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Tire noise optimization problem: a mixed integer linear programming approach

RAIRO-OPERATIONS RESEARCH 2021年第5期55卷 3073-3085页

作者： Becker, Matthias Ginoux, Nicolas Martin, Sebastien Roka, Zsuzsanna Leibniz Univ Hannover FG HCI Appelstr 9A D-30167 Hannover Germany Univ Lorraine CNRS IECL F-57000 Metz France Univ Lorraine Lab Concept Optimisat & Modelisat Syst LCOMS EA 7306 F-57000 Metz France

We present a mixed integer linear programming (MILP) approach in order to model the non-linear problem of minimizing the tire noise function. In a recent work, we proposed an exact solution for the Tire Noise Optimization Problem, dealing with an APproximation of the noise (TNOP-AP). Here we study the original non-linear problem modeling the EXact- or real-noise (TNOP-EX) and propose a new scheme to obtain a solution for the TNOP-EX. Relying on the solution for the TNOP-AP, we use a Branch&Cut framework and develop an exact algorithm to solve the TNOP-EX. We also take more industrial constraints into account. Finally, we compare our experimental results with those obtained by other methods.

关键词： mixed integer linear programming branch-and-cut tire shape optimization

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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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Bridging mixed integer linear programming for truss topology optimization and additive manufacturing

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OPTIMIZATION AND ENGINEERING 2021年第2期22卷 849-893页

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

One crucial advantage of additive manufacturing regarding the optimization of lattice structures is that there is a reduction in manufacturing constraints compared to classical manufacturing methods. To make full use of these advantages and to exploit the resulting potential, it is necessary that lattice structures are designed using optimization. Against this backdrop, two mixed integer programs are developed in order to use the methods of mathematical optimization in the context of topology optimization on the basis of a fitted ground structure method. In addition, an algorithm driven product design process is presented to systematically combine the areas of mathematical optimization, computer aided design, finite element analysis and additive manufacturing. Our developed computer aided design tool serves as an interface between state-of-the-art mathematical solvers and computer aided design software and is used for the generation of design data based on optimization results. The first mixed integer program focuses on powder-based additive manufacturing, including a preprocessing that allows a multi-material topology optimization. The second mixed integer program generates support-free lattice structures for additive manufacturing processes usually depending on support structures, by considering geometry-based design rules for inclined and support-free cylinders and assumptions for location and orientation of parts within a build volume. The problem to strengthen a lattice structure by local thickening or beam addition or both, with the objective function to minimize costs, is modeled. In doing so, post-processing is excluded. An optimization of a static area load with a practice-oriented number of connection nodes and beams was manufactured using the powder-based additive manufacturing system EOS INT P760.

关键词： Technical operations research mixed integer linear programming Design for additive manufacturing Unstructured lattices Truss topology design Topology optimization

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Experiences with mixed integer linear programming based approaches on short-term hydro scheduling

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IEEE TRANSACTIONS ON POWER SYSTEMS 2001年第4期16卷 743-749页

作者： Chang, GW Aganagic, M Waight, JG Medina, J Burton, T Reeves, S Christoforidis, M Natl Chung Cheng Univ Dept Elect Engn Chiayi 621 Taiwan Siemens Energy Management & Informat Syst Brooklyn Pk MN 55428 USA ECNZ So Generat Grp Twizel New Zealand Swiss Rail SBB Zollikofen Switzerland

This paper describes experiences with mixed integer linear programming (MILP) based approaches on the short-term hydro scheduling (STHS) function. The STHS is used to determine the optimal or near-optimal schedules for the dispatchable hydro units in a hydro-dominant system for a user-definable study period at each time step while respecting all system and hydraulic constraints. The problem can be modeled in detail for a hydro system that contains both conventional and pumped-storage units. Discrete and dynamic constraints such as unit startup/shutdown and minimum-up/minimum-down time limits are also included in the model for hydro unit commitment (HUC). The STHS problem is solved with a state-of-the-art package which includes an algebraic modeling language and a MILP solver. The usefulness of the proposed solution algorithm is illustrated by testing the problem with actual hydraulic system data. Numerical experiences show that the solution technique is computationally efficient, simple, and suit able for decision support of short-term hydro operations planning. In addition, the proposed approaches can be easily extended for scheduling applications In deregulated environment.

关键词： algebraic modeling language hydro unit commitment interior point method mixed integer linear programming short-term hydro scheduling

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Energy-saving optimization of application server clusters based on mixed integer linear programming

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2023年 171卷 111-129页

作者： Xiong, Zhi Zhao, Min Yuan, Ziyue Xu, Jianlong Cai, Lingru Shantou Univ Dept Comp Sci & Technol Shantou 515063 Peoples R China Shantou Univ Minist Educ Key Lab Intelligent Mfg Technol Shantou 515063 Peoples R China

The issue of how to dynamically optimize the deployment of an application server cluster according to the changing load to reduce energy consumption is an important problem that must be urgently solved. In this paper, we propose an energy-saving optimization strategy for application server clusters, whose optimization content includes the on/off state, CPU frequency, and load size of each server. Compared with existing research, our strategy is not only more accurate in power and load models but also considers the switching cost of servers to avoid server switching jitter. The strategy includes two schemes, which both formulate the cluster energy-saving optimization as a mixed integer linear programming (MILP) problem and then adopt a toolkit to solve the problem. One scheme defines variables for each server, and the resulting programming problem is called the MILP4PH problem. The other scheme defines variables for each server type, resulting in a programming problem called the MILP4GH problem. The experimental results reveal that for clusters with poor homogeneity, the MILP4PH problem has fewer variables and can be solved in real time, while for clusters with good homogeneity, the MILP4GH problem has fewer variables and can be solved in real time.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Application server cluster Energy-saving optimization mixed integer linear programming Switching jitter Homogeneity

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Time-Optimal Coordination of Flexible Manufacturing Systems Using Deterministic Finite Automata and mixed integer linear programming

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DISCRETE EVENT DYNAMIC SYSTEMS-THEORY AND APPLICATIONS 2009年第3期19卷 287-315页

作者： Kobetski, Avenir Fabian, Martin Chalmers Univ Technol Dept Signals & Syst S-41296 Gothenburg Sweden

Automation and flexibility are often mentioned as key concepts in modern production industry. To increase the level of flexibility, deterministic finite automata (DFA) can be used to model, specify and verify the production systems. Often, it is also desirable to optimize some production criteria, such as for example the cycle time of a manufacturing cell. In this paper, a method for automatic conversion from DFA to a mixed integer linear programming (MILP) formulation is first presented. This conversion is developed for a number of DFA structures that have shown to be useful in practical applications. Special attention is paid to reducing the search region explored by the MILP solver. Second, a conversion from the MILP solution to a DFA supervisor is described. This allows to combine the advantages of DFA modeling with the efficiency of MILP and supervisory control theory to automatically generate time-optimal, collision-free and non-blocking working schedules for flexible manufacturing systems.

关键词： Discrete event systems Scheduling mixed integer linear programming Collision avoidance Deadlock avoidance

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Robust Automated Mass Spectra Interpretation and Chemical Formula Calculation Using mixed integer linear programming

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ANALYTICAL CHEMISTRY 2013年第20期85卷 9777-9784页

作者： Baran, Richard Northen, Trent R. Lawrence Berkeley Natl Lab Div Life Sci Berkeley CA 94720 USA

Untargeted metabolite profiling using liquid chromatography and mass spectrometry coupled via electrospray ionization is a powerful tool for the discovery of novel natural products, metabolic capabilities, and biomarkers. However, the elucidation of the identities of uncharacterized metabolites from spectral features remains challenging. A critical step in the metabolite identification workflow is the assignment of redundant spectral features (adducts, fragments, multimers) and calculation of the underlying chemical formula. Inspection of the data by experts using computational tools solving partial problems (e.g., chemical formula calculation for individual ions) can be performed to disambiguate alternative solutions and provide reliable results. However, manual curation is tedious and not readily scalable or standardized. Here we describe an automated procedure for the robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming optimization (RAMSI). Chemical rules among related ions are expressed as linear constraints and both the spectra interpretation and chemical formula calculation are performed in a single optimization step. This approach is unbiased in that it does not require predefined sets of neutral losses and positive and negative polarity spectra can be combined in a single optimization. The procedure was evaluated with 30 experimental mass spectra and was found to effectively identify the protonated or deprotonated molecule ([M + H](+) or [M - H](-)) while being robust to the presence of background ions. RAMSI provides a much-needed standardized tool for interpreting ions for subsequent identification in untargeted metabolomics workflows.

关键词： CHEMICAL formulas linear programming METABOLITES ELECTROSPRAY ionization mass spectrometry IONS mixed integer linear programming MASS spectrometry PROTON transfer reactions

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A mixed integer linear programming solution for single hoist multi-degree cyclic scheduling with reentrance

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ENGINEERING OPTIMIZATION 2014年第5期46卷 704-723页

作者： Li, Xin Fung, Richard Y. K. City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China

This article considers single hoist multi-degree cyclic scheduling problems with reentrance. Time window constraints are also considered. Firstly, a mixed integer programming model is formulated for multi-degree cyclic hoist scheduling without reentrance, referred to as basic lines in this article. Two valid inequalities corresponding to this problem are also presented. Based on the model for basic lines, an extended mixed integer programming model is proposed for more complicated scheduling problems with reentrance. Phillips and Unger's benchmark instance and randomly generated instances are applied to test the model without reentrance, solved using the commercial software CPLEX. The efficiency of the model is analysed based on computational time. Moreover, an example is given to demonstrate the effectiveness of the model with reentrance.

关键词： hoist scheduling problems mixed integer linear programming multi-degree cycle reentrance robotic cells

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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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A Fast and Accurate Self-Healing Scheme for Intelligent Distribution Networks Using mixed integer linear programming

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IEEE ACCESS 2024年 12卷 21586-21595页

作者： Lu, Jiangang Su, Junni Zhao, Ruifeng Chen, Fengchao Wang, Qiujie Yan, Wei Guangdong Power Grid Co Ltd Elect Power Dispatching & Control Ctr Guangzhou 510600 Peoples R China Guangdong Power Grid Co Ltd Dongguan Power Supply Bur Dongguan 523000 Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Sec Chongqing 400044 Peoples R China

Fast and accurate self-healing after faults is an important way to improve the reliability of distribution networks. To improve the self-healing speed and accuracy of distribution networks, this paper proposes a fast and accurate self-healing scheme for distribution networks using mixed integer linear programming. Firstly, this method constructs a centralized 5G communication network architecture, which can effectively reduce the communication delay during the self-healing process. Secondly, the principle of logical algebraic transformation is utilized to linearize the switch function in the fault location model, thereby achieving equivalent transformation of the fault location model. Thirdly, using the polyhedral approximation method, the mixed integer second-order cone programming of the power supply recovery model is transformed into a mixed integer linear programming. The proposed method was validated by distribution network systems with different nodes in MATLAB/Simulink, and the results showed that the proposed method significantly improved self-healing speed and accuracy.

关键词： Distribution networks Power supplies Fault location 5G mobile communication Location awareness Fault currents Delays mixed integer linear programming Distribution network fault location fault recovery self-healing mixed integer linear programming 5G communication

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