that show the following: (1) our algorithms are efficient to use and effective to implement; and (2) significant benefits accrue as a result of integrating the production and distribution functions. / Ph. D.
29

Modeling, Analysis, and Algorithmic Development of Some Scheduling and Logistics Problems Arising in Biomass Supply Chain, Hybrid Flow Shops, and Assembly Job Shops

Singh, Sanchit 15 July 2019 (has links)
In this work, we address a variety of problems with applications to `ethanol production from biomass', `agile manufacturing' and `mass customization' domains. Our motivation stems from the potential use of biomass as an alternative to non-renewable fuels, the prevalence of `flexible manufacturing systems', and the popularity of `mass customization' in today's highly competitive markets. Production scheduling and design and optimization of logistics network mark the underlying topics of our work. In particular, we address three problems, Biomass Logistics Problem, Hybrid Flow Shop Scheduling Problem, and Stochastic Demand Assembly Job Scheduling Problem. The Biomass Logistics Problem is a strategic cost analysis for setup and operation of a biomass supply chain network that is aimed at the production of ethanol from switchgrass. We discuss the structural components and operations for such a network. We incorporate real-life GIS data of a geographical region in a model that captures this problem. Consequently, we develop and demonstrate the effectiveness of a `Nested Benders' based algorithm for an efficient solution to this problem. The Hybrid Flow Shop Scheduling Problem concerns with production scheduling of a lot over a two-stage hybrid flow shop configuration of machines, and is often encountered in `flexible manufacturing systems'. We incorporate the use of `lot-streaming' in order to minimize the makespan value. Although a general case of this problem is NP-hard, we develop a pseudo-polynomial time algorithm for a special case of this problem when the sublot sizes are treated to be continuous. The case of discrete sublot sizes is also discussed for which we develop a branch-and-bound-based method and experimentally demonstrate its effectiveness in obtaining a near-optimal solution. The Stochastic Demand Assembly Job Scheduling Problem deals with the scheduling of a set of products in a production setting where manufacturers seek to fulfill multiple objectives such as `economy of scale' together with achieving the flexibility to produce a variety of products for their customers while minimizing delivery lead times. We design a novel methodology that is geared towards these objectives and propose a Lagrangian relaxation-based algorithm for efficient computation. / Doctor of Philosophy / In this work, we organize our research efforts in three broad areas - Biomass Supply Chain, Hybrid Flow Shop, and Assembly Job Shop, which are separate in terms of their application but connected by scheduling and logistics as the underlying functions. For each of them, we formulate the problem statement and identify the challenges and opportunities from the viewpoint of mathematical decision making. We use some of the well known results from the theory of optimization and linear algebra to design effective algorithms in solving these specific problems within a reasonable time limit. Even though the emphasis is on conducting an algorithmic analysis of the proposed solution methods and in solving the problems analytically, we strive to capture all the relevant and practical features of the problems during formulation of each of the problem statement, thereby maintaining their applicability. The Biomass Supply Chain pertains to the production of fuel grade ethanol from naturally occurring biomass in the form of switchgrass. Such a system requires establishment of a supply chain and logistics network that connects the production fields at its source, the intermediate points for temporary storage of the biomass, and bio-energy plant and refinery at its end for conversion of the cellulosic content in the biomass to crude oil and ethanol, respectively. We define the components and operations necessary for functioning of such a supply chain. The Biomass Logistics Problem that we address is a strategic cost analysis for setup and operation of such a biomass supply chain network. We focus our attention to a region in South Central Virginia and use the detailed geographic map data to obtain land use pattern in the region. We conduct survey of existing literature to obtain various transportation related cost factors and costs associated with the use of equipment. Our ultimate aim here is to understand the feasibility of running a biomass supply chain in the region of interest from an economic standpoint. As such, we represent the Biomass Logistics Problem with a cost-based optimization model and solve it in a series of smaller problems. A Hybrid Flow Shop (HFS) is a configuration of machines that is often encountered in the flexible manufacturing systems, wherein a particular station of machines can execute processing of jobs/tasks simultaneously. In our work, we approach a specific type of HFS, with a single machine at the first stage and multiple identical machines at the second stage. A batch or lot of jobs/items is considered for scheduling over such an HFS. Depending upon the area of application, such a batch is either allowed to be split into continuous sections or restricted to be split in discrete sizes only. The objective is to minimize the completion time of the last job on its assigned machine at the second stage. We call this problem, Hybrid Flow Shop Scheduling Problem, which is known to be a hard problem in literature. We aim to derive the results which will reduce the complexity of this problem, and develop both exact as well as heuristic methods in order to obtain near-optimal solution to this problem. An Assembly Job Shop is a variant of the classical Job Shop which considers scheduling a set of assembly operations over a set of assembly machines. Each operation can only be started once all the other operations in its precedence relationship are completed. Assembly Job Shop are at the core of some of the highly competitive manufacturing facilities that are principled on the philosophy of Mass Customization. Assuming an inherent nature of demand uncertainty, this philosophy aims to achieve ‘economy of scale’ together with flexibility to produce a variety of products for the customers while minimizing the delivery lead times simultaneously. We incorporate some of these challenges in a concise framework of production scheduling and call this problem as Stochastic Demand Assembly Job Scheduling Problem. We design a novel methodology that is geared towards achieving the set objectives and propose an effective algorithm for efficient computation.
30

Time-staged decomposition and related algorithms for stochastic mixed-integer programming

Qi, Yunwei 24 August 2012 (has links)
No description available.

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