检索结果-内蒙古大学图书馆

A New Multicommodity Network Flow Model and Branch and Cut for Optimal Quantum Boolean Circuit Synthesis

INFORMS JOURNAL ON COMPUTING 2025年第1期37卷 1-196, C2页

作者： Jung, Jihye Choi, In-Chan Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Korea Univ Div Ind Management Engn Seoul 02841 South Korea

This study introduces a new optimization model and a branch-and-cut approach for synthesizing optimal quantum circuits for reversible Boolean functions, which are pivotal components in quantum algorithms. Although heuristic algorithms have been extensively explored for quantum circuit synthesis, research on exact counterparts remains relatively limited. However, the need to design quantum circuits with guaranteed optimality is increasing, especially for improving computational fidelity on noisy intermediate-scale quantum devices. This study presents mathematical optimization as a viable option for optimal synthesis, with the potential to accommodate practical considerations arising in fast-evolving quantum technologies. We set a demonstrative problem to implement reversible Boolean functions using high-level gates known as multiple control Toffoli gates while minimizing a technology-based proxy called quantum cost-the number of low-level gates used to realize each high-level gate. To address this problem, we propose a discrete optimization model based on a multicommodity network and discuss potential future variations at an abstract level to incorporate technical considerations. A customized branch and cut is then developed upon different aspects of our model, including polyhedron integrality, surrogate constraints, and variable prioritization. Our experiments demonstrate the robustness of the proposed approach in finding cost-optimal circuits for all benchmark instances within a two-hour time frame. Furthermore, we present interesting intuitions from these experiments and compare our computational results with relevant studies, highlighting newly discovered circuits with the lowest quantum costs reported in this paper.

关键词： mixed-integer programming multicommodity network branch and cut quantum computing circuit synthesis

来源：评论

学校读者我要写书评

暂无评论

Resource allocation for Hyphantria cunea invasive management:a novel simulation-based optimization model

引用

APPLIED MATHEMATICAL MODELLING 2025年 138卷

作者： Zhang, Shuhua Liu, Ming Wang, Peng Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Peoples R China Jiangsu Prov & Chinese Acad Sci Inst Bot Jiangsu Key Lab Res & Utilizat Plant Resources Nanjing 210014 Peoples R China

In this study, we propose an integrated mixed-integer programming (MIP) model for allocating a limited budget to control a destructive invasive insect endangering the forest vegetation, Hyphantria cunea (H. cunea). Our model seeks to minimize the number of infected and dead trees within a preset planning period, factoring in various infestation scenarios, migration behaviors, resource limitations, intervention timing, and monitoring capabilities. Our modeling framework is novel in the sense that it depicts the transmission process of H. cunea as an infectious compartmental model. Our optimization model is also meaningful because it innovatively bridges the spread dynamics of H. cunea and the optimal resource allocation by using the time-varying number of infected trees that can be accepted in testing and surveillance. Our numerical test data and parameter settings have been collected from large-scale field surveys on H. cunea in Jiangsu Province over the past three years. The scenarios-based method offers significant computational advantages in searching for the best alternatives to real-world size problems in a rational time. Furthermore, our test results specify that the proposed model can not only aid in controlling H. cunea, but also can be adopted as a potential tool for managing other invasive species in the future.

关键词： Invasive species management Resources allocation Evolutionary dynamics Scenarios-based optimization mixed-integer programming

来源：评论

学校读者我要写书评

暂无评论

Day-to-Day Integrated Optimization of Bus Transit Maintenance and Vehicle Scheduling

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第3期26卷 3814-3830页

作者： He, Yanbo Zhang, Wenyi Liu, Tao Ma, Jihui Sun, Huijun Beijing Jiaotong Univ Key Lab Transport Ind Big Data Applicat Technol Co Minist Transport Beijing 100044 Peoples R China Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Peoples R China Natl United Engn Lab Integrated & Intelligent Tran Chengdu 611756 Peoples R China Southwest Jiaotong Univ Yibin Res Inst Yibin 644000 Peoples R China Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China

This study addresses integrated bus maintenance and vehicle scheduling in a day-to-day dynamic operation setting to ensure that regular preventative maintenance and newly identified predictive maintenance needs are handled promptly. The problem is formulated as a mixed-integer linear programming aiming to minimize operational costs and risks over a day-to-day rolling planning horizon. A two-stage decomposition (TSD) method is proposed to solve this challenging problem. In the first stage, bus maintenance is scheduled on a daily basis. The second stage then optimizes specific bus maintenance and vehicle schedules within each day. With such a problem decomposition, the computational complexity is significantly reduced as the within-day problems become independent. Computational experiments conducted on a real-world bus line demonstrate the effectiveness and superiority of the TSD method. Compared to a one-step solution method and an algorithm in a similar existing study, the proposed TSD method consumes far less computation time, while delivering high-quality solutions. Moreover, the TSD solution method can be further greatly accelerated if the within-day problems of the second stage are solved in parallel with different computers. The computational results also highlight the benefits of the proposed two-stage optimization approach in enhancing operational cost-efficiency and bus resource utilization.

关键词： Public transit bus maintenance vehicle scheduling mixed-integer programming model decomposition

来源：评论

学校读者我要写书评

暂无评论

mixed logical dynamical modeling and model predictive control of piecewise affine systems with dead zone constraints

引用

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年第7期35卷 2816-2834页

作者： Cai, Xu Zhang, Xin Lou, Xuyang Wu, Wei Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi Peoples R China Jiangnan Univ Sch IOT Engn Wuxi Peoples R China Jiangnan Univ Sch IOT Engn Wuxi 214122 Peoples R China

This paper is concerned with model predictive control of piecewise affine systems with dead zone constraints based on the mixed logical dynamical modeling approach. A predictive control strategy based on a mixed logic dynamical model is proposed. The strategy contains two steps: the first step is to transform the piecewise affine systems into mixed logic dynamical models;the second step is to apply a predictive control scheme based on this model. Then, the optimal control sequence can be obtained by solving the constructed mixed-integer quadratic/linear programming problem. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approaches.

关键词： mixed logic dynamical model mixed-integer programming model predictive control piecewise affine system

来源：评论

学校读者我要写书评

暂无评论

Decomposing a renewable energy design and dispatch model

引用

OPTIMIZATION AND ENGINEERING 2025年第1期26卷 613-653页

作者： Wales, Jesse Zolan, Alexander Flamand, Tuelay Newman, Alexandra Air Force Inst Techol Dayton OH 45433 USA Natl Renewable Energy Lab Golden CO USA Univ Colorado Denver Denver CO USA Colorado Sch Mines Golden CO 80401 USA

We address a mixed-integer linear programming model which selects a cost-minimizing set of available technologies with which to design a renewable energy system and prescribe their associated dispatch decisions. Realistically sized instances of such models pose computational challenges. To this end, we develop a Lagrangian heuristic based on a decomposition methodology which partitions the model into blocks and optimizes these more manageable, smaller subproblems. It also provides a lower bound to assess solution quality. We apply this methodology to the National Renewable Energy Laboratory's Renewable Energy Integration and Optimization (REoptTM\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {REopt}<^>{\textrm{TM}}$$\end{document}) model to generate near-optimal solutions to realistic instances containing, on average, approximately 300,000 variables and at least as many constraints, with a mean 30% optimality gap improvement using a five-minute solution time limit, compared to directly solving the original monolith.

关键词： Large-scale optimization Block decomposition mixed-integer programming Renewable energy

来源：评论

学校读者我要写书评

暂无评论

Feed-forward neural networks as a mixed-integer program

引用

ENGINEERING WITH COMPUTERS 2025年 1-19页

作者： Aftabi, Navid Moradi, Nima Mahroo, Fatemeh Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA Concordia Univ Informat Syst Engn Montreal PQ Canada Clemson Univ Dept Ind Engn Clemson SC 29634 USA

Deep neural networks (DNNs) are widely studied in various applications. A DNN consists of layers of neurons that compute affine combinations, apply nonlinear operations, and produce corresponding activations. The rectified linear unit (ReLU) is a typical nonlinear operator, outputting the max of its input and zero. In scenarios like max pooling, where multiple input values are involved, a fixed-parameter DNN can be modeled as a mixed-integer program (MIP). This formulation, with continuous variables representing unit outputs and binary variables for ReLU activation, finds applications across diverse domains. This study explores the formulation of trained ReLU neurons as MIP and applied MIP models for training neural networks (NNs). Specifically, it investigates interactions between MIP techniques and various NN architectures, including binary DNNs (employing step activation functions) and binarized DNNs (with weights and activations limited to -1,0,+1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-1,0,+1$$\end{document}). The research focuses on training and evaluating proposed approaches through experiments on handwritten digit classification models. The comparative study assesses the performance of trained ReLU NNs, shedding light on the effectiveness of MIP formulations in enhancing training processes for NNs.

关键词： mixed-integer programming Deep learning Neural networks Optimization Operations research

来源：评论

学校读者我要写书评

暂无评论

integer programming approaches for distributionally robust chance constraints with adjustable risks

引用

COMPUTERS & OPERATIONS RESEARCH 2025年 177卷

作者： Zhang, Yiling Univ Minnesota Ind & Syst Engn Minneapolis MN 55455 USA

We study distributionally robust chance-constrained programs (DRCCPs) with individual chance constraints under a Wasserstein ambiguity. The DRCCPs treat the risk tolerances associated with the distributionally robust chance constraints (DRCCs) as decision variables to trade off between the system cost and risk of violations by penalizing the risk tolerances in the objective function. We develop integer programming approaches for individual chance constraints with uncertainty either on the right-hand side or on the left-hand side. In particular, we derive mixed integer programming reformulations for the two types of uncertainty to determine the optimal risk tolerance for the chance constraint. Valid inequalities are derived to strengthen the formulations. We test diverse instances of diverse sizes.

关键词： Distributionally robust optimization Chance-constrained programming Wasserstein metric mixed-integer programming Adjustable risk

来源：评论

学校读者我要写书评

暂无评论

ESG-based index tracking with portfolio policy

引用

JOURNAL OF CLEANER PRODUCTION 2025年 492卷

作者： Neghab, Davood Pirayesh Moradi, Mohammad Cevik, Mucahit Bradrania, Reza Toronto Metropolitan Univ Dept Mech & Ind Engn Toronto ON Canada Sharif Univ Technol Tehran Iran Univ South Australia UniSA Business Adelaide Australia

We incorporate socially responsible investment goals in the index tracking portfolio problem. We propose a comprehensive decision-making framework to construct an ESG-based portfolio to track the market index while ensuring ESG performance at a specific confidence level. To solve the problem, we develop a hybrid method combining genetic algorithms and chance-constrained optimization. Moreover, we estimate a linear portfolio policy to determine the portfolio weights directly using stock-level characteristics. We provide the empirical application of the proposed method using the constituents of the S&P 500 index and quantify the trade-off between the tracking error and the ESG value of the portfolios. The results indicate that imposing ESG criteria at the highest confidence level increases the annualized mean absolute deviation (MAD) by more than 14.5% compared to non-ESG-based portfolios. However, the significance of this trade-off increases with a smaller portfolio size and a shorter investment horizon. Furthermore, the empirical analysis suggests that the size of the firms is more important than their value and momentum in portfolio rebalancing. The proposed system guides investors in their portfolio choice to set targets that best match their ethical as well as monetary goals.

关键词： Index tracking ESG value Genetic algorithms Chance-constrained optimization mixed-integer programming Portfolio construction

来源：评论

学校读者我要写书评

暂无评论

Bilevel optimization approach for fuel treatment planning

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第1期320卷 205-218页

作者： Lagos, Tomas Choi, Junyeong Segundo, Brittany Gan, Jianbang Ntaimo, Lewis Prokopyev, Oleg A. Univ Sydney Discipline Business Analyt Sydney NSW 2000 Australia Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA Texas A&M Univ Dept Ecol & Conservat Biol College Stn TX 77843 USA Texas A&M Univ Wm Michael Dept Ind & Syst Engn Barnes 64 College Stn TX 77843 USA Univ Zurich Dept Business Adm CH-8032 Zurich Switzerland

Various fuel treatment practices involve removing all or some of the vegetation (fuel) from a landscape to reduce the potential for fires and their severity. Fuel treatments form the first line of defense against large-scale wildfires. In this study, we formulate and solve a bilevel integer programming model, where the fuel treatment planner (modeled as the leader) determines appropriate locations and types of treatments to minimize expected losses from wildfires. The follower (i.e., the lower-level decision-maker) corresponds to nature, which is adversarial to the leader and designs a wildfire attack (i.e., locations and time periods, where and when, respectively, wildfires occur) to disrupt the leader's objective function, e.g., the total expected area burnt. Both levels in the model involve integrality restrictions for decision variables;hence, we explore the model's difficulty from the computational complexity perspective. Then, we design specialized solution methods for general and some special cases. We perform experiments with semi-synthetic and real-life instances to illustrate the performance of our approaches. We also explore numerically the fundamental differences in the structural properties of solutions arising from bilevel model and its single-level counterpart. These disparities encompass factors like the types of treatments applied and the choice of treated areas. Additionally, we conduct various types of sensitivity analysis on the performance of the obtained policies and illustrate the value of the bilevel solutions.

关键词： Bilevel optimization mixed-integer programming Cutting plane Wildfire prevention Global optimization

来源：评论

学校读者我要写书评

暂无评论

Reduction of losses in electric power distribution system-dynamic reconfiguration case study

引用

Control Theory and Technology 2025年第1期23卷 49-63页

作者： Branimir Novoselnik Drago Bago Jadranko Matuško Mato Baotić Faculty of Electrical Engineering and Computing University of ZagrebUnska 3Zagreb10000Croatia Faculty of Mechanical Engineering Computing and Electrical EngineeringUniversity of MostarMatice Hrvatske bbMostar88000Bosnia and Herzegovina

This paper deals with reduction of losses in electric power distribution system through a dynamic reconfiguration case study of a grid in the city of Mostar,Bosnia and *** proposed solution is based on a nonlinear model predictive control algorithm which determines the optimal switching operations of the distribution *** goal of the control algorithm is to find the optimal radial network topology which minimizes cumulative active power losses and maximizes voltages across the network while simultaneously satisfying all system *** optimization results are validated through multiple simulations(using real power demand data collected for a few characteristic days during winter and summer)which demonstrate the efficiency and usefulness of the developed control algorithm in reducing the grid losses by up to 14%.

关键词： Nonlinear model predictive control Dynamic reconfiguration Power distribution system mixed-integer programming Real-life case study

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：