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نتیجه جستجو - Constrained optimization

تعداد مقالات یافته شده: 8
ردیف عنوان نوع
1 Intrusive acceleration strategies for uncertainty quantificationfor hyperbolic systems of conservation laws
استراتژی های شتابزنی سرعتی برای تعیین کمیت عدم اطمینان برای سیستم هایپربولیک قوانین حفاظت-2020
Methods for quantifying the effects of uncertainties in hyperbolic problems can be divided into intrusive and non-intrusive techniques. Non-intrusive methods allow the usage of a given deterministic solver in a black-box manner, while being embarrassingly parallel. On the other hand, intrusive modifications allow for certain acceleration techniques. Moreover, intrusive methods are expected to reach a given accuracy with a smaller number of unknowns compared to non-intrusive techniques. This effect is amplified in settings with high dimensional uncertainty. A downside of intrusive methods is the need to guarantee hyperbolicity of the resulting moment system. In contrast to stochastic-Galerkin (SG), the Intrusive Polynomial Moment (IPM) method is able to maintain hyperbolicity at the cost of solving an optimization problem in every spatial cell and every time step. In this work, we propose several acceleration techniques for intrusive methods and study their advantages and shortcomings compared to the non-intrusive Stochastic Collocation method. When solving steady problems with IPM, the numerical costs arising from repeatedly solving the IPM optimization problem can be reduced by using concepts from PDE-constrained optimization. Integrating the iteration from the numerical treatment of the optimization problem into the moment update reduces numerical costs, while preserving local convergence. Additionally, we propose an adaptive implementation and efficient parallelization strategy of the IPM method. The effectiveness of the proposed adaptations is demonstrated for multi-dimensional uncertainties in fluid dynamics applications, resulting in the observation of requiring a smaller number of unknowns to achieve a given accuracy when using intrusive methods. Furthermore, using the proposed acceleration techniques, our implementation reaches a given accuracy faster than Stochastic Collocation.
Keywords: Uncertainty quantification | Hyperbolic conservation laws | Intrusive | Stochastic-Galerkin | Collocation | Intrusive Polynomial Moment Method
مقاله انگلیسی
2 An interval-stochastic programming based approach for a fully uncertain multi-objective and multi-mode resource investment project scheduling problem with an application to ERP project implementation
یک رویکرد مبتنی بر برنامه نویسی فاصله ای تصادفی برای یک هدف چندجانبه کاملاً نامشخص و برنامه زمانبندی پروژه سرمایه گذاری منابع چند حالته با برنامه ای برای اجرای پروژه ERP-2020
Most of the real-life project scheduling cases may involve different types of uncertainties simultane- ously such as randomness, fuzziness and dynamism. Based on this motivation, the present paper pro- poses a novel interval programming and chance constrained optimization based hybrid solution approach for a fully uncertain, multi-objective and multi-mode resource investment project scheduling problem (MRIPSP). The classical discrete-time binary integer programming formulation of the problem is extended by incorporating both the interval-valued and interval-stochastic project parameters as well as variables. In addition to the uncertain project parameters/inputs, the completion times of the activities which rep- resent the project schedule and the availabilities of the renewable project resources are also stated as uncertain project variables and represented by interval numbers. Then, the proposed interval-stochastic multi-mode resource investment project scheduling (IS-MRIPSP) model is converted into its crisp equiva- lent form by using the proposed approach. The proposed approach is also able to consider different types of project scheduling risks and produces more reliable and risk-free solutions according to the project manager’s attitude toward risks. Furthermore, in addition to the classical makespan objective, effective and efficient utilization of the renewable project resources, i.e., human resources, is also targeted. The efficiency and reliability factors of the human resources are also taken into consideration. In order to generate balanced project schedules which tradeoffbetween the project time and total human resource costs, compromise programming approach is adapted. Finally, in order to test the validity and practicality of the proposed approach, a real-life application is presented for an enterprise resource planning (ERP) implementation project scheduling problem of an international industrial software company.
Keywords: Resource investment project scheduling | Interval programming | Chance-constrained programming | Human resource allocation | ERP project management | Risk attitude
مقاله انگلیسی
3 Nonlinear optimal control of cascaded irrigation canals with conservation law PDEs
کنترل بهینه غیرخطی کانال های آبیاری آبشاری با PDE های قانون حفاظت-2020
This paper considers an optimal control problem for cascaded irrigation canals. The aim of the optimal control is to guarantee both the minimum water levels for irrigation demands and avoidance of water overflows even dam collapse. Due to the structural complexities involving control gates and interconnected long-distance water delivery reaches that are modeled by the Saint-Venant PDEs with conservation laws, wave superposition effects, coupling effects and strong nonlinearities made the optimal control be a hard task. A nonlinear optimal control method is proposed to deal with the PDE-constrained optimization problem via a control parameterization approach. Control parameterization approximates the time-varying control by a linear combination of basis functions with control parameters. The Hamiltonian function method is used to derive the gradients of the objective function with respect to the control parameters as well as the time scale parameters for providing the search directions of the optimization problem with acceptable amount of computations. Based on the gradient formulas, a gradient-based optimization algorithm is proposed to solve the optimal control problem. The proposed nonlinear optimal control method is validated in two cases: a single reach canal in Yehe Irrigation District in Hebei Province (China) and a cascaded two-reach canal system.
Keywords: Partial differential equations | Optimal control | Control parameterization | Gradient-based optimization
مقاله انگلیسی
4 یک استراتژی حاوی-شایعه موثر
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 25
شایعات کاذب منجر به زیان های بزرگ اقتصادی و یا بی ثباتی اجتماعی می¬شود. از این رو کاهش تاثیر شایعات فاجعه آور، اهمیت اساسی دارد. تمرکز این مقاله بروی چگونگی سرکوب شایعات کاذب با استفاده از حقیقت، است. اثربخشی و هزینه استراتژی حاوی شایعه به ترتیب بر اساس مجموعه ای از فرضیه های منطقی و مدل جدید ریسک اختلاط شایع - حقیقت به دست آمد. بر این اساس، مسئله اصلی به عنوان یک مسئله بهینه سازی محدود (مدل RC) مدل سازی شد که متغیر مستقل و تابع هدف آن ، به ترتیب استراتژی حاوی شایعه و کارآیی استراتژی حاوی شایعه می باشد. هدف از مسئله بهینه سازی، یافتن موثر ترین استراتژی حاوی شایعه محدود به بودجه حاوی شایعه است. برخی از استراتژی های حاوی شایعات بهینه با حل مدل RC مربوطه بدست آمد. تأثیر عوامل مختلف بر روی بیشترین اثربخشی هزینه در مدل RC، از طریق آزمایش های کامپیوتری مشخص شد. نتایج بدست آمده برای توسعه استراتژی¬های موثر شایعه، مناسب و آموزنده است.
کلید واژه ها: محدودیت شایعه | مدل توزیع اختلاط شایعات – حقیقت | اثربخشی | هزینه | بهینه سازی محدود
مقاله ترجمه شده
5 Air traffic flow management under uncertainty using chance-constrained optimization
مدیریت جریان ترافیک هوایی در شرایط عدم اطمینان با استفاده از بهینه سازی فرصت محدود-2017
In order to efficiently balance traffic demand and capacity, optimization of Air Traffic Flow Management (ATFM) relies on accurate predictions of future capacity states. How ever, these predictions are inherently uncertain due to factors, such as weather. This pa per presents a novel computationally efficient algorithm to address uncertainty in ATFM by using a chance-constrained optimization method. First, a chance-constrained model is developed based on a previous deterministic Integer Programming optimization model of ATFM to include probabilistic sector capacity constraints. Then, to efficiently solve such a large-scale chance-constrained optimization problem, a polynomial approximation-based approach is applied. The approximation is based on the numerical properties of the Bern stein polynomial, which is capable of effectively controlling the approximation error for both the function value and gradient. Thus, a first-order algorithm is adopted to obtain a satisfactory solution, which is expected to be optimal. Numerical results are reported in order to evaluate the polynomial approximation-based approach by comparing it with the brute-force method. Moreover, since there are massive independent approximation pro cesses in the polynomial approximation-based approach, a distributed computing frame work is designed to carry out the computation for this method. This chance-constrained optimization method and its computation platform are potentially helpful in their applica tion to several other domains in air transportation, such as airport surface operations and airline management under uncertainties.
Keywords: Air traffic flow management | Chance-constrained optimization | Bernstein polynomial
مقاله انگلیسی
6 Static force capability optimization of humanoids robots based on modified self-adaptive differential evolution
بهینه سازی قابلیت نیروی استاتیک از ربات های انسان نما بر مبنای تکامل تفاضلی خودساخته ی اصلاح شده-2017
Article history:Received 7 November 2015Revised 20 October 2016Accepted 24 October 2016Available online 27 October 2016Keywords:Optimization metaheuristic Differential evolution Constrained optimization Humanoid robotStatic force capabilityThe current society requires solutions for many problems in safety, economy, and health. The social con- cerns on the high rate of repetitive strain injury, work-related osteomuscular disturbances, and domestic issues involving the elderly and handicapped are some examples. Therefore, studies on complex machines with structures similar to humans, known as humanoids robots, as well as emerging optimization meta- heuristics have been increasing. The combination of these technologies may result in robust, safe, reliable, and flexible machines that can substitute humans in multiple tasks. In order to contribute to this topic, the static modeling of a humanoid robot and the optimization of its static force capability through a modified self-adaptive differential evolution (MSaDE) approach is proposed and evaluated in this study. Unlike the original SaDE, MSaDE employs a new combination of strategies and an adaptive scaling factor mechanism. In order to verify the effectiveness of the proposed MSaDE, a series of controlled experiments are performed. Moreover, some statistical tests are applied, an analysis of the results is carried out, and a comparative study of the MSaDE performance with other metaheuristics is presented. The results show that the proposed MSaDE is robust, and its performance is better than other powerful algorithms in the literature when applied to a humanoid robot model for the pushing and pulling tasks.© 2016 Elsevier Ltd. All rights reserved.
Keywords: Optimization metaheuristic | Differential evolution | Constrained optimization | Humanoid robot | Static force capability
مقاله انگلیسی
7 Traffic-aware Geo-distributed Big Data Analytics with Predictable Job Completion Time
تحلیل داده بزرگ توزیع شده جغرافیایی ترافیک آگاه با زمان تکمیل وطیفه قابل پیش بینی-2016
Big data analytics has attracted close attention from both industry and academic because of its great benefits in cost reduction and better decision making. As the fast growth of various global services, there is an increasing need for big data analytics across multiple data centers (DCs) located in different countries or regions. It asks for the support of a cross-DC data processing platform optimized for the geo-distributed computing environment. Although some recent efforts have been made for geo-distributed big data analytics, they cannot guarantee predictable job completion time, and would incur excessive traffic over the inter-DC network that is a scarce resource shared by many applications. In this paper, we study to minimize the inter-DC traffic generated by MapReduce jobs targeting on geo-distributed big data, while providing predicted job completion time. To achieve this goal, we formulate an optimization problem by jointly considering input data movement and task placement. Furthermore, we guarantee predictable job completion time by applying the chance-constrained optimization technique, such that the MapReduce job can finish within a predefined job completion time with high probability. To evaluate the performance of our proposal, we conduct extensive simulations using real traces generated by a set of queries on Hive. The results show that our proposal can reduce 55% inter-DC traffic compared with centralized processing by aggregating all data to a single data center.
Index Terms: Big data | geo-distributed | MapReduce | traffic-aware
مقاله انگلیسی
8 Reliability-driven scheduling of time/cost-constrained grid workflows
برنامه ریزی قابلیت اطمینان رانده زمان / هزینه محدود گردش شبکه-2016
Article history:Received 9 August 2014 Received in revised form 24 May 2015Accepted 27 July 2015Available online 5 August 2015Keywords:Workflow scheduling ReliabilityReal-time systems Grid computingConstrained optimization Ant colony optimizationWorkflow scheduling in Grids and Clouds is a NP-Hard problem. Constrained workflow scheduling, arisen in recent years, provides the description of the user requirements through defining constraints on factors like makespan and cost. This paper proposes a scheduling algorithm to maximize the workflow execu- tion reliability while respecting the user-defined deadline and budget. We have used ant colony system to minimize an aggregation of reliability and constraints violation. Three novel heuristics have been pro- posed which are adaptively selected by ants. Two of them are employed to find feasible schedules and the other is used to enhance the reliability. Two methods have been investigated for time and cost considera- tions in the resource selection. One of them assigns equal importance to the time and cost factors, and the other weighs them according to the tightness of satisfaction of the corresponding constraints. Simulation results demonstrate the effectiveness of the proposed algorithm in finding feasible schedules with high reliability. As it is shown, as an additional achievement, the Grid profit loss has been decreased.© 2015 Elsevier B.V. All rights reserved.
Workflow scheduling | Reliability | Real-time systems | Grid computing | Constrained optimization | Ant colony optimization
مقاله انگلیسی
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