The insertion of a payload into orbit is a very complex and costly activity. Therefore, the best performance of the launch vehicle is important for each launch. To achieve this goal, usually the vehicle trajectory is ...
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The insertion of a payload into orbit is a very complex and costly activity. Therefore, the best performance of the launch vehicle is important for each launch. To achieve this goal, usually the vehicle trajectory is determined via an optimization process which results in the maximum payload mass that can be inserted into orbit or, equivalently, the minimum propellant expenditure to achieve orbit. This is a specially complex problem belonging to the general class of optimal control problems. This work investigates the trajectory optimization of a multistage launch vehicle. The optimal control problem is transformed into a nonlinear programming problem with the use of two different transcription methods: Hermite-Simpson collocation and multiple shooting. To solve the resulting parameter optimization problem, the gradient-based algorithm called sequential conjugate gradient-restoration algorithm is used, and an extension of the algorithm is proposed which enhances its applicability to more general optimization problems. The proposed algorithm is used to optimize the trajectory of the Brazilian microsatellite launcher VLM-1, in missions with different complexities. To validate the methodology, the results are compared with those obtained with a commercial optimization tool.
Unit Commitment (UC) and Optimal Power Flow (OPF) are two fundamental problems in short-term electric power systems planning that are traditionally solved sequentially. The state-of-the-art mostly uses a direct curren...
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Unit Commitment (UC) and Optimal Power Flow (OPF) are two fundamental problems in short-term electric power systems planning that are traditionally solved sequentially. The state-of-the-art mostly uses a direct current (DC) approximation of the power flow equations. However, utilizing the DC approach in the UC-level may lead to infeasible or suboptimal generator commitment schedules for the OPF problem. In this paper, we aim to simultaneously solve the UC Problem with alternating current (AC) power flow equations, which combines the challenging nature of both UC and OPF Problems. Due to the highly nonconvex nature of the AC flow equations, we utilize the mixed-integer second-order cone programming (MISOCP) relaxation of the UC Problem as the basis of our solution approach. The MISOCP relaxation is utilized for finding both a lower bound and a candidate generator commitment schedule. Once this schedule is obtained, we solve a multi-period OPF problem to obtain feasible solutions for the UC problem with AC power flows. For smaller instances, we develop two different algorithms that exploit the recent advances in the OPF literature and obtain high-quality feasible solutions with provably small optimality gaps. For solving larger instances, we develop a Lagrangian decomposition based approach that yields promising results.
In this work, a multi-period nonlinear programming formulation is presented to obtain the optimal long-term oilfield production planning, based on a two-phase, one-dimensional, and Cartesian-coordinated phenomenologic...
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In this work, a multi-period nonlinear programming formulation is presented to obtain the optimal long-term oilfield production planning, based on a two-phase, one-dimensional, and Cartesian-coordinated phenomenological reservoir model. The phenomenological model contains a set of second-order partial differential equations, which are approximated by a collocation on finite element method. This CFE method prevents mathematical stability limitations due to stiffness problems, resulting in an algebraic equation system added as an optimization set of constraints. This is a significant and innovating approach as there are only a handful of similar studies in the literature that integrate phenomenological models as mathematical constraints in the optimization problem. However, these works do not solve the model using long-term production planning coupled with a simultaneous strategy. Also, formulation applied to two study cases allowed solving the optimization problem within an adequate time without requiring a high-performance computing platform. Results show the economic impact of simultaneously considering the constraints and the state variables evolution throughout the reservoir's life span to obtain the optimal long-term production planning.
This article aimed to develop a model for managing multiple uses of water to minimize conflicts of use related to the operation of reservoir systems in hydroelectric watersheds. The MUWOS model - Multiple Uses Water O...
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This article aimed to develop a model for managing multiple uses of water to minimize conflicts of use related to the operation of reservoir systems in hydroelectric watersheds. The MUWOS model - Multiple Uses Water Optimization System, which consists of an optimization model based on non-linear programming, developed and structured in GAMS (General Algebraic Modeling System) using the MINOS solver, for conflict mitigation and minimization from the modeling and operational optimization for different navigation and hydrological sce-narios. The MUWOS was applied to Tapaj acute accent os watershed for the future Hydropower Project - HPP Sa tilde o Luiz do Tapaj acute accent os, Itaituba, state of Par acute accent a, Brazil. For power generation and navigation water depth, considering inflows for dry, medium and wet hydrological scenarios, MUWOS showed, in relation to reference levels for the low, me-dium and high navigation scenarios, water depths dropped below the minimum for average generations of 2411 MW;2939 MW and 3586 MW, respectively. For energy generation and transported cargo capacity, considering the same hydrological scenarios, MUWOS demonstrated that, in relation to the reference levels of the low, medium and high navigation scenarios, average generations above 2869 MW;3508 MW and 4740 MW, respectively, result in no gains in transported cargo capacity, and average generations below 1344 MW;1622 MW, and 2056 MW, respectively, make cargo transport unfeasible. The developed model represents a tool of fundamental importance for the operational optimization of reservoir systems with multiple uses, allowing the optimization of generation and outflow in HPPs, with the maintenance of navigability conditions downstream of dams.
Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effec...
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Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.
Efficient Water Distribution Systems (WDSs) are crucial for modern society. Their operation requires large amounts of energy with significant financial impact for the utility providers. Existing solution methods are o...
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Efficient Water Distribution Systems (WDSs) are crucial for modern society. Their operation requires large amounts of energy with significant financial impact for the utility providers. Existing solution methods are often oversimplified and can only solve the problem for very small, schematic networks. This article studies a pumping scheduling problem for WDSs in which, besides scheduling the operation of pumps over a planning horizon, several constraints regarding hydraulic properties are considered. The goal is to provide a pumping plan of minimum cost that satisfies all demand and respects operational and hydraulic constraints. This work proposes a nonlinear and non-convex formulation as well as a high-performance heuristic. The physical hydraulic behaviour is ensured via hydraulic simulation software. The present method significantly improved the best solutions for several benchmark instances by up to 17%. The solutions also reduce the energy consumed during peak periods, when the electrical grid is most strained.
Product-service system (PSS) has attracted attention of manufacturers to shift from product-providing to solutionproviding, which is a marketable set of products and services. The existing researches emphasize the ful...
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Product-service system (PSS) has attracted attention of manufacturers to shift from product-providing to solutionproviding, which is a marketable set of products and services. The existing researches emphasize the fulfillment of individualized customer requirements through different PSS configurations. The PSS planning phase is of high importance in generating conceptual schemes, which translates customer requirements (CRs) to design requirements (DRs). In this paper, a systematic decision-making approach based on QFD is put forward aiming to configure the PSS design requirements (DRs). To address the uncertainty and hesitancy in QFD modeling, a hesitant fuzzy linguistic term sets (HFLTSs) is applied to elicit the experts' linguistic preferences in evaluating the importance of CRs and the relationships between CRs and DRs. To dealing with the group decision-making problems concerning the HFLTSs, the min-upper operator and the max-lower operator assemble the experts' evaluation results into a linguistic interval, and then the numerical results can be obtained by using the 2-tuple linguistic representation model and the interval preference degree computation. A non-linear 0-1 programming model is proposed to select the target DRs' specifications for maximizing customer satisfaction under cost constraint. In order to objectively determine the satisfaction degree of each optional specification of DR, the information axiom is introduced to construct the objective function via information content computation. To deal with the qualitative DRs, HFLTSs and information axiom are combined and hesitant information axiom (HIA) is proposed. Finally, a DRs optimization model is established using HIA and the imprecision method. A case study is carried out to demonstrate the effectiveness of the optimal PSS planning approach developed.
To strengthen the three-term Hestenes-Stiefel conjugate gradient method proposed by Zhang et al., we suggest a modified version of it. For this purpose, by considering the Dai-Liao approach, the third term of Zhang et...
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To strengthen the three-term Hestenes-Stiefel conjugate gradient method proposed by Zhang et al., we suggest a modified version of it. For this purpose, by considering the Dai-Liao approach, the third term of Zhang et al. method is multiplied by a positive parameter which can be determined adaptively. To render an appropriate choice for the parameter of the search direction, we carry out a matrix analysis by which the sufficient descent property of the method is guaranteed. In the following, convergence analyses are discussed for convex and nonconvex cost functions. Eventually, numerical tests shed light on the efficiency of the performance of the proposed method.
Various optimization problems result from the introduction of nonlinear terms into combinatorial optimization problems. In the context of energy optimization for example, energy sources can have very different charact...
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Various optimization problems result from the introduction of nonlinear terms into combinatorial optimization problems. In the context of energy optimization for example, energy sources can have very different characteristics in terms of power range and energy demand/cost function, also known as efficiency function or energy conversion function. Introducing these energy sources characteristics in combinatorial optimization problems, such as energy resource allocation problems or energy-consuming activity scheduling problems may result into mixed integer nonlinear problems neither convex nor concave. Approximations via piecewise linear functions have been proposed in the literature. Non-convex optimization models and heuristics exist to compute optimal breakpoint positions under a bounded absolute error-tolerance. We present an alternative solution method based on the upper and lower bounding of nonlinear terms using non necessarily continuous piecewise linear functions with a relative epsilon-tolerance. Conditions under which such approach yields a pair of mixed integer linear programs with a performance guarantee are analyzed. Models and algorithms to compute the non necessarily continuous piecewise linear functions with absolute and relative tolerances are also presented. Computational evaluations performed on energy optimization problems for hybrid electric vehicles show the efficiency of the method with regards to the state of the art. (C) 2018 Elsevier B.V. All rights reserved.
The aim of this paper is to compare the performance of a local solution technique-namely Sequential Linear programming (SLP) employing random starting points-with state-of-the-art global solvers such as Baron and more...
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The aim of this paper is to compare the performance of a local solution technique-namely Sequential Linear programming (SLP) employing random starting points-with state-of-the-art global solvers such as Baron and more sophisticated local solvers such as Sequential Quadratic programming and Interior Point for the pooling problem. These problems can have many local optima, and we present a small example that illustrates how this can *** demonstrate that SLP-usually deemed obsolete since the arrival of fast reliable SQP solvers, Interior Point Methods and sophisticated global solvers-is still the method of choice for an important class of pooling problems when the criterion is the quality of the solution found within a given acceptable time budget. On this measure SLP significantly ourperforms all other tested *** addition we introduce a new formulation, the qq-formulation, for the case of fixed demands, that exclusively uses proportional variables. We compare the performance of SLP and the global solver Baron on the qq-formulation and other common formulations. While Baron with the qq-formulation generates weaker bounds than with the other formulations tested, for both SLP and Baron the qq-formulation finds the best solutions within a given time budget. The qq-formulation can be strengthened by pq-like cuts in which case the same bounds as for the pq-formulation are obtained. However the associated time penalty due to the additional constraints results in poorer solution quality within the time budget.
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