检索结果-内蒙古大学图书馆

Exponential neutrosophic programming contrivance in multiple criteria for type-2 uncertain multi-objective transportation problem

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 143卷

作者： Kumar, Anesh Singh, Pitam Motilal Nehru Natl Inst Technol Allahabad Dept Math Prayagraj 211004 Uttar Pradesh India

Exploration of type-2 uncertainty in decision-making contexts is ongoing, and this article takes a pioneering approach by introducing the integration of type-2 normal uncertain parameters in the formulation of multi-objective transportation problem. The transformation of the type-2 normal uncertain model into deterministic one is achieved through methods based on expected value chance constraint programming. The problem is further investigated within a neutrosophic framework, where exponential membership functions with optimistic, pessimistic, and mixed variations are considered. The Pareto optimal solutions are obtained using truth, indeterminacy, and falsity membership functions in confluence with exponential neutrosophic programming techniques, including optimistic, pessimistic, and mixed senses. These techniques offer a flexible approach, empowering the decision-maker to select confidence levels according to their priorities. They can then analyze the Euclidean distance of the obtained optimal values of the objective functions, enhancing their understanding of the results. The benefits of employing our suggested strategies in a practical sense are clearly demonstrated via numerical experimentation, and finally, a comparison of the proposed approaches is also shown based on Euclidean distance. Furthermore, MATLAB software (version R2023b, 64-bit (maci64)) utilized to develop the code to solve the suggested issue.

关键词： Multi-fold uncertainty Multi-objective transportation problem Non-linear exponential neutrosophic programming Exponential truth Indeterminacy and falsity membership function etc

来源：评论

学校读者我要写书评

暂无评论

Sustainable and reliable design of autonomous driving lanes: A chance-constrained extended goal programming approach

引用

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 195卷

作者： Chen, Yu Wang, Wei Hua, Xuedong Wang, David Z. W. Wang, Jian Southeast Univ Sch Transportat 02 Si Pai Lou Nanjing 211189 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore

As autonomous vehicles (AVs) advance and gradually integrate into urban roadways, managing traffic in mixed environments, where human-driven vehicles (HVs) coexist with AVs, has become a critical challenge. Previous research has developed several optimal network design models for autonomous driving lanes (ADLs) to partially address this issue. However, these studies largely overlook uncertainties in AV market share or travel demand, which may lead to either excessive investment in underutilized infrastructure or traffic inefficiencies with insufficient capacity. To address these challenges, this study introduces a chance constrained programming (CCP) approach with sample approximation to effectively incorporate such uncertainties while accommodating varying risk preferences. To harmonize various sustainable development goals, such as network efficiency and social equity, CCP is further combined with extended goal programming, forming the proposed chance-constrained extended goal programming (CCEGP) model in this study for sustainable and reliable ADL design. Additionally, the routing behaviors of HV and AV users, each with differing levels of traffic awareness, are unified within a mixed crossnested logit-based stochastic user equilibrium model, thereby enhancing behavioral realism and model generalizability. The heuristic coati optimization algorithm is modified for solving, and case studies are conducted to validate its applicability.

关键词： Autonomous Vehicle Autonomous Driving Lane Uncertain AV Market Share Chance-Constrained programming Chance-Constrained Extended Goal programming Social Equity

来源：评论

学校读者我要写书评

暂无评论

Supply chain viability by integrating R-imperatives, product development, and design decisions: A stochastic programming framework

引用

OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2025年 136卷

作者： Darmian, Sobhan Mostafayi Sgarbossa, Fabio Fattahi, Mohammad Morande, Juan Pablo NTNU Dept Mech & Ind Engn Trondheim Norway Northumbria Univ Newcastle Business Sch Newcastle Upon Tyne England Auburn Univ Dept Ind & Syst Engn Auburn AL USA

This study focuses on operationalizing R-imperatives by integrating product development and design decisions in supply chains (SC) facing disruptive events. This approach leads to viable SCs that are sustainable, resilient to disruptions, adaptable to ongoing changes, and enhance circularity. We use a stochastic programming approach to propose this new planning framework. To measure SC resilience, we consider the operational cost increase during the recovery period following a potential disruption. Additionally, flexibility in product configuration after disruptions is introduced as a new contingency strategy in this study. Motivated by two European companies involved in a circular manufacturing initiative in Norway, we define disruption scenarios and assess the effectiveness of our framework. Our results show that SCs can improve both economically and in terms of circularity when R-imperatives, product development, and design decisions are integrated. Furthermore, under disruption events, SC resilience improves on average by 4.28 % and 5.07 % in these two case studies. We also highlight that budget savings from this approach can range between 2 % to 10 % when a SC adapts to increased demand.

关键词： Viability Product development Supply chain design Circular economy Two-stage stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Development of a Photo-Curing 3D Printer for Fabrication of Small-Scale Soft Robots With programming Spatial Magnetization

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2766-2773页

作者： Li, Shishi Meng, Xianghe Shen, Xingjian Wang, Jinrong Xie, Hui Harbin Inst Technol State Key Lab Robot & Syst Harbin 150080 Peoples R China

The magnetic soft robot has potential applications in biomimetic, soft interaction, and biomedical fields. However, its functionality depends on deformation patterns and locomotion modes, challenging the fabrication of complex structures with precise magnetization. Therefore, we developed a programming magnetization photo-curing 3D printer for fabrication of small-scale soft robots. The printer integrates a three-dimensional magnetic field generator (3D-MFG) and a digital light processing photo-curing system. The 3D-MFG generates a high-strength (up to 80 mT) magnetic field through Halbach arrays (x-y plane) and a solenoid (z-axis), generating arbitrary uniform magnetic field with low energy consumption. Adhesion between the printed structure and the substrate was analyzed, and A real-time force-based printing control method is presented for precise optimization of key parameters, including layer thickness, approaching force, and separation speed, enhancing overall print quality and reliability in stacking of complex three-dimensional structures. Finally, a crawling robot mimicking inchworm gait, a swimming robot with butterfly-inspired motion, and a capsule robot for targeted drug delivery were fabricated by the developed system. These experimental results validated the printer's capability to fabricate highly complex structures, advancing the practical application of small-scale soft robots in biomimetic and biomedical fields.

关键词： Three-dimensional printing Solenoids Robots Coils Magnetization Magnetic separation Soft robotics Fabrication programming Curing Soft robot materials and design additive manufacturing biologically-inspired robots

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Enabled Evolutionary Two-Stage Stochastic programming

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Pan, Jeng-Shyang Song, Pei-Cheng Chu, Shu-Chuan Snasel, Vaclav Watada, Junzo Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China VSB Tech Univ Ostrava Dept Comp Sci Ostrava Czech Republic Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Japan

Two-stage Stochastic programming (2SP) is an effective framework for decision-making and modeling under uncertainty. Some 2SP problems are challenging due to their high dimensionality and nonlinearity. Machine learning can assist in solving 2SP problems by providing data-driven insights and approximations. Evolutionary algorithms are more general and effective methods for handling various 2SP problems by exploiting their structures and features. However, there is still a research gap in combining machine learning and evolutionary algorithms for solving 2SP problems. Therefore, this paper proposes for the first time a Machine Learning-enabled Evolutionary 2SP framework (MLE2SP), which uses machine learning to construct surrogate-assisted evolutionary optimization frameworks for 2SP. It constructs a novel multi-output 2SP surrogate model that considers scenarios and decision variables of both stages for the first time and proposes a data conversion method to handle the high-dimensional decision variables and scenarios. It also proposes a Machine Learning-enabled Differential Evolution Sampling (MLDES) method to update candidate solutions, which extracts knowledge from dominant candidate solutions to guide the evolutionary direction. Moreover, this work provides open sources of linear and nonlinear two-stage stochastic mixed-integer programming problem instances as benchmark test functions. The effectiveness and generality of the proposed algorithm and framework are verified by the test results on the benchmark test functions and a disaster relief logistics problem, which provide a new research direction for designing general and effective two-stage stochastic programming solving frameworks.

关键词： Stochastic processes programming Machine learning Evolutionary computation Machine learning algorithms Linear programming Random variables Metaheuristics Heuristic algorithms Decision making Stochastic programming machine learning evolutionary computation surrogate model uncertainty

来源：评论

学校读者我要写书评

暂无评论

Exploring the Landscape of programming Language Identification With Machine Learning Approaches

引用

IEEE ACCESS 2025年 13卷 23556-23579页

作者： Verma, Amandeep Saha, Rahul Kumar, Gulshan Brighente, Alessandro Conti, Mauro Kim, Tai-Hoon Lovely Profess Univ Sch Comp Applicat Phagwara 144001 Punjab India Univ Padua Dept Math I-35131 Padua Italy Lovely Profess Univ Sch Comp Sci & Engn Phagwara 144001 Punjab India Chonnam Natl Univ Sch Elect & Comp Engn Gwangju 61186 South Korea

The increasing complexity of modern software development necessitates tools and methodologies for code analysis, maintenance, and migration in multi-language Integrated Development Environments (IDEs). The security needs of the software development process also recently led to the introduction of the Software Bill of Materials (SBOM), which vendors must include in their products. Automated tools become imperative to speed up software production and distribution. In the present code development landscape, automated programming Language Identification (PLI) helps to develop secure software development with its in-depth insights into programming consequences for code maintenance, legacy system management, code analysis, quality assurance, software modernization, migration, and code search. With the increasing success of Machine Learning (ML) models in detection methods, researchers use them for PLI showing superior performance of detection compared to the basic method of identification based on the source file extension. In this paper, we present the first survey for ML-based PLI methods providing insights into the domain's present status and guiding towards a futuristic tool development for PLI. We evaluate various ML techniques that recognize programming languages. These techniques include conventional, operation-based, and data source-based techniques. Our study examines the advantages and limitations of identifying the programming language in text, images, and videos. Our investigation also emphasizes the capabilities of the existing solutions that improve software development practices. Furthermore, our survey analyzes the strengths and limitations of existing code detection tools such as GitHub's Linguist, Pygments, ***, Ace, Google's Code Prettify, SourcererCC, Guesslang, and SonarLint. We also provide some open research problems for future researchers.

关键词： Codes Computer languages Software programming Feature extraction Syntactics Surveys Source coding Maintenance Machine learning programming language detection machine learning code software

来源：评论

学校读者我要写书评

暂无评论

A Column-Parallel On-programming Pixel-Current Readout Embedded in an OLED Display Driver IC

引用

IEEE JOURNAL OF SOLID-STATE CIRCUITS 2025年第4期60卷 1174-1189页

作者： Lim, Gyu-Wan Kim, Dong-Kyu Kang, Gyeong-Gu Shin, Seunghwa Kim, Kihyun Park, Yousung Kim, Won Kim, Young-Bok Jeon, Hyun-Kyu Kim, Hyun-Sik Korea Adv Inst Sci & Technol KAIST Sch Elect Engn Daejeon 34141 South Korea LX Semicon Co Ltd Seoul 06267 South Korea

This article presents an organic light-emitting diode (OLED) driver IC that embeds a column-parallel real-time pixel-current readout circuit for sensing current during actual display operation. It features a folding integrator (FI) for high sensitivity and wide dynamic range (DR) in current sensing, along with a source-driven pixel and an AFE-combined voltage driver (ACD) to facilitate current sensing during display data programming. A channel-shared common-mode noise rejection filter (CMRF) is also presented to effectively suppress channel-common panel noise. The 16-channel prototype chip was fabricated using a 180-nm CMOS process. Measurement results show that the FI achieves 80-mV/nA sensitivity and a 16-fold DR extension, while the CMRF offers up to 63 dB of noise rejection. The 11-bit current readout exhibits a maximum differential nonlinearity (DNL) of 0.33 LSB, an integral nonlinearity (INL) of 1.7 LSB, an rms noise of 1.98 digital number (DN), and a signal-to-noise ratio (SNR) of 60.3 dB. Real demonstrations with LED also validate the pixel-current sensing functionality and performance of the proposed chip.

关键词： Organic light emitting diodes Thin film transistors Voltage Sensors Sensitivity Noise Real-time systems programming Manufacturing Information and communication technology Common-mode noise rejection current sensing display driver IC folding integrator (FI) full-scale range non-uniformity compensation organic light-emitting diode (OLED) readout real time source-driven pixel thin-film transistor (TFT)

来源：评论

学校读者我要写书评

暂无评论

Surrogate-Assisted Scenario-Generation Method for Simulation-Based Stochastic programming Problems

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13161-13174页

作者： Suemitsu, Issei Izui, Kazuhiro Kyoto Univ Dept Micro Engn Nishikyo Ku Kyoto 6158540 Japan Hitachi Ltd Ctr Technol Innovat Res & Dev Grp Kokubunji Tokyo 1858601 Japan

Simulation is required in real-world industrial decision-making to model the complexity, such as supply chain management with uncertain future demand. Scenario generation is critical for handling inherent uncertainties as deterministic scenarios to solve stochastic programming problems (SPPs). Generating a minimal yet representative scenario set is important to solve simulation-based SPPs (SBSPPs) since SBSPPs require thousands of simulation iterations proportional to the number of scenarios, leading to significant computational time. However, conventional methods, such as Monte Carlo method and recent problem-driven approaches, are ineffective in solving SBSPPs due to time-consuming simulation evaluation. This paper proposes a surrogate-assisted scenario-generation method called Inferred Cost-Space Scenario Clustering (ICSSC) that is applicable to various SPPs including SBSPPs. ICSSC employs a scenario clustering based on a new cost-space scenario distance evaluated by the surrogate model trained with offline simulation data to quickly approximate simulation evaluations. We conducted three types of numerical experiments to validate the effectiveness: Markowitz portfolio optimization, stochastic server location, and inventory placement optimization. Empirical results revealed that ICSSC could generate an effective scenario set based on the impact of uncertainties on decision outcomes for broader SPPs, and yields better solutions with 7.2 times shorter runtime than Monte Carlo methods.

关键词： Uncertainty Stochastic processes Optimization Scenario generation programming Probability distribution Monte Carlo methods Computational modeling Decision making Supply chain management Planning under uncertainty optimization and optimal control AI-based methods inventory management

来源：评论

学校读者我要写书评

暂无评论

Fast Probabilistic Energy Flow Calculation for Natural Gas Systems: A Convex Multiparametric programming Approach

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 6786-6796页

作者： Jia, Wenhao Ding, Tao Yuan, Yi Zhang, Hongji Xi An Jiao Tong Univ Dept Elect Engn Xian 710049 Shaanxi Peoples R China

Probabilistic energy flow (PEF) calculation is a fundamental task for the operation and planning of both natural gas systems (NGSs) and integrated energy systems considering uncertainties. Traditional Monte Carlo simulation (MCS) based PEF approach requires repeated energy flow calculations based on a large number of random samples, leading to a huge computation burden. Hence, this paper proposes a convex multiparametric programming (MPP) based fast PEF calculation method for NGSs. First, we develop an energy function based convex optimization model whose optimal solution is equivalent to the solution of deterministic energy flow equations. Then, we propose a convex MPP model with uncertain boundary conditions (such as the gas injections/loads) as the parameters. A multiparametric quadratic approximation algorithm is further introduced to solve the proposed MPP and obtain the analytical energy flow expression. This analytical expression characterizes the mapping relationship between the energy flow solution and uncertain parameters. Finally, we establish an online PEF calculation framework, in which the repeat energy flow calculations can be efficiently performed by merely substituting the uncertain parameters into the analytical energy flow expression obtained offline. Case studies on multiple NGSs verify the effectiveness of the proposed method. Note to Practitioners-This paper introduces a novel approach for efficient PEF calculation in NGSs that addresses the computational challenges posed by traditional methods. Conventional MCS-based PEF approaches involve numerous energy flow calculations with massive stochastic scenarios, resulting in significant computational burdens. The key contribution of our work is to equivalently reformulate the original PEF problem as an energy function based convex MPP model, which incorporates uncertain boundary conditions (e.g., gas injections/loads) as its parameters. Then, a multiparametric quadratic approximation algorithm is s

关键词： Mathematical models Boundary conditions Natural gas programming Probabilistic logic Iterative methods Computational efficiency Integrated energy systems natural gas systems probabilistic energy flow multiparametric programming

来源：评论

学校读者我要写书评

暂无评论

Design of closed-loop manufacturing-remanufacturing system network considering uncertain defect and waste rate: Scenario-based chance-constrained programming

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 259卷

作者： Wang, Gaolong Wang, Yue Li, Xinchao Lu, Shan Liaoning Petrochem Univ Sch Informat & Control Engn Fushun 113001 Peoples R China Shenzhen Polytech Univ Inst Intelligence Sci & Engn Shenzhen 518055 Peoples R China

To facilitate sustainability by extending the life-cycle and residual value of production materials, a collaborate scheduling scheme for closed-loop manufacturing system with disturbance uncertainty is studied. Based on the features of the closed-loop manufacturing system under multi-product and multi-period scenarios, a mixedinteger linear optimization model is developed, where manufacturing and remanufacturing units are coordinated by recycling the production materials from work-in-process and final products. Based on this, a novel closed-loop manufacturing-remanufacturing scheduling framework is proposed. Additionally, in response to variations in product quality and processing capabilities, a scenario-based chance-constrained programming is introduced to address potential sources of uncertainty. Subsequently, a sequential optimization method is devised to reduce computational complexity. Finally, an industrial case from the electronic factory is investigated to demonstrate its applicability by implementing the proposed approach with comparison to several other strategies.

关键词： Closed-loop manufacturing-remanufacturing system Mixed-integer linear optimization Scenario-based chance-constrained programming

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：