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作者： Tso, William Weikang Texas A&M University

学位级别：Ph.D., Doctor of Philosophy

Intensifying public concern about climate change risks has accelerated the push for more tangible action in the transition toward low-carbon or carbon-neutral energy. Concurrently, the energy industry is also undergoing a digital transformation with the explosion in available data and computational power. To address these challenges, systematic decision-making strategies are necessary to analyze the vast array of technology options and information sources while navigating this energy transition. In this work, mathematical optimization is utilized to answer some of the outstanding issues around designing cleaner processes from resources such as natural gas and renewables, operating the logistics of these energy systems, and statistical modeling from data. First, exploiting natural gas to produce lower emission liquid transportation fuels is investigated through an optimization-based process synthesis. This extends previous studies by incorporating chemical looping as an alternative syngas production method for the first time. Second, a similar process synthesis approach is implemented for the optimal design of a novel biomass-based process that coproduces ammonia and methanol, improving their production flexibility and profit margins. Next, operational difficulties with solar and wind energies due to their temporal intermittency and uneven geographical distribution are tackled with a supply chain optimization model and a clustering decomposition algorithm. The former describes power generation through energy carriers (hydrogen-rich chemicals) connecting resource-dense rural areas to resource-deficient urban centers. Results show the potential of energy carriers for long-term storage. The latter is developed to identify the appropriate number of representative time periods for approximating an optimization problem with time series data, instead of using a full time horizon. This algorithm is applied to the simultaneous design and scheduling of a renewable power system

关键词： Process systems engineering Mathematical optimization Process synthesis Capacity expansion Unit commitment Design and scheduling Supply chain optimization parametric programming Hyperparameter tuning Natural gas Niomass Solar Wind Energy storage Battery Hydrogen Ammonia Methanol Liquid transportation fuels Chemical looping

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BILEVEL OPTIMIZATION PROBLEMS OF DISTRIBUTION OF INTERBUDGETARY TRANSFERS UNDER GIVEN LIMITATIONS

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CYBERNETICS AND SYSTEMS ANALYSIS 2019年第6期55卷 905-913页

作者： Sergienko, I. V. Semenova, N. V. Semenov, V. V. Natl Acad Sci Ukraine VM Glushkov Inst Cybernet Kiev Ukraine

The problems of optimal distribution of transfers within given budget limitations are formulated and analyzed. The mathematical model is presented as a bilevel linear optimization problem that contains linear problems of integer optimization at the lower level. Both optimistic and pessimistic versions of the problem are considered. Far the approximate solution of optimistic version, the algorithm of finding local solutions for parametric lower-level integer programming problems on the basis of the method of directing neighborhoods is proposed. The auxiliary integer progrannning problem with Boolean variables of a higher level is solved bused on local algorithms.

关键词： bilevel optimization problem integer optimization parametric programming Boolean variables local algorithm

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Linear Optimization in Uncertain Environment: Sensitivity of the Solution to the Belief Degree

Journal of Uncertain Systems

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Journal of Uncertain Systems 2021年第1期14卷

作者： Ghaffari-Hadigheh, Alireza Department of Applied Mathematics Azarbaijan Shahid Madani University Tabriz Iran

Uncertainty theory has been initiated in 2007 by Liu, as an axiomatically developed notion, which considers the uncertainty on data as a belief degree on the domain expert's opinion. Uncertain linear optimization is devised to model linear programs in an uncertain environment. In this paper, we investigate the relation between uncertain linear optimization and parametric programming. It is denoted that the problem can be converted to parametric linear optimization problem, at which belief degrees play the role of parameters, and parametric linear optimization with its rich literature provides insightful interpretations. In a point of view, a strictly complementary optimal solution of problem is known for the belief degree α = 0, as well as the associated optimal partition. One may be interested in knowing the region of belief degrees (parameters) where this optimal partition remains invariant for all parameter values (belief degrees) in this region. We consider the linear optimization problem with uncertain rim data, i.e., the right-hand side and the objective function data. The known results in the literature are translated to the language of uncertainty theory, and managerial interpretations are provided. The methodology is illustrated via concrete examples. © 2021 World Scientific Publishing Company.

关键词： belief degree linear optimization optimal partition parametric programming Uncertainty theory

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Dual Prediction-Correction Methods for Linearly Constrained Time-Varying Convex Programs

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第8期64卷 3355-3361页

作者： Simonetto, Andrea IBM Res Ireland Optimizat & Control Grp Dublin 15 Ireland

Devising efficient algorithms to solve continuously-varying strongly convex optimization programs is key in many applications, from control systems to signal processing and machine learning. In this context, solving means to find and track the optimizer trajectory of the continuously-varying convex optimization program. Recently, a novel prediction-correction methodology has been put forward to set up iterative algorithms that sample the continuously-varying optimization program at discrete time steps and perform a limited amount of computations to correct their approximate optimizer with the new sampled problem and predict how the optimizer will change at the next time step. Prediction-correction algorithms have been shown to outperform more classical strategies, i.e., correction-only methods. Typically, prediction-correction methods have asymptotical tracking errors of the order of h(2) , where h is the sampling period, whereas classical strategies have order of h. Up to now, prediction-correction algorithms have been developed in the primal space, both for unconstrained and simply constrained convex programs. In this paper, we show how to tackle linearly constrained continuously-varying problem by prediction-correction in the dual space and we prove similar asymptotical error bounds as their primal versions.

关键词： Dual ascent parametric programming prediction-correction methods time-varying convex optimization

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Explicit spline-based continuous-time MPC: a study on design and performance

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IFAC-PapersOnLine 2020年第2期53卷 7013-7019页

作者： Boris Rohaľ-Ilkiv Martin Gulan Peter Minarčík Institute of Automation Measurement and Applied Informatics Faculty of Mechanical engineering Slovak University of Technology in Bratislava Námestie slobody 17 812 31 Bratislava Slovakia

This paper presents a continuous-time model predictive control scheme based on B-spline functions used for signals and model approximation. The proposed controller offers two interesting advantages. First, it formulates the control signal as a continuous polynomial spline function, the nature of which is determined by its control polygon that is subject of optimization. Second, all continuous constraints assumed over prediction horizon are consistently transformed into constraints imposed on a finite number of elements of this control polygon. Using parametric quadratic programming we further show how to obtain an explicit representation of the proposed controller, which is known for its efficient online implementation. The featured simulation study demonstrates that by a suitable choice of number and position of knots of the spline function over the prediction horizon it is possible to substantially reduce the number of critical regions of the explicit controller while preserving control performance, and to mitigate the direct correlation between number of regions and chosen length of prediction horizon.

关键词： explicit model predictive control parametric programming linear systems B-spline functions continuous-time control efficient implementation

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An Effective Solution Approach to Fuzzy programming with Application to Project Scheduling

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2018年第8期20卷 2383-2398页

作者： Zhang, Qianyu Zhou, Jian Wang, Ke Pantelous, Athanasios A. Shanghai Univ Sch Management Shanghai 200444 Peoples R China Monash Univ Dept Econometr & Business Stat Monash Business Sch 20 Chancellors WalkWellington RdClayton Campus Melbourne Vic 3800 Australia

In the present paper, a novel solution approach is proposed for a class of fuzzy programming models. In this direction, we prove that the lower and upper bounds of their fuzzy optimal objective values at a possibility level, a 2 0;1 , can be calculated solving a pair of crisp mathematical programming models. Thus, their membership functions are simulated enumerating the upper and lower bounds obtained from a series of a-level calculations. Motivated from real-world project scheduling applications, several numerical examples are considered to evaluate the accuracy, efficiency and applicability of our treatment. The results derived are compared with those of Chen and Tsai (Eur J Oper Res 212(2): 386-397, 2011) to demonstrate that our method is easier to be utilized, with lower computational complexity, nonetheless without losing its effectiveness. Finally, the new approach does not suffer from the limitation to be applicable only to linear programming models that makes it suitable for a wider range of models and applications.

关键词： Fuzzy programming Solution approach Project scheduling parametric programming

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Accommodating Renewable Energy Sources into the Modern Power System via a General Coordinated Mechanism

Accommodating Renewable Energy Sources into the Modern Power...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Yu, Jing Guo, Ye Sun, Hongbin Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Environm Sci & New Energy Technol Engn L Shenzhen Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing Peoples R China

ISBN: (纸本)9781728119816

In order to better accommodate renewable energy sources (RESs), a general coordinated mechanism for modern power system has been proposed, which is simultaneously appropriate for economic dispatch and market clearing under the current structure of wholesale markets. In which, the system operator can achieve a global optimum under uncertainties by investing independent aggregators (IAs) with better autonomies on trade-offs between expected profits and risks. Meanwhile, IAs with RESs are free to model their internal uncertainty in a heterogeneous manner and integrate the local information into tailored bids without revealing privacy. Moreover, a parametric-programming-based distributed (PPD) algorithm is designed to support the once-daily communication mechanism. The case studies validated the performance of the proposed mechanism.

关键词： renewable energy sources parametric programming electricity market

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Valuating residential real estate using parametric programming

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第1期217卷 120-128页

作者： Narula, Subhash C. Wellington, John F. Lewis, Stephen A. Indiana Univ Purdue Univ Ft Wayne IPFW Ft Wayne IN 46805 USA Virginia Commonwealth Univ Richmond VA 23284 USA

When the estimation of the single equation multiple linear regression model is looked upon as an optimization problem, we show how the principles and methods of optimization can assist the analyst in finding an attractive prediction model. We illustrate this with the estimation of a linear prediction model for valuating residential property using regression quantiles. We make use of the linear parametric programming formulation to obtain the family of regression quantile models associated with a data set. We use the principle of dominance to reduce the number of models for consideration in the search for the most preferred property valuation model (s). We also provide useful displays that assist the analyst and the decision maker in selecting the final model (s). The approach is an interface between data analysis and operations research. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Linear programming parametric programming Real estate valuation Regression Regression quantiles

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Approximate multi-parametric programming based B&B algorithm for MINLPs

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COMPUTERS & CHEMICAL ENGINEERING 2012年 42卷 288-297页

作者： Gueddar, Taoufiq Dua, Vivek UCL Dept Chem Engn Ctr Proc Syst Engn London WC1E 7JE England

In this work an improved B&B algorithm for MINLPs is proposed. The basic idea of the proposed algorithm is to treat binary variables as parameters and obtain the solution of the resulting multi-parametric NLP (mp-NLP) as a function of the binary variables, relaxed as continuous variables, at the root node of the search tree. It is recognized that solving the mp-NLP at the root node can be more computationally expensive than exhaustively enumerating all the terminal nodes of the tree. Therefore, only a local approximate parametric solution, and not a complete map of the parametric solution, is obtained and it is then used to guide the search in the tree. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： parametric programming Branch & Bound MINLP

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Application of Custom Macro B high level CNC programming language in a five-axis milling machine for drilling holes distributed in axi-symmetric working planes

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Procedia Manufacturing 2019年 41卷 976-983页

作者： O. Rodriguez-Alabanda P.E. Romero G. Guerrero-Vaca Area of Manufacturing Processes - Department of Mechanics University of Córdoba Rabanales Campus Leonardo da Vinci Building Carretera Madrid-Cádiz km 396 E-14071 Córdoba. Spain

This paper describes a specific application of the Custom Macro B high level language which is within the scope of CNC programming. This type of programming language allows working with parametric part programs, based on the group technology, through which it is possible to save programming time and costs, facilitates the setting works in the machines implying less CN files also of a smaller size of them into the memory used in the CNC equipment. Small spring bars are commonly used to assemble the belt in a watchcase and the necessary drilling operation on the part shows the particularity that it is impossible to make the holes in the direction perpendicular to the plane to be drilled. There are CAD-CAM solutions for this specific work allowing to program inclined drills for its implementation in 4-axis and 5-axis milling machines. The proposed solution is the best choice and the most flexible to this specific case and it has been developed, simulated and evaluated for different part designs, showing a remarkable improvement from the point of view of flexibility and programming time with respect to two different CAD-CAM software tools commonly used as solution to this 5 axes CNC programming feature.

关键词： CNC programming parametric programming Fanuc custom macro Fanuc CNC CNC milling

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