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Economic optimization of combined cycle district heating systems

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2014年 7卷 91-100页

作者： Gopalakrishnan, Hariharan Kosanovic, Dragoljub Univ Massachusetts Dept Mech & Ind Engn 160 Governors Dr Amherst MA 01003 USA

This paper presents a methodology for economic optimization of combined cycle district heating systems. Heat and power requirements vary over 24 h periods due to changing weather conditions and consumer requirements. System thermal performance is highly dependent on ambient temperature and operating load, because individual component performances are nonlinear functions of these parameters. Since electric grid charges are much higher for on-peak than off-peak periods, on-site fuel choices vary in prices, and cheaper fuel availabilities are limited by suppliers, opportunities arise to optimally schedule system operation, and minimize total daily running cost. For such problems a mixed-integer nonlinear programming formulation is proposed. Limited fuel availability constraints make problem solving difficult using classical techniques such as the branch-and-bound method. As an alternative, a genetic algorithm is proposed in which a genetic search is applied only on integer variables and a gradient search is applied on continuous variables. A comparative study using actual system operation data shows optimal scheduling can reduce total daily running cost by 11% and improve system operating efficiency by 6%. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Economic optimization Combined cycle district heating mixed-integer nonlinear programming

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Co-evolutionary hybrid differential evolution for mixed-integer optimization problems

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ENGINEERING OPTIMIZATION 2001年第6期33卷 663-682页

作者： Lin, YC Hwang, KS Wang, FS Natl Chung Cheng Univ Dept Chem Engn Chiayi 621 Taiwan Natl Chung Cheng Univ Dept Elect Engn Chiayi 621 Taiwan

Evolutionary algorithms are promising candidates for obtaining the global optimum. Hybrid differential evolution is one of the evolutionary algorithms, which has been successfully applied to many real-world nonlinear programming problems. This paper proposes a co-evolutionary hybrid differential evolution to solve mixed-integer nonlinear programming (MIN-LP) problems. The key ingredients of the algorithm consist of an integer-valued variable evolution and a real-valued variable co-evolution, so that the algorithm can be used to solve MINLP problems or pure integer programming problems. Furthermore, the algorithm combines a local search heuristic (called acceleration) and a widespread search heuristic (called migration) to promote the search for a global optimum. Some numerical examples are tested to illustrate the performance of the proposed algorithm. Numerical examples show that the proposed algorithm converges to better solutions than the conventional MINLP optimization methods.

关键词： global optimization evolutionary algorithm mixed-integer nonlinear programming hybrid method

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A statistical learning approach to pasture, rangeland, forage (PRF) insurance coverage selection

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APPLIED ECONOMIC PERSPECTIVES AND POLICY 2024年第4期46卷 1429-1449页

作者： Zapata, Samuel D. Villavicencio, Xavier Xicay, Anderson Texas A&M Univ Dept Agr Econ Mcallen TX 78504 USA Texas A&M AgriLife Extens Serv Dept Agr Econ Weslaco TX USA

This study uses statistical learning methods to identify robust coverage alternatives for the Pasture, Rangeland, Forage (PRF) insurance program. Shrinkage and ensemble learning techniques are adapted to the context of the PRF coverage selection process. The out-of-sample performance of the proposed methods is evaluated on 116 representative grids throughout Texas during 2018-2022. Ensemble learning methods generated more stable coverage choices compared with the other selection strategies considered. Depending on the target return, a reduction in the prediction error between 5% and 14% was observed. Furthermore, the proposed coverages can provide a broader protection than current coverage choices made by farmers.

关键词： ensemble learning mixed-integer nonlinear programming portfolio analysis rainfall index shrinkage Texas

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A nonlinear model for a capacitated random transportation network

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2015年第8期32卷 500-515页

作者： Shiripour, Saber Mahdavi-Amiri, Nezam Mahdavi, Iraj Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Sharif Univ Technol Fac Math Sci Tehran Iran

In this study, we consider a capacitated location-multi-allocation-routing problem with population-dependent random travel times. The objective is to find appropriate locations as server locations among the candidate locations, allocate the existing population in each demand node to server locations, and determine the movement path of each member to reach its corresponding server with respect to the simultaneous change of the random travel times so that the expected total transportation time is minimized. In our study, the concept of population-dependent random travel times incurs two issues: (1) consideration of some random factors in computing the travel times and (2) impact of the traveling population (presence of people or vehicles) on these random factors simultaneously. Here, three random factors of the time spent in traffic, the number of accidents, and the number of road failures are considered. Also, the capacities of server nodes for servicing the people or vehicles and the capacities of arcs to pass the people or vehicles are assumed to be limited. Defining a linear function for population-dependent random travel time, we formulate the problem as a mixed-integer nonlinear programming model. Also, to investigate the validation and behavior of the proposed random model, several network examples are provided and computational results are analyzed.

关键词： population-dependent random travel times multi-allocation Poisson distribution function mixed-integer nonlinear programming

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Joint optimization of oil well placement and controls

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COMPUTATIONAL GEOSCIENCES 2012年第4期16卷 1061-1079页

作者： Bellout, Mathias C. Ciaurri, David Echeverria Durlofsky, Louis J. Foss, Bjarne Kleppe, Jon NTNU Dept Petr Engn & Appl Geophys Ctr Integrated Operat Petr Ind Trondheim Norway IBM Corp TJ Watson Res Ctr Dept Petr & Energy Analyt Yorktown Hts NY USA Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA NTNU Dept Engn Cybernet Ctr Integrated Operat Petr Ind Trondheim Norway

Well placement and control optimization in oil field development are commonly performed in a sequential manner. In this work, we propose a joint approach that embeds well control optimization within the search for optimum well placement configurations. We solve for well placement using derivative-free methods based on pattern search. Control optimization is solved by sequential quadratic programming using gradients efficiently computed through adjoints. Joint optimization yields a significant increase, of up to 20% in net present value, when compared to reasonable sequential approaches. The joint approach does, however, require about an order of magnitude increase in the number of objective function evaluations compared to sequential procedures. This increase is somewhat mitigated by the parallel implementation of some of the pattern-search algorithms used in this work. Two pattern-search algorithms using eight and 20 computing cores yield speedup factors of 4.1 and 6.4, respectively. A third pattern-search procedure based on a serial evaluation of the objective function is less efficient in terms of clock time, but the optimized cost function value obtained with this scheme is marginally better.

关键词： Oil production optimization Optimal oil well placement Simulation-based optimization Derivative-free optimization Adjoint methods mixed-integer nonlinear programming

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Continuous Piecewise Linear Delta-Approximations for Univariate Functions: Computing Minimal Breakpoint Systems

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2015年第2期167卷 617-643页

作者： Rebennack, Steffen Kallrath, Josef Colorado Sch Mines Div Econ & Business Golden CO 80401 USA Univ Florida Dept Astron Gainesville FL 32611 USA BASF SE Sci Comp GMC MS B009 D-67056 Ludwigshafen Germany

For univariate functions, we compute optimal breakpoint systems subject to the condition that the piecewise linear approximator, under-, and over-estimator never deviate more than a given -tolerance from the original function over a given finite interval. The linear approximators, under-, and over-estimators involve shift variables at the breakpoints allowing for the computation of an optimal piecewise linear, continuous approximator, under-, and over-estimator. We develop three non-convex optimization models: two yield the minimal number of breakpoints, and another in which, for a fixed number of breakpoints, the breakpoints are placed such that the maximal deviation is minimized. Alternatively, we use two heuristics which compute the breakpoints subsequently, solving small non-convex problems. We present computational results for 10 univariate functions. Our approach computes breakpoint systems with up to one order of magnitude less breakpoints compared to an equidistant approach.

关键词： Global optimization nonlinear programming mixed-integer nonlinear programming Non-convex optimization

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A mathematical model for assessing the effects of a lot splitting feature on a dynamic cellular manufacturing system

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PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT 2017年第4-5期11卷 557-573页

作者： Kazemi, Mohammad Gol, Shima Shafiee Tavakkoli-Moghaddam, Reza Kia, Reza Khorrami, Javad Birjand Univ Technol Dept Ind Engn Birjand Iran Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Islamic Azad Univ Firoozkooh Branch Dept Ind Engn Firoozkooh Iran Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran

Numerous studies have been carried out in the field of cellular manufacturing systems (CMS) by considering different types of production costs. In all the presented models, it has been assumed that either the production lot of a part type should be processed by only one machine or it can be split among several machines. To the best of our knowledge, there is no research considering the advantages and disadvantages of the lot splitting feature in designing a CMS under a dynamic environment. In this paper, a mixed-integer nonlinear programming model is formulated to design a dynamic CMS by considering the burdened costs of processing part operations, idleness of cells and machines, inter-cell movements, installation/uninstallation of machines, machine overhead, production lost, splitting production lots and dispersing machines among cells. Furthermore, the advantages and disadvantages of the lot splitting feature are investigated by regarding its effect on the burdened costs. After linearization, an illustrative numerical example is solved by GAMS software (CPLEX solver) to illustrate the model performance and analyze the effect of the lot splitting feature. Since the given problem is NP-hard, an efficient simulated annealing algorithm is developed and tested using several test problems.

关键词： Facilities planning and design Dynamic cellular manufacturing systems mixed-integer nonlinear programming Lot splitting effects Simulated annealing

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Convex quadratic relaxations for mixed-integer nonlinear programs in power systems

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MATHEMATICAL programming COMPUTATION 2017年第3期9卷 321-367页

作者： Hijazi, Hassan Coffrin, Carleton Van Hentenryck, Pascal Australian Natl Univ NICTA CSIRO Data61 Decis Sci 7 London Circuit Canberra ACT 2601 Australia Los Alamos Natl Lab POB 1663 Los Alamos NM 87545 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

This paper presents a set of new convex quadratic relaxations for nonlinear and mixed-integer nonlinear programs arising in power systems. The considered models are motivated by hybrid discrete/continuous applications where existing approximations do not provide optimality guarantees. The new relaxations offer computational efficiency along with minimal optimality gaps, providing an interesting alternative to state-of-the-art semidefinite programming relaxations. Three case studies in optimal power flow, optimal transmission switching and capacitor placement demonstrate the benefits of the new relaxations.

关键词： mixed-integer nonlinear programming Global optimization Convex relaxation Optimal power flow Optimal transmission switching Capacitor placement

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A note on representations of linear inequalities in non-convex mixed-integer quadratic programs

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OPERATIONS RESEARCH LETTERS 2017年第6期45卷 631-634页

作者： Letchford, Adam N. Grainger, Daniel J. Univ Lancaster Dept Management Sci Lancaster LA1 4YW England Univ Lancaster Lancaster England

In the literature on the quadratic 0-1 knapsack problem, several alternative ways have been given to represent the knapsack constraint in the quadratic space. We extend this work by constructing analogous representations for arbitrary linear inequalities for arbitrary non-convex mixed-integer quadratic programs with bounded variables. (C) 2017 Elsevier B.V. All rights reserved.

关键词： mixed-integer nonlinear programming Global optimisation Semidefinite programming

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BARON: A general purpose global optimization software package

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JOURNAL OF GLOBAL OPTIMIZATION 1996年第2期8卷 201-205页

作者： Sahinidis, NV UNIV ILLINOIS DEPT MECH & IND ENGNURBANAIL 61801

The Branch-And-Reduce Optimization Navigator (BARON) is a computational system for facilitating the solution of nonconvex optimization problems to global optimality. We provide a brief description of the algorithms used by the software, describe the types of problems that can be currently solved and summarize our recent computational experience. BARON is available by anonymous ftp from ***.

关键词： branch-and-bound polynomial programming multiplicative programming mixed-integer nonlinear programming quadratic programming fixed-charge problem

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