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American Control Conference

作者： James Usevitch Dimitra Panagou University of Michigan Ann Arbor

ISBN: (纸本)9781538679012;9781538679265

Convergence guarantees of many resilient consensus algorithms are based on the graph theoretic properties of rand (r, s)-robustness. These algorithms guarantee consensus of normally behaving agents in the presence of a bounded number of arbitrarily misbehaving agents if the values of the integers r and s are sufficiently high. However, determining the largest integer r for which an arbitrary digraph is r-robust is highly nontrivial. This paper introduces a novel method for calculating this value using mixed integer linear programming. The method only requires knowledge of the graph Laplacian matrix, and can be formulated with affine objective and constraints, except for the integer constraint. integer programming methods such as branch-and-bound can allow both lower and upper bounds on r to be iteratively tightened. Simulations suggest the proposed method demonstrates greater efficiency than prior algorithms.

关键词： mixed integer linear programming directed graphs integer Agents Upper bound algorithms

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mixed integer linear programming Formulation for Optimal Reactive Compensation and Voltage Control of Distribution Power Systems

Mixed Integer Linear Programming Formulation for Optimal Rea...

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2017 IEEE Power & Energy Society General Meeting

作者： Castrillon, Jhon A. Giraldo, Juan S. Castro, Carlos A. Univ Estadual Campinas Campinas SP Brazil

ISBN: (纸本)9781538622124

This work proposes a mixed integer linear program. ming (MILP) formulation, based on a linear power flow that has recently been proposed in the literature, for the reactive power compensation of distribution networks through the optimal selection of capacitor banks (CB) as well as the optimal tap selection of voltage regulators (VRs) and on load tap changers (OLTCs) in order to maintain the system voltages within hounds. In the proposed formulation the voltage dependent load model is taken into account and the objective is to minimize the supply energy cost on a day-long time period. In order to evaluate the proposed model, several simulations are shown for a 34-bus radial distribution system. The results are duly discussed.

关键词： linear power flow load model mixed integer linear programming optimal reactive compensation voltage control distribution systems

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Scheduling of a consumer goods production plant with intermediate buffer by decomposition and mixed-integer linear programming

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IFAC-PapersOnLine 2019年第13期52卷 1837-1842页

作者： Yfantis, Vassilios Corominas, Francesc Engell, Sebastian Process Dynamics and Operations Group Department of Biochemical and Chemical Engineering TU Dortmund University Emil-Figge-Str. 70 Dortmund44227 Germany Procter and Gamble Temselaan 100 Strombeek-Bever1853 Belgium

Varying demands and global competition pose significant challenges in the consumer goods industry. Exploiting market opportunities often leads to the need of an extension of the production capacities of manufacturing plants. This can be achieved by adding new plants or adding production units to existing plants which may require very significant investments. Another option is the reconfiguration of an existing plant layout to achieve higher throughputs at relatively low cost. A potential reconfiguration strategy is the decoupling of production stages by buffers which increases the flexibility and is efficient if the bottlenecks of the process vary over time. In this contribution the scheduling of an industrial consumer goods production plant with two production stages with a buffer in between the stages is discussed. This can be used to assess the benefits of the modified layout and for the later online scheduling of the plant with the buffer. The scheduling algorithm is based on decomposition and mathematical programming. The generated schedules are compared to schedules for the current layout. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： integer programming Competition Flexible manufacturing systems Investments Plant layout Scheduling Scheduling algorithms Consumer goods production Industrial consumers Intermediate buffers Manufacturing process Market opportunities mixed integer linear programming Optimization problems Reconfiguration

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Modeling and evaluating agile assembly systems using mixed-integer linear programming

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Procedia CIRP 2020年 93卷 1073-1078页

作者： Peter Burggräf Matthias Dannapfel Tobias Adlon Esben Schukat Hannes Kahmann Lucian Holtwiesche Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University Campus Boulevard 30 52074 Aachen Germany Chair of International Production Engineering and Management (IPEM) University of Siegen Paul-Bonatz-Straße 9-11 57076 Siegen Germany

Agile assembly systems in automotive production are predominantly characterized by decoupled and independent workstations. This raises the question how such assembly systems can be evaluated and compared against the predominantly used ‪‪assembly lines. Job-shop-problems as a promising formulation do not yet offer a model that covers all the intended degrees of freedom. This research work thus presents an extended job-shop-problem and its validation while considering parallel and multifunctional workstations, sequence-dependent set-up times, routing and sequence flexibility as well as a multivariable objective function. The resulting model based on mixed integer linear programming contributes to the evaluation of agile assembly systems and supports the development of real-time capable algorithms for practical applications.

关键词： Agile assembly modular assembly job shop problem mixed integer linear programming assembly control

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mixed-integer linear programming Model and Constraints Reduction Methods for Security-Constrained Unit Commitment

Mixed-Integer Linear Programming Model and Constraints Reduc...

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Sustainable Power and Energy Conference (iSPEC)

作者： Zhi Li Li Li Yanping Wang Guannan Bao Hanghang Liu Dapeng Liao Ruipeng Guo State Grid Jinan Power Supply Company Jinan Shandong China State Grid Shandong Electric Power Company Jinan Shandong China College of Electrical Engineering Zhejiang University Hangzhou Zhejiang China State Grid Dongying Power Supply Company Dongying Shandong China

ISBN: (数字)9781728191645

ISBN: (纸本)9781728191652

Security-constrained unit commitment (SCUC) is one of the most important daily tasks that must be accomplished in electric power market. To solve the SCUC problem efficiently, this paper proposes a mixed-integer linear programming (MILP) model and related constraints reduction methods. The proposed model includes both pre-and post-contingency security constraints, which are linearized by the DC power flow equation and the line outage distribution factor (LODF), respectively. The proposed constraints reduction methods reduce the security constraints of parallel branches by identifying the representative branches in each group of parallel branches, and eliminate the redundant post-contingency security constraints by analyzing the maximum possible values of post-contingency power flow. The correctness and effectiveness of the proposed model and methods are verified on 39 bus New England system and IEEE-118 test system.

关键词： Computational modeling Software mixed integer linear programming Mathematical model Security Task analysis Load flow

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Exact augmented Lagrangian duality for mixed integer linear programming

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MATHEMATICAL programming 2017年第1-2期161卷 365-387页

作者： Feizollahi, Mohammad Javad Ahmed, Shabbir Sun, Andy Georgia State Univ Robinson Coll Business Atlanta GA 30303 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

We investigate the augmented Lagrangian dual (ALD) for mixed integer linear programming (MIP) problems. ALD modifies the classical Lagrangian dual by appending a nonlinear penalty function on the violation of the dualized constraints in order to reduce the duality gap. We first provide a primal characterization for ALD for MIPs and prove that ALD is able to asymptotically achieve zero duality gap when the weight on the penalty function is allowed to go to infinity. This provides an alternative characterization and proof of a recent result in Boland and Eberhard (Math Program 150(2):491-509, 2015, Proposition 3). We further show that, under some mild conditions, ALD using any norm as the augmenting function is able to close the duality gap of an MIP with a finite penalty coefficient. This generalizes the result in Boland and Eberhard (2015, Corollary 1) from pure integer programming problems with bounded feasible region to general MIPs. We also present an example where ALD with a quadratic augmenting function is not able to close the duality gap for any finite penalty coefficient.

关键词： mixed integer linear programming Lagrangian duality Penalty functions

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Compact mixed integer linear programming models to the minimum weighted tree reconstruction problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第1期256卷 242-251页

作者： Fortz, Bernard Oliveira, Olga Requejo, Cristina Univ Libre Bruxelles Dept Comp Sci B-1050 Brussels Belgium INRIA Lille Nord Europe Lille France Univ Aveiro Dept Math P-3810193 Aveiro Portugal

The Minimum Weighted Tree Reconstruction (MWTR) problem consists of finding a minimum length weighted tree connecting a set of terminal nodes in such a way that the length of the path between each pair of terminal nodes is greater than or equal to a given distance between the considered pair of terminal nodes. This problem has applications in several areas, namely, the inference of phylogenetic trees, the modeling of traffic networks and the analysis of internet infrastructures. In this paper, we investigate the MWTR problem and we present two compact mixed-integer linear programming models to solve the problem. Computational results using two different sets of instances, one from the phylogenetic area and another from the telecommunications area, show that the best of the two models is able to solve instances of the problem having up to 15 terminal nodes. (C) 2016 Elsevier B.V. All rights reserved.

关键词： mixed integer linear programming Tree realization Topology discovery Routing topology inference Minimum evolution problem

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An Optimal Bus Scheduling Model Based on mixed-integer linear programming

An Optimal Bus Scheduling Model Based on Mixed-Integer Linea...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Ge Meng Yongxuan Lai Fan Yang School of Informatics Xiamen University China Xiamen University China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

The bus company mainly relies on the experienced scheduling personnel to prepare the time plate of the bus line manually in advance, and then conFigure the vehicles. However, this kind of scheduling method often cause a waste of bus seat resources only by experience. In this paper, we propose a vehicles scheduling model called MILPS (mixed-integer linear programming Model of Scheduling) to minimise the gap between the demand and supply of bus seats. In the first phase, we first analyze the historical trajectory data of taxis in Xiamen and select the two most popular stations as the starting and ending points of bus routes, then generate the service area between them by rectangle. In the second phase, we derive several criteria to build bus scheduling model based on mixed-integer linear programming. Finally, we use CPLEX optimizer to solve this MILP problem. Experimental results show that the proposed scheme can effectively reduce the waste of bus seat resources (>76.8% reduction) and significantly improve the delivery ratio (>71.5%).

关键词： Conferences Companies mixed integer linear programming Trajectory Personnel Information technology Public transportation

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Complete mixed integer linear programming formulations for modularity density based clustering

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DISCRETE OPTIMIZATION 2017年 25卷 141-158页

作者： Costa, Alberto Ng, Tsan Sheng Foo, Lin Xuan Natl Univ Singapore Singapore Singapore Swiss Fed Inst Technol Future Resilient Syst Zurich Switzerland Natl Univ Singapore Dept Ind & Syst Engn Singapore Singapore

Modularity density maximization is a clustering method that improves some issues of the commonly used modularity maximization approach. Recently, some mixed integer linear programming (MILP) reformulations have been proposed in the literature for the modularity density maximization problem, but they require as input the solution of a set of auxiliary binary Non-linear Programs (NLPs). These can become computationally challenging when the size of the instances grows. In this paper we propose and compare some explicit MILP reformulations of these auxiliary binary NLPs, so that the modularity density maximization problem can be completely expressed as MILP. The resolution time is reduced by a factor up to two order of magnitude with respect to the one obtained with the binary NLPs. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Clustering Modularity density mixed integer linear programming Reformulations

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LO-constrained regression using mixed integer linear programming

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2017年 165卷 29-37页

作者： Willis, Mark J. von Stosch, Moritz Newcastle Univ Sch Chem Engn & Adv Mat Newcastle Upon Tyne NE1 7RU Tyne & Wear England

In this work, sparse regression using a penalized least absolute deviations objective function is considered. Regression model sparsity is promoted using a L-0 - pseudo norm penalty (the cardinality of the model parameter vector). Implemented using mixed integer linear programming (MILP) it is demonstrated that the use of the L-0 norm (without approximation) enables efficient and accurate solutions to sparse regression problems of practical size. For model development with a large number of potential model parameters (or features) methods to relax the MILP are also developed;using nonlinear function approximations to the L-0- norm, penalty terms are linearized and solved using sequential linear programming. Experimental results (using both simulated and real data) demonstrate that these algorithms are also computationally efficient producing accurate and parsimonious model structures. Applications considered are the development of a calibration model for prediction with Near Infrared (NIR) data and the development of a model for the prediction of chemical toxicity - a quantitative structure activity relationship (QSAR).

关键词： Sparse regression mixed integer linear programming

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