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An Effective Problem Decomposition Method for Scheduling of Diffusion Processes Based on mixed integer linear programming

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2014年第3期27卷 357-363页

作者： Jung, Chihyun Pabst, Detlef Ham, Myoungsoo Stehli, Marcel Rothe, Marcel GLOBALFOUNDRIES Inc Malta NY 12020 USA Liberty Univ Sch Engn & Computat Sci Lynchburg VA 24515 USA GLOBALFOUNDRIES Inc D-10109 Dresden Germany

Diffusion processes in semiconductor fabrication facilities (Fabs) refer to the series of processes from wafer cleaning processes to furnace processes. Most furnace tools are batch tools, with large batch sizes, and have relatively long process times, when compared to the other processes. Strict time window constraints link cleaning processes with furnace processes for quality control. Those operational requirements for diffusion processes make their scheduling very difficult. This paper proposes an advanced scheduling approach based on a rolling horizon scheduling concept. Due to the combinatorial nature of the scheduling problem, the complexity of the problem increases exponentially, when the number of jobs and tools increase. However, the computation time allowed for the scheduler is limited in practice, because the variability in most Fabs requires schedulers to update the schedule in short intervals. We suggest an mixed integer linear programming model for diffusion processes, and propose an effective decomposition method to deal with this complexity problem. The decomposition method repeats multiple scheduling iterations, as it gradually extends the number of runs on tools, enabling the scheduler to generate near-optimal schedules in limited time intervals. The scheduler could make large improvements on key performance indicators, such as time window violation rates, batch sizes, throughput, etc. The software architecture of the scheduler implementation is also addressed in this paper.

关键词： Diffusion processes scheduling batch tools time window constraints mixed integer linear programming decomposition method

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Feature selection for Support Vector Machines via mixed integer linear programming

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INFORMATION SCIENCES 2014年 279卷 163-175页

作者： Maldonado, Sebastian Perez, Juan Weber, Richard Labbe, Martine Univ Los Andes Santiago Chile Univ Chile Dept Ind Engn Santiago Chile Univ Libre Bruxelles Dept Comp Sci B-1050 Brussels Belgium

The performance of classification methods, such as Support Vector Machines, depends heavily on the proper choice of the feature set used to construct the classifier. Feature selection is an NP-hard problem that has been studied extensively in the literature. Most strategies propose the elimination of features independently of classifier construction by exploiting statistical properties of each of the variables, or via greedy search. All such strategies are heuristic by nature. In this work we propose two different mixed integer linear programming formulations based on extensions of Support Vector Machines to overcome these shortcomings. The proposed approaches perform variable selection simultaneously with classifier construction using optimization models. We ran experiments on real-world benchmark datasets, comparing our approaches with well-known feature selection techniques and obtained better predictions with consistently fewer relevant features. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Feature selection Support Vector Machine mixed integer linear programming

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A branch and bound method for the solution of multiparametric mixed integer linear programming problems

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JOURNAL OF GLOBAL OPTIMIZATION 2014年第2-3期59卷 527-543页

作者： Oberdieck, Richard Wittmann-Hohlbein, Martina Pistikopoulos, Efstratios N. ETH Inst Chem & Bioengn Dept Chem & Appl Biosci CH-8093 Zurich Switzerland Univ London Imperial Coll Sci Technol & Med Ctr Proc Syst Engn Dept Chem Engn London SW7 2BY England

In this paper, we present a novel algorithm for the solution of multiparametric mixed integer linear programming (mp-MILP) problems that exhibit uncertain objective function coefficients and uncertain entries in the right-hand side constraint vector. The algorithmic procedure employs a branch and bound strategy that involves the solution of a multiparametric linear programming sub-problem at leaf nodes and appropriate comparison procedures to update the tree. McCormick relaxation procedures are employed to overcome the presence of bilinear terms in the model. The algorithm generates an envelope of parametric profiles, containing the optimal solution of the mp-MILP problem. The parameter space is partitioned into polyhedral convex critical regions. Two examples are presented to illustrate the steps of the proposed algorithm.

关键词： Multiparametric programming mixed integer linear programming Branch and bound

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Combinatorial therapy discovery using mixed integer linear programming

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BIOINFORMATICS 2014年第10期30卷 1456-1463页

作者： Pang, Kaifang Wan, Ying-Wooi Choi, William T. Donehower, Lawrence A. Sun, Jingchun Pant, Dhruv Liu, Zhandong Baylor Coll Med Computat & Integrat Biomed Res Ctr Houston TX 77030 USA Texas Childrens Hosp Jan & Dan Duncan Neurol Res Inst Houston TX 77030 USA Baylor Coll Med Dept Pediat Neurol Houston TX 77030 USA Baylor Coll Med Dept Obstet & Gynaecol Houston TX 77030 USA Baylor Coll Med Dept Mol Virol & Microbiol Houston TX 77030 USA Univ Texas Hlth Sci Ctr Houston Sch Biomed Informat Houston TX 77030 USA Univ Penn Dept Canc Biol Philadelphia PA 19104 USA

Motivation: Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. Results: Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set.

关键词： mixed integer linear programming COMBINATORIAL analysis CANCER CARDIOVASCULAR diseases PROTEINS

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Thyristor-Controlled Switch Capacitor Placement in Large-Scale Power Systems via mixed integer linear programming and Taylor Series Expansion

Thyristor-Controlled Switch Capacitor Placement in Large-Sca...

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IEEE PES General Meeting

作者： Ziaee, Omid Choobineh, F. Fred Univ Nebraska Dept Elect Engn Lincoln NE 68588 USA

ISBN: (纸本)9781479964154

Allocation of flexible alternating current transmission system (FACTS) devices to an electric power transmission network may be formulated as a nonlinear mathematical program. Solving such a nonlinear program for a large transmission network is computationally very expensive, and obtaining the optimal solution may be impossible. We present a Taylor series expansion approximation of the nonlinearities of the problem and propose a mixed integer linear program (MILP) for finding the optimum location and proper settings of a Thyristor-Controlled Series Capacitor (TCSC) in an electric power network. The objective of this problem is to minimize total generation cost based on the DC load flow model. The proposed method is implemented for the 118-bus IEEE test case and the results are discussed.

关键词： Flexible AC Transmission System DC optimal power flow mixed integer linear programming Taylor series expansion generation cost

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Volt/Var Optimization of Unbalanced Distribution Feeders via mixed integer linear programming

Volt/Var Optimization of Unbalanced Distribution Feeders via...

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2014 Power Systems Computation Conference (PSCC)

作者： Borghetti, Alberto Napolitano, Fabio Nucci, Carlo Alberto Univ Bologna Dept Elect Elect & Informat Engn Bologna Italy

ISBN: (纸本)9788393580132

The paper presents a mixed integer linear programming (MILP) model for the solution of the three-phase volt/var optimization (VVO) of medium voltage unbalanced distribution feeders. The VVO of a distribution feeder is aimed at calculating the most efficient operating conditions by means of the scheduling of transformers equipped with an on-load tap changer and distributed reactive power resources (such as embedded generators and switchable capacitors banks). The proposed model allows the representation of feeders composed by three-phase, two-phase, and single-phase lines, by transformers with different winding connections, by unbalanced wye- and delta-connected loads, by three-phase and single phase capacitor banks and embedded generators. The accuracy of the results is verified by using IEEE Test Feeders.

关键词： power distribution feeders unbalanced networks volt/var optimization mixed integer linear programming

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Optimal Microgrid Component Sizing using mixed integer linear programming

Optimal Microgrid Component Sizing using Mixed Integer Linea...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Yu, Xiang Eric Malysz, Pawel Sirouspour, Shahin Emadi, Ali McMaster Univ Hamilton ON L8S 4L8 Canada

ISBN: (纸本)9781479956159

This paper proposes a novel method for determining the optimal number of renewable energy and storage components in a microgrid given typical load profiles, local pricing regime, and capital costs. Case studies using solar panels and advanced lead acid battery modules are performed under residential, commercial, and off-grid sites. Simple mixed integer linear programming (MILP) optimization problems are formulated, presented, and solved in each scenario where economic analysis highlights the utility of the proposed approach.

关键词： Batteries Economics Electricity Microgrids Optimization Pricing mixed integer linear programming MICRO-GRID Pricing Batteries Electricity economics component sizing Solar collectors

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Performance analysis of mixed integer linear programming with Wavelength Division Multiplexing 2

Performance analysis of Mixed Integer Linear Programming wit...

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2nd International Conference on Devices, Circuits and Systems (ICDCS)

作者： Vinolee, R. Bhaskar, Vidhyacharan SRM Univ Dept Elect & Commun Engn Kattankulathur Tamil Nadu India

ISBN: (纸本)9781479913565

Wavelength division multiplexing network is a method to improve capacity of transmission and to design the best path between the source and destination and assign the wavelength to the path for data transmission. A simple node is to be designed with mixed integer linear programming /GPLK 4.4 as the mathematical problem formulation for the single-hop and virtual hop of the network. Comparison of the topologies, INTERNET, EON and National Science Foundation Network (NSFNET) With Wavelength Division Multiplexing Conversion having different light-path flows is made for different number of nodes with capacity and average Ingress, Egress and groomed traffic. The performance metrics is determined by Wavelength of Light paths, single hop path, Number of Virtual hops, Network Congestion, Number of Wavelength per link, and Wavelength channel capacity.

关键词： mixed integer linear programming Virtual topology Wavelength division Multiplexing Routing and Wavelength Assignment

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Power Reduction in FM Networks by mixed-integer programming: A Case Study

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IEEE ACCESS 2024年 12卷 23725-23732页

作者： Avella, Pasquale Nobili, Paolo Sassano, Antonio Univ Sannio Dipartimento Ingn I-82100 Benevento Italy Univ Tuscia DAFNE I-01100 Viterbo Italy Sapienza Univ Roma Dipartimento Ingn Informat Automat & Gestionale DI I-00185 Rome Italy

The climate change emergency calls for a reduction in energy consumption in all human activities and production processes. The radio broadcasting industry is no exception. However, reducing energy requirements by uniformly cutting the radiated power at every transmitter can potentially impair the quality of service. A careful evaluation and optimization study are in order. In this paper, by analyzing the Italian frequency modulation analog broadcasting service, we show that it is indeed possible to significantly reduce the energy consumption of the broadcasters without sacrificing the quality of the service, rather, even getting improvements.

关键词： Climate change Energy consumption mixed integer linear programming Power system management Energy conservation Optimization methods Power system reliability Power system measurements Human factors Quality of service Broadcasting Broadcast networks FM power reduction mixed-integer programming

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Cost Optimization of Combined Building Heating/Cooling Equipment via mixed-integer linear programming

Cost Optimization of Combined Building Heating/Cooling Equip...

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American Control Conference

作者： Michael J. Risbeck Christos T. Maravelias James B. Rawlings Robert D. Turney University of Wisconsin-Madison Johnson Controls Milwaukee WI

ISBN: (纸本)9781479917730

In this paper, we propose a mixed-integer linear program to economically optimize equipment usage in a central heating/cooling plant subject to time-of-use and demand charges for utilities. The optimization makes both discrete on/off and continuous load decisions for equipment while determining utilization of thermal energy storage systems. This formulation allows simultaneous optimization of heating and cooling subsystems, which interact directly when heat-recovery chillers are present. Nonlinear equipment models are approximated as piecewise-linear to balance modeling accuracy with the computational constraints imposed by online implementation and to ensure global optimality for the computed solutions. The chief benefits of this formulation are its ability to tightly control on/off switching of equipment, its consideration of cost contributions from auxiliary equipment such as pumps, and its applicability to large systems with multiple heating and cooling units in which a combinatorial problem must be solved to pick the optimal mix of equipment. These features result in improved performance over heuristic scheduling rules or other formulations that do not consider discrete decision variables. We show optimization results for a system with four conventional chillers, two heat-recovery chillers, and one hot water boiler. With a timestep of 1 h and a horizon of 48 h, the optimization problem can be solved to optimality within 5 minutes, indicating suitability for online implementation.

关键词： EQUIPMENT CHILLER mixed integer linear programming Demand Charges Auxiliary equipment optimality Pumps

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