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mixed integer optimization for cyclic scheduling of multiproduct plants under exponential performance decay

CHEMICAL ENGINEERING RESEARCH & DESIGN 2005年第A10期83卷 1208-1217页

作者： Casas-Liza, J Pinto, JM Papageorgiou, LG Polytech Univ Othmer Dept Chem & Biol Sci & Engn Brooklyn NY 11201 USA Univ Sao Paulo Dept Chem Engn Sao Paulo Brazil UCL Dept Chem Engn Ctr Proc Syst Engn London England

The aim of this work is to propose a mathematical programming model for the economic lot scheduling problem (ELSP) in units that present performance decay. Typical examples in the chemical processing industry include catalyst deactivation and cokeification, as well as fouling in heat exchangers. The main decision related to this problem concerns processing interruption for maintenance so to restore unit performance. The dynamic behaviour of the system along time is represented by an exponential decay function. The problem is formulated as a mixed integer nonlinear programme (MINLP). Moreover, a mixed integer linear programme (MILP) that results from the discretization of cycle time is developed. The models are tested in a wide set of problems with several parameter values and the results for the models with continuous and discrete cycle times are compared. Results show that the MINLP model achieves the optimal solutions in shorter CPU times. Nevertheless, the usefulness of the MILP is shown as it provides tight bounds that provide convergence of the MINLP. Moreover, the formulations are compared to hierarchical approaches.

关键词： mixed integer optimization economic lot scheduling problem exponential performance decay cyclic scheduling

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mixed integer optimization FOR TRUSS TOPOLOGY DESIGN PROBLEMS AS A DESIGN TOOL FOR AM COMPONENTS 2

MIXED INTEGER OPTIMIZATION FOR TRUSS TOPOLOGY DESIGN PROBLEM...

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2nd International Conference on Simulation for Additive Manufacturing (Sim-AM)

作者： Reintjes, Christian Lorenz, Ulf Univ Siegen Inst Technol Management Unteres Schloss 3 D-57072 Siegen Germany

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Novel mixed integer optimization sparse regression approach in chemometrics

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ANALYTICA CHIMICA ACTA 2020年 1137卷 115-124页

作者： Bertsimas, D. Kitane, D. Lahlou Azami, N. Doucet, F. R. MIT Operat Res Ctr Cambridge MA 02139 USA Mohammed VI Polytech Univ Ben Guerir Morocco ELEMISSION Inc Montreal PQ Canada

Sparse mathematical modelling plays an increasingly important role in chemometrics due to its interpretability and prediction power. While many sparse techniques used in chemometrics rely on L-1 penalization to create sparser models, mixed integer optimization (MIO) achieves sparsity by imposing constraints directly in the model. In this paper, we develop an intuitive and flexible robust sparse regression framework using MIO. We use constraints and penalization to achieve sparsity and robustness respectively. We test and compare results with those obtained using other techniques generating sparser models such as LASSO and sparse PLS. We also use PLS as a baseline to compare predictive performance. We use a LIBS data set of certified reference materials (CRM) of various mineral ores to illustrate the framework using different objective functions. The MIO framework proposed improves accuracy, sparsity and robustness vs. LASSO and SPLS. MIO achieves an average R-2 higher than other methods on average by at least 10.6%. Robust MIO approach also improves interpretability. It also uses 4.3 variables on average while LASSO and SPLS use 16.1 and 805.8 respectively. We also illustrate how interpretability can help build better models through examples derived from the data sets used. When adding noise to the signal, MIO achieves an R-2 of 0.69 on average when all models have negative R-2 values. The MIO framework proposed is versatile and could be used in other chemometrics applications. (C) 2020 Elsevier B.V. All rights reserved.

关键词： mixed integer optimization LIBS Spectroscopy Quadratic optimization Robust optimization LASSO Partial least squares

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A mixed integer optimization formulation for the well scheduling problem on petroleum fields

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COMPUTERS & CHEMICAL ENGINEERING 2005年第7期29卷 1523-1541页

作者： Kosmidis, VD Perkins, JD Pistikopoulos, EN Univ London Imperial Coll Sci Technol & Med Dept Chem Engn Ctr Proc Syst Engn London SW7 2AZ England

In this paper, we present a novel mixed integer nonlinear (MINLP) model for the daily well scheduling in petroleum fields, where the nonlinear reservoir behaviour, the multiphase flow in wells and constraints from the surface facilities are simultaneously considered. Discrete decisions include the operational status of wells (open or closed), the allocation of wells to manifolds or separators and the allocation of flow lines to separators. Continuous decisions include the well oil rates and the allocation of gas-to-gas lift wells. A solution strategy is proposed involving (i) logic constraints, (ii) inclusion of piecewise linear approximations of each well model and (iii) an outer approximation based algorithm. A number of examples illustrate the applicability of the proposed model and solution strategy where an increase in oil production up to 10% is observed when the results of the proposed method are compared with typical heuristic rules widely applied in practice. (c) 2004 Elsevier Ltd. All rights reserved.

关键词： well scheduling mixed integer optimization oilfields production optimization

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Feature subset selection for logistic regression via mixed integer optimization

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COMPUTATIONAL optimization AND APPLICATIONS 2016年第3期64卷 865-880页

作者： Sato, Toshiki Takano, Yuichi Miyashiro, Ryuhei Yoshise, Akiko Univ Tsukuba Grad Sch Syst & Informat Engn 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan Senshu Univ Sch Network & Informat Tama Ku 2-1-1 Higashimita Kawasaki Kanagawa 2148580 Japan Tokyo Univ Agr & Technol Inst Engn 2-24-16 Naka Cho Koganei Tokyo 1848588 Japan Univ Tsukuba Fac Engn Informat & Syst 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan

This paper concerns a method of selecting a subset of features for a logistic regression model. Information criteria, such as the Akaike information criterion and Bayesian information criterion, are employed as a goodness-of-fit measure. The purpose of our work is to establish a computational framework for selecting a subset of features with an optimality guarantee. For this purpose, we devise mixed integer optimization formulations for feature subset selection in logistic regression. Specifically, we pose the problem as a mixed integer linear optimization problem, which can be solved with standard mixed integer optimization software, by making a piecewise linear approximation of the logistic loss function. The computational results demonstrate that when the number of candidate features was less than 40, our method successfully provided a feature subset that was sufficiently close to an optimal one in a reasonable amount of time. Furthermore, even if there were more candidate features, our method often found a better subset of features than the stepwise methods did in terms of information criteria.

关键词： Logistic regression Feature subset selection mixed integer optimization Information criterion Piecewise linear approximation

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Design of Active Distribution Network Voltage Regulation based on Two-layer mixed integer optimization 2

Design of Active Distribution Network Voltage Regulation bas...

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IEEE 2nd International Conference on Power Science and Technology (ICPST)

作者： Cai, Xinlei Kan, YiMeng Zhu, Jinzhou Wu, Longteng Guangdong Power Grid Co Ltd Power Dispatching & Controlling Ctr Guangzhou Peoples R China Harbin Inst Technol Sch Elect Engn Harbin Peoples R China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047;9798350349030

With the vigorous development of distributed new energy generation, the power network is transitioning from a traditional centralized unidirectional power flow to the active distribution network containing many controllable sources. The voltage fluctuations caused by the intermittent nature of renewable energy generation sources will cause the deterioration of power quality. In this paper, we propose a voltage regulation strategy based on two-layer mixed integer optimization for enhancing the energy management system of microgrids. Based on the sum of squared deviations and the net load evaluation, the regulator parameters are optimized using a two-layer structure to achieve systematic voltage regulation for active distribution grids with controllable sources. Simulation experiments on an IEEE 33 distribution network model containing 6 photovoltaics reveal that the proposed method is able to complete the control design process of the active distribution network based on quantitative information, and to accurately identify and evaluate different control structures.

关键词： active distribution network distributed generation voltage control mixed integer optimization

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Extension and Performance Evaluation of Recent optimization Techniques on mixed integer optimization Problems

Extension and Performance Evaluation of Recent Optimization ...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Punnathanam, Varun Kommadath, Remya Kotecha, Prakash Indian Inst Technol Dept Chem Engn Gauhati India

ISBN: (纸本)9781509006229

In this work, we extend three recently developed computational intelligence techniques, viz., Ying-Yang Pair optimization, Moth-flame optimization, and Grey Wolf optimization for real variable optimization problems to solve mixed integer optimization problems. As these techniques are developed primarily for continuous variables, three strategies to handle the integer variables are considered in this article thereby leading to 9 variants. These variants are evaluated on ten unique runs of eighteen mixed integer constrained optimization problems and their performance is evaluated under equal functional evaluations.

关键词： mixed integer optimization Grey Wolf optimization Moth-flame optimization Ying-Yang Pair optimization

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A mixed integer optimization method with double penalties for the complete consumption of renewable energy in distributed energy systems

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartA期52卷

作者： Huang, Dawen Zhou, Dengji Jia, Xingyun Yan, Siyun Li, Taotao Huang, Di Zhang, Chenyu Shanghai Jiao Tong Univ Key Lab Power Machinery & Engn Educ Minist Shanghai 200240 Peoples R China State Grid Jiangsu Elect Power Co Ltd Nanjing 211106 Peoples R China State Grid Jiangsu Elect Power Co Ltd Res Inst Nanjing Peoples R China

The flexible distributed energy system is an important strategic choice for all countries to develop the efficient consumption technology of abundant renewable energy. However, renewable energy with the obvious peakvalley, intermittence and randomness characteristics increases the difficulty of coordinately scheduling distributed units. This work proposes a mixed integer optimization model through designing a continuation method for integer decision variables and a constraint strategy with double penalties to ensure that the optimal start-stop states and optimal operating power level of distributed units are accurately configured under the premise of completely consuming renewable energy. A standard paradigm of modeling and operating optimization is further established for complex distributed energy systems to completely consume renewable energy, which is not limited to the system scale and the number of integer decision variables. The analysis results indicate that the proposed model simultaneously ensures the optimal start-stop states and operating power of distributed units. Compared with other methods, the proposed method reduces the switching times of start-stop states by 50% at least and improves the smoothness of operating power by 1.9%, moreover, it reduces the operating cost by 1.26% at least and saves the computing time by 79.2%. The proposed mixed integer optimization method has higher stability and robustness. It provides a reliable technology for the optimal operation of distributed energy systems, the flexible configuration of distributed units, and the complete consumption of high-proportion renewable energy.

关键词： Distributed energy systems Renewable energy consumption Energy storage mixed integer optimization Penalty functions

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Robust sensor placement for pipeline monitoring: mixed integer and greedy optimization

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ADVANCED ENGINEERING INFORMATICS 2018年 36卷 55-63页

作者： Sela, Lina Amin, Saurabh UT Austin Civil Architectural & Environm Engn Austin TX 78712 USA MIT Civil & Environm Engn Cambridge MA 02139 USA

Intelligent water systems aided by sensing technologies have been identified as an important mechanism towards ensuring the resilience of urban systems. In this work, we study the problem of sensor placement that is robust to intermittent failures of sensors, i.e. sensor interruptions. We propose robust mixed integer optimization (RMIO) and robust greedy approximation (RGA) solution approaches. The underlying idea of both approaches is to promote solutions that achieve multiple detectability of events, such that these events remain detectable even when some sensors are interrupted. Additionally, we apply a previously proposed greedy approximation approach for solving the robust submodular function optimization (RSFO) problem. We compare scalability of these approaches and the quality of the solutions using a set of real water-networks. Our results demonstrate that considering sensor interruptions in the design stage improves sensor network performance. Importantly, we ford that although the detection performances of RMIO and RGA approaches are comparable, RMIO generally has better performance than RGA, and is scalable to large-scale networks. Furthermore, the results demonstrate that RMIO and RGA approaches tend to outperform the RSFO approach.

关键词： Robust sensor placement mixed integer optimization Submodular optimization

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Binary Search Algorithm for mixed integer optimization: Application to energy management in a microgrid.

Binary Search Algorithm for Mixed Integer Optimization: Appl...

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European Control Conference

作者： Paulo R. C. Mendes José M. Maestre Carlos Bordons Julio E. Normey-Rico Department of Automation and Systems Federal University of Santa Catarina Florianpolis Brazil Departamento de Ingeniería de Sistemas y Automática University of Seville Seville Spain

ISBN: (纸本)9781509025923

This paper presents a binary search algorithm to deal with binary variables in mixed integer optimization problems. One example of this kind of problem is the optimal operation of hydrogen storage and energy sale and purchase into a microgrids context. In this work was studied a system composed by a microgrid that has a connection with the external electrical network and a charging station for electric cars. The system modeling was carried out by the Energy Hubs methodology. The proposed algorithm transforms the MIQP (mixed integer Quadratic Program) problem into a QP (Quadratic Program) that is easier to solve. In this way the overall control task is carried out the electricity purchase and sale to the power grid, maximizes the use of renewable energy sources, manages the use of energy storages and supplies the charge of the parked vehicles.

关键词： MIQP Binary Search Algorithm mixed integer optimization

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