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Global optimization algorithm for capacitated multi-facility continuous location-allocation problems

JOURNAL OF GLOBAL OPTIMIZATION 2018年第4期71卷 871-889页

作者： Lara, Cristiana L. Trespalacios, Francisco Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA ExxonMobil Res & Engn Co Corp Strateg Res 1545 Route 22 East Annandale NJ 08801 USA

In this paper we propose a nonlinear Generalized Disjunctive programming model to optimize the 2-dimensional continuous location and allocation of the potential facilities based on their maximum capacity and the given coordinates of the suppliers and customers. The model belongs to the class of Capacitated Multi-facility Weber Problem. We propose a bilevel decomposition algorithm that iteratively solves a discretized MILP version of the model, and its nonconvex NLP for a fixed selection of discrete variables. Based on the bounding properties of the subproblems, -convergence is proved for this algorithm. We apply the proposed method to random instances varying from 2 suppliers and 2 customers to 40 suppliers and 40 customers, from one type of facility to 3 different types, and from 2 to 32 potential facilities. The results show that the algorithm is more effective at finding global optimal solutions than general purpose global optimization solvers tested.

关键词： Location-allocation problem Weber problem Nonconvex optimization Generalized disjunctive programming mixed-integer nonlinear programming

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A MILP model for the detailed scheduling of multiproduct pipelines with the hydraulic constraints rigorously considered

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COMPUTERS & CHEMICAL ENGINEERING 2019年 130卷 106543-000页

作者： Zhou, Xingyuan Liang, Yongtu Zhang, Xin Liao, Qi Gao, Song Zhang, Wan Zhang, Haoran China Univ Petr Beijing Key Lab Urban Oil & Gas Distribut Technol Natl Engn Lab Pipeline Safety Fuxue Rd 18 Beijing 102249 Peoples R China Univ Southern Calif Viterbi Sch Engn 3650 McClintockAve Los Angeles CA 90089 USA Univ Tokyo Ctr Spatial Informat Sci 5-1-5 Kashiwanoha Kashiwa Chiba 2778568 Japan

Multiproduct pipelines are the most effective and important way to transport refined products from refineries to the downstream market. The detailed scheduling of a multiproduct pipeline with the hydraulic constraints considered plays a vital role in the pipeline's safe and economic operation. Aiming at this issue, this paper proposes a continuous-time mixed-integer nonlinear programming (MINLP) model taking the sum of the pumps' running and start/stop costs as the objective function. Based on a method proposed in a previous study for the rigorous description of the hydraulic loss changes in multiproduct pipelines, the pipeline hydraulic constraints and pump-related functions are considered directly and rigorously in the model. Other actual field processing constraints, such as the pipeline shutdown and contaminated oil flowrate, are also considered. To deal with the nonlinear relationship between the flowrate and pressure/volume, a piecewise linearization method is adopted, and the pipeline flowrate is graded according to the divided intervals. Finally, the application of the established model is successfully conducted on a real-world multiproduct pipeline through two case studies. A comparison with a previous discrete-time model is also performed to verify this model's applicability, accuracy, and superiority. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Multiproduct pipeline Detailed scheduling Hydraulic constraints mixed-integer nonlinear programming Continuous-time representation

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Distribution network design with big data: model and analysis

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ANNALS OF OPERATIONS RESEARCH 2018年第1-2期270卷 539-551页

作者： Wang, Gang Gunasekaran, Angappa Ngai, Eric W. T. Univ Massachusetts Dartmouth Dept Decis & Informat Sci Charlton Coll Business 285 Old Westport Rd N Dartmouth MA 02747 USA Hong Kong Polytech Univ Dept Management & Mkt Kowloon Hong Kong Peoples R China

This study addresses the problem of locating distribution centers in a single-echelon, capacitated distribution network. Such network consists of several potential distribution centers and various demand points dispersed in different regional markets. The distribution operations of this network generate massive amounts of data. The problem is how to utilize big data generated to identify the right number of distribution centers to open and the right assignment of customers to opened distribution centers while minimizing the total handling and operation costs of distribution centers, transportation, and penalty. Restrictions on both network capacity and single sourcing strategy are also considered. This study formulates this problem as mixed-integer nonlinear program. The effects of different scenarios on distribution-center locations as demand, the operation costs of distribution centers and outbound transportation, and the number of customers are analyzed through simulation on randomly generated big datasets. Empirical results indicate that the model presented is appropriate and robust. The operational value of big data in the distribution network design is revealed through a case study in which several design alternatives are evaluated.

关键词： Big data Capacitated network design Service levels mixed-integer nonlinear programming Simulation

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RBFOpt: an open-source library for black-box optimization with costly function evaluations

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MATHEMATICAL programming COMPUTATION 2018年第4期10卷 597-629页

作者： Costa, Alberto Nannicini, Giacomo Swiss Fed Inst Technol Future Cities Lab Program Singapore Singapore IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA

We consider the problem of optimizing an unknown function given as an oracle over a mixed-integer box-constrained set. We assume that the oracle is expensive to evaluate, so that estimating partial derivatives by finite differences is impractical. In the literature, this is typically called a black-box optimization problem with costly evaluation. This paper describes the solution methodology implemented in the open-source library RBFOpt, available on COIN-OR. The algorithm is based on the Radial Basis Function method originally proposed by Gutmann (J Glob Optim 19:201-227, 2001. https://***/10.1023/A:1011255519438), which builds and iteratively refines a surrogate model of the unknown objective function. The two main methodological contributions of this paper are an approach to exploit a noisy but less expensive oracle to accelerate convergence to the optimum of the exact oracle, and the introduction of an automatic model selection phase during the optimization process. Numerical experiments show that RBFOpt is highly competitive on a test set of continuous and mixed-integer nonlinear unconstrained problems taken from the literature: it outperforms the open-source solvers included in our comparison by a large amount, and performs slightly better than a commercial solver. Our empirical evaluation provides insight on which parameterizations of the algorithm are the most effective in practice. The software reviewed as part of this submission was given the Digital Object Identifier (DOI) https://***/10.5281/zenodo.597767.

关键词： Black-box optimization Derivative-free optimization Global optimization Radial basis function Open-source software mixed-integer nonlinear programming

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Optimization-based process synthesis under seasonal and daily variability: Application to concentrating solar power

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AICHE JOURNAL 2019年第7期65卷 e16458-e16458页

作者： Peng, Xinyue Root, Thatcher W. Maravelias, Christos T. Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA

We propose an optimization-based framework for process synthesis under variability in two frequencies. Low-frequency variability is represented through scenarios and high-frequency variability is modeled using modes. The proposed framework allows for the selection of different process configurations during different modes, a feature necessary to model systems under wide high frequency variability (e.g., solar-based technologies). The optimization problem is formulated as a two-stage stochastic programming model with mode subproblems nested inside each scenario. The proposed framework is applied to the design of concentrating solar power plants with thermochemical energy storage, leading to the formulation of a computationally efficient model, as well as the identification of a superior design. (c) 2018 American Institute of Chemical Engineers AIChE J, 65: e16458 2019

关键词： superstructure-based process synthesis mixed-integer nonlinear programming stochastic programming

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A JOINT DETECTION AND RECONSTRUCTION METHOD FOR BLIND GRAPH SIGNAL RECOVERY

A JOINT DETECTION AND RECONSTRUCTION METHOD FOR BLIND GRAPH ...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Mao, Xianghui Gu, Yuantao Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781538646588

Sampling and reconstruction is a fundamentally important problem in the field of graph signal processing. Many works have been contributed to reconstructing bandlimited signals from measurements taken on a known subset of vertices. However, in some cases, the vertex defects occur randomly over the graph. In such situation, the existing graph signal reconstruction methods fail to deal with such blind reconstruction problem. In this paper, we formulate the blind reconstruction problem as mixed-integer nonlinear programming, and propose a Joint Detection and Reconstruction (JDR) method to simultaneously detect the vertices' working states and reconstruct the bandlimited signal. The convergence property of the proposed method is analyzed. In the experimental part, both synthetic dataset and real-world dataset are applied to verify the proposed methods.

关键词： blind reconstruction bandlimited graph signal mixed-integer nonlinear programming

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Towards Robust Admission Control in Delay-Constrained Routing Problems

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Electronic Notes in Discrete Mathematics 2018年 69卷 45-52页

作者： Frangioni, Antonio Galli, Laura Stea, Giovanni Dipartimento di Informatica Università di Pisa Largo B. Pontecorvo 3 Pisa 56127 Italy Dipartimento di Ingegneria dell'Informazione Università di Pisa Largo Lucio Lazzarino 1 Pisa 56122 Italy

Delay-Constrained Routing (DCR) problems require to route a new flow in a computer network subject to worst-case end-to-end delay guarantees. The delay of a packet flow has three components, one of which is the “queueing delay”, that depends on the scheduling algorithm implemented by the routers of the network. When flows are not independent of each other, i.e., admitting a new flow changes the delay of the existing ones, admission control policies are necessary to ensure that existing flows do not become latency-unfeasible. It has been recently shown that admission control runs contrary to the usual objective function employed in these models, i.e., minimization of the reserved rates, significantly worsening network performance. In this paper we investigate the phenomenon and propose a heuristic way to overcome the problem. © 2018 Elsevier B.V.

关键词： admission control maximum delay constraints mixed-integer nonlinear programming Robustness Routing problems scheduling algorithms Second-Order Cone Programs

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Optimal Location of DGs in DC Power Grids Using a MINLP Model Implemented in GAMS 9

Optimal Location of DGs in DC Power Grids Using a MINLP Mode...

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9th IEEE Power, Instrumentation and Measurement Meeting (EPIM)

作者： Montoya, O. D. Garrido, V. M. Grisales-Norena, L. F. Gil-Gonzalez, W. Garces, A. Ramos-Paja, C. A. Univ Tecnol Bolivar Ingn Elect & Ingn Elect Cartagena Colombia Inst Tecnol Metropolitano Dept Electromecan & Mecatron Medellin Colombia Univ Tecnol Pereira Programa Ingn Elect Pereira Colombia Univ Nacl Colombia Fac Minas Medellin Colombia

ISBN: (纸本)9781538678428

This paper addresses the problem of optimal location and sizing of distributed generators (DGs) in direct current (dc) power grids by using a mixed-integer nonlinear programming (MINLP) formulation. The reduction of the power losses in all branches of the network are considered as the objective function;while the restrictions are the power balance, voltage regulation, maximum penetration and maximum distributed generation units available. The general algebraic modeling system (GAMS) is selected as nonlinear optimizing package to solve this problem;besides, a small numerical example of energy production is introduced to illustrate the usability of using GAMS. Finally, a 21-node dc grid with two ideal generators, and multiple constant power loads, is used as test system.

关键词： Direct-current power grids distributed generators location and sizing general algebraic modeling system mixed-integer nonlinear programming power losses minimization

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Monomial-wise optimal separable underestimators for mixed-integer polynomial optimization

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JOURNAL OF GLOBAL OPTIMIZATION 2017年第4期67卷 759-786页

作者： Buchheim, Christoph D'Ambrosio, Claudia TU Dortmund Fak Math Vogelpothsweg 87 D-44227 Dortmund Germany Ecole Polytech LIX CNRS UMR7161 F-91128 Palaiseau France

We introduce a new method for solving box-constrained mixed-integer polynomial problems to global optimality. The approach, a specialized branch-and-bound algorithm, is based on the computation of lower bounds provided by the minimization of separable underestimators of the polynomial objective function. The underestimators are the novelty of the approach because the standard approaches in global optimization are based on convex relaxations. Thanks to the fact that only simple bound constraints are present, minimizing the separable underestimator can be done very quickly. The underestimators are computed monomial-wise after the original polynomial has been shifted. We show that such valid underestimators exist and their degree can be bounded when the degree of the polynomial objective function is bounded, too. For the quartic case, all optimal monomial underestimators are determined analytically. We implemented and tested the branch-and-bound algorithm where these underestimators are hardcoded. The comparison against standard global optimization and polynomial optimization solvers clearly shows that our method outperforms the others, the only exception being the binary case where, though, it is still competitive. Moreover, our branch-and-bound approach suffers less in case of dense polynomial objective function, i.e., in case of polynomials having a large number of monomials. This paper is an extended and revised version of the preliminary paper [4].

关键词： Polynomial optimization mixed-integer nonlinear programming Separable underestimators

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Operational strategy and planning for raw natural gas refining complexes: Process modeling and global optimization

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AICHE JOURNAL 2017年第2期63卷 652-668页

作者： Zhang, Bing J. Chen, Qing L. Li, Jie Floudas, Christodoulos A. Sun Yat Sen Univ Sch Chem Engn & Technol Guangdong Engn Technol Res Ctr Petrochem Energy C Key Lab Low Carbon Chem & Energy Conservat Guangd 135 Xingang West Rd Guangzhou 510275 Guangdong Peoples R China Texas A&M Univ Artie McFerrin Dept Chem Engn 3122 TAMU College Stn TX 77843 USA Texas A&M Univ Texas A&M Energy Inst 3372 TAMU College Stn TX 77843 USA Univ Manchester Sch Chem Engn & Analyt Sci Manchester M13 9PL Lancs England

Optimal operational strategy and planning of a raw natural gas refining complex (RNGRC) is very challenging since it involves highly nonlinear processes, complex thermodynamics, blending, and utility systems. In this article, we first propose a superstructure integrating a utility system for the RNGRC, involving multiple gas feedstocks, and different product specifications. Then, we develop a large-scale nonconvex mixed-integer nonlinear programming (MINLP) optimization model. The model incorporates rigorous process models for input and output relations based on fundamentals of thermodynamics and unit operations and accurate models for utility systems. To reduce the noncovex items in the proposed MINLP model, equivalent reformulation techniques are introduced. Finally, the reformulated nonconvex MINLP model is solved to global optimality using state of the art deterministic global optimization approaches. The computational results demonstrate that a significant profit increase is achieved using the proposed approach compared to that from the real operation. (c) 2016 American Institute of Chemical Engineers AIChE J, 63: 652-668, 2017

关键词： natural gas operational planning mixed-integer nonlinear programming enterprise-wide optimization global optimization

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