检索结果-内蒙古大学图书馆

multi-objective optimization in the construction of steel-concrete composite columns with carbon emission considerations: Pareto front development and decision-making

引用

AIN SHAMS ENGINEERING JOURNAL 2025年第2期16卷

作者： Zhan, Zhengjie Xia, Pan Xia, Dongtao Hu, Yan Hubei Univ Technol Sch Civil Engn Architecture & Environm Wuhan 430068 Peoples R China Hubei Univ Technol Engn & Technol Coll Dept Civil Engn Wuhan 430068 Peoples R China Hubei Univ Technol Key Lab Intelligent Hlth Percept & Ecol Restorat R Minist Educ Wuhan 430068 Peoples R China

This study tackles the multi-objective optimization (MOP) challenges in constructing steel-concrete columns by introducing a novel TCQE model that considers time, cost, quality, and carbon emissions. Employing relative deviation theory with dynamic weighting, the model normalizes MOP and applies an improved ant colony algorithm (IACA) to generate optimized solutions. Empirical research validates the model's efficiency and applicability. Furthermore, a decision-making framework based on AHP-TOPSIS is proposed, demonstrating superior weight distribution and fairness in the decision process. Compared to the ideal point method and VIKOR, the proposed approach consistently identifies optimal solutions, affirming its scientific validity and effectiveness. The findings suggest broad application prospects in practical construction projects and provide valuable insights for construction management research, highlighting the theoretical and practical significance of the model and framework.

关键词： Steel-Concrete Composite Column Ant colony algorithm Carbon emissions multi-objective optimization Decision-making

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of the gas valve and liquid piston in the ionic liquid hydrogen compressor

引用

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 118卷 312-322页

作者： Wang, Lingzi Liao, Yiling Liu, Xiaoming Guo, Yi Feng, Jianmei Peng, Xueyuan Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Peoples R China State Key Lab High End Compressor & Syst Technol Hefei 230031 Anhui Peoples R China Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China

This paper presents a multi-objective optimization for the self-acting valve of the ionic liquid hydrogen compressor. A Taguchi method was employed to investigate the effects of four design parameters on the performance indicators of energy storage (E) and isothermal efficiency (eta). The signal-to-noise ratio (SNR) and mean signal-to-noise ratio (MSNR), along with analysis of variance (ANOVA), were used to identify the critical design parameters and their contribution rates to the compressor's performance. A bi-objective optimization of E and eta was conducted with grey relational analysis (GRA) employed to obtain the parameter combination closest to the optimal setting. The results indicated that the Mach number had the greatest impact on E and eta, with contribution rates of 61.59 % and 81.85 %, respectively. The optimal design parameters were identified as spring stiffness of 100 N/m, Mach number of 0.015, valve disc density of 8.9 g/cm3, and initial liquid piston height of 50 mm.

关键词： multi-objective optimization Hydrogen energy Ionic liquid compressor Grey relational analysis Self-acting valve

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization algorithms for freight allocation in a food grain supply chain

引用

APPLIED SOFT COMPUTING 2025年 171卷

作者： Anoop, K. P. Panicker, Vinay V. Siby, Jerin Aryadutt, C. S. Natl Inst Technol Calicut Dept Mech Engn Supply Chain & Syst Simulat Lab Kozhikode 673601 India

This article addresses multi-objective optimization of a freight allocation problem and presents the case of a food grain organization in India (FOI). The inventory and warehouse parameters that are relevant in the regional level allocation of food grains (using freight trains) are represented using three penalty factors, namely rake penalty factor, capacity utilization penalty factor, and weekly penalty factor. The article formulates a tri-objective optimization model to minimize each of the three penalty factors. Two customized multi-objective optimization algorithms are developed based on multi-objective Simulated Annealing (MOSA) and Elitist Non-dominated Sorting Genetic Algorithm II (NSGA II) to solve the formulated model. The algorithms are tested and validated via computational experiments designed using historical data collected from the FOI. The algorithms help the transportation managers at the FOI to generate improved and balanced transportation plans (with respect to the three objectives) in a quick time. Further, the performance of the algorithms is compared based on seven different performance metrics reported in literature. The MOSA-based algorithm performs equally or better than the NSGA II-based algorithm with respect to four performance metrics.

关键词： Food grain supply chain multi-objective optimization Freight allocation multi-objective simulated annealing Non-dominated sorting genetic algorithm II

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of multi-energy complementary systems integrated biomass-solar-wind energy utilization in rural areas

引用

ENERGY CONVERSION AND MANAGEMENT 2025年 323卷

作者： Chen, Min Wei, Jiayuan Yang, Xianting Fu, Qiang Wang, Qingyu Qiao, Sijia Wuhan Univ Sci & Technol Sch Urban Construct Wuhan Peoples R China Wuhan Univ Sci & Technol Hubei Prov Engn Res Ctr Urban Regenerat Wuhan Peoples R China

Rural areas possess abundant renewable energy sources, such as solar and biomass energy;however, the current methods of energy utilization suffer from low efficiency and serious pollution issues. As rural residents' living standards continue to improve, there is an urgent need to optimize and adjust the structure of rural energy systems. multi-energy complementary systems (MECS) have the potential to enhance energy utilization efficiency, achieve high efficiency and energy savings, significantly reduce carbon emissions, and effectively address the challenges faced by rural energy development. This study explores a typical framework for rural MECS that integrates photovoltaic, wind turbine, and biomass biogas combined cooling, heating, and power technology while considering the partial load ratio of equipment components and coupling characteristics between different energy sources. Based on various scenarios of valley electricity utilization, multi-objective optimization models are established to determine the capacity of MECS with economy, environment, and primary energy saving rate as objective functions. The non-dominated sorting genetic algorithm (NSGA-II) along with Technique for Order Preference by Similarity to Ideal Solution decision-making method is adopted to obtain optimal solutions from the Pareto solution set. The case study conducted in a rural area of central China has demonstrated the effective enhancement of coupling capacity in MECS through battery storage. By actively storing energy during off-peak electricity periods, battery storage strengthens the complementary capabilities of photovoltaic systems, wind turbines, and itself. This approach allows for a reduction in planned capacity for photovoltaic and wind power systems within MECS while increasing the planned capacity for internal combustion engines, resulting in respective decreases in system investment costs by 16.19% and 13.18%. Furthermore, incorporating more biogas-fired cogeneration durin

关键词： multi-objective optimization multi-energy complementary systems Integrated biomass-solar-wind energy utiliza- tion Partial load ratio Rural areas

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of Residential Block Space Morphology in Xingtai City Under Energy-Saving Orientation

引用

BUILDINGS 2025年第7期15卷 1028-1028页

作者： Tian, Fang Huang, Tengfei Huang, Wenhao Hebei Univ Engn Sch Architecture & Art Handan 056038 Peoples R China

Studies have shown that the morphology of residential blocks has a significant impact on the buildings' energy use intensity (EUI), solar energy utilization potential (SEUP), and average sunlight hours (ASH). This paper utilizes the Rhino and Grasshopper platforms, employing the Wallacei multi-objective optimization algorithm, to study the relationship between the morphology of residential blocks in Xingtai City, EUI, SEUP, and ASH. First, 108 residential blocks in Xingtai City were surveyed, based on varying design criteria, they were classified into three categories: multi-story, high-rise Type I, and high-rise Type II. Next, after integrating microclimatic factors, the Wallacei multi-objective optimization algorithm was employed to optimize three objectives: EUI, SEUP, and ASH. Finally, the simulation results were subjected to a quantitative analysis using statistical methods, such as K-means clustering. The spatial morphology of residential blocks had a maximum impact of 11.69% on EUI, 39.8% on SEUP, and 36.85% on ASH. Therefore, energy saving can be achieved by controlling the building density, average number of floors, building shape factor and other morphological indicators of residential blocks.

关键词： multi-objective optimization energy conservation oriented microclimate residential block form optimization

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of Injection Molding Process Parameters for Junction Boxes Based on BP Neural Network and NSGA-II Algorithm

引用

MATERIALS 2025年第3期18卷 577-577页

作者： Hong, Tengjiao Huang, Dong Ding, Fengjuan Zhang, Liyong Dong, Fulong Chen, Lei Anhui Sci & Technol Univ Coll Intelligent Mfg Chuzhou 233100 Peoples R China Stamford Int Univ Sch Business Adm Bangkok 10250 Thailand Fengyang Cty Sci & Technol Innovat Serv Ctr Chuzhou 233100 Peoples R China

Many factors affect the quality of the injection molding of plastic products, including the process parameters, mold materials, type and geometry of plastic parts, cooling system, pouring system, etc. A multi-objective optimization method for injection molding process parameters based on the BP neural network and NSGA-II algorithm is proposed to address the problem of product quality defects caused by unreasonable process parameter settings. Taking the junction box shell as the object, numerical simulation was carried out using Moldflow2019 software and a six-factor five-level orthogonal experiment was designed to explore the influence of injection molding process parameters, such as the mold temperature, melt temperature, injection pressure, holding pressure, holding time, and cooling time, on the volume shrinkage rate and warpage deformation of the junction box. Based on a numerical simulation, the BP neural network and NSGA-II algorithm were used to optimize the optimal combination of injection molding process parameters, volume shrinkage rate, and warpage deformation. The research results indicate that the melt temperature has the most significant impact on the quality of the injection molding of junction boxes, followed by the holding time, holding pressure, cooling time, injection pressure, and mold temperature. After optimization using the BP neural network and the NSGA-II algorithm, the optimal process parameter combination was obtained with a melt temperature of 230.03 degrees C, a mold temperature of 51.27 degrees C, an injection pressure of 49.13 MPa, a holding pressure of 69.01 MPa, a holding time of 15.48 s, and a cooling time of 34.91 s. At this time, the volume shrinkage rate and warpage deformation of the junction box were 6.905% and 0.991 mm, respectively, which decreased by 33.2% and 3.8% compared to the average volume shrinkage rate (10.34884%) and warpage deformation (1.030764 mm) before optimization. The optimization effect was significant. In a

关键词： junction box warping deformation volume shrinkage rate BP neural network multi-objective optimization process parameters

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of work package scheme problem to minimize project carbon emissions and cost

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Zhang, Yaning Li, Xiao Teng, Yue Shen, Geoffrey Q. P. Bai, Sijun Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China Northwestern Polytech Univ Sch Management Xian Peoples R China Univ Hong Kong Dept Civil Engn Hong Kong Peoples R China

The construction industry accounts for around 30% of global energy consumption and 33% of CO2 emissions. For the carbon neutrality initiative, reducing carbon emissions from construction projects become a critical objective for project success. However, a dilemma arises in balancing carbon emissions and project cost, particularly during the work package-based project planning phase. To address this issue, this article presents a novel multiobjective optimization model for the work package scheme problem, aimed at minimizing both project carbon emissions and cost. multi-objective Evolutionary Algorithms (EAs) are developed to solve the model. Firstly, a multi-objective Mixed-Integer Programming (MIP) model is developed to establish the functional relation between work package attributes (duration and work content) and optimization objectives (carbon emissions and cost). Secondly, two multi-objective optimization EAs, NSGA-II and SPEA2, are developed to obtain the Pareto frontier. The experimental results indicate that NSGA-II and SPEA2 exhibit superior trade-off capabilities compared to the Gurobi and the state-of-the-art heuristic algorithm. Compared to Gurobi, the proposed EAs achieve an approximately 68% reduction in carbon emissions, accompanied by about an 11% cost increase. Compared to the heuristic algorithm, the EAs achieve around 10% reductions in carbon emissions with an approximately 5% cost increase. Additionally, sensitivity analysis conducted on a project instance dataset demonstrates the robustness of the proposed model and algorithms. This article paves the way for achieving lowcarbon and sustainable construction project management in the context of carbon neutrality.

关键词： multi-objective optimization Project planning Work packages Carbon emissions Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization for CF/PPS-stainless steel induction welding with pin structure based on SSA-BP neural network

引用

ADVANCED COMPOSITE MATERIALS 2025年

作者： Li, Hao Pang, Leishuo Wang, Wenchao Yu, Tian Zhang, Zijian Li, Shipeng Tianjin Univ Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300072 Peoples R China Aerosp Precis Prod Inc Ltd Henan Key Lab Fastening Connect Technol Xinyang 464000 Henan Peoples R China

Carbon Fiber Reinforced Thermoplastic Plastic (CFRTP) has been increasingly used in aerospace and automotive manufacturing with its excellent mechanical properties. Based on the melt-curing characteristics of thermoplastic composites, combining the full-thickness reinforced joining technology with induction welding can provide an effective way for high-strength joining. In this paper, the full factorial experimental design method is used to deeply explore the influence law of welding time, consolidation force and heating current on the tensile properties of welded joints. Combined with Sparrow Search Algorithm (SSA) and BP neural network, a welding joint tensile strength prediction model was constructed. In addition, a multi-objective model based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was developed. The multi-objective optimization method of TOPSIS was used to select the optimal parameter combinations with ultimate tensile strength and first debonding strength as the optimization objectives: welding time of 29.995 min, consolidation force of 638.669 N, and heating current of 443.351 A. Experimental studies have shown that the optimized welding joints have an increase in ultimate tensile strength of up to 32.4 % and an increase in first time debonding strength of up to 47.0 % with respect to the non-optimized welding joints.

关键词： Cfrtp induction welding ssa-bp neural network multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of counter-flow dew-point evaporative coolers for multi-scenario applications using non-dominated sorting genetic algorithm II

引用

APPLIED THERMAL ENGINEERING 2025年 262卷

作者： Li, Yongcai Jing, Chengjun Sichuan Univ Coll Architecture & Environm Chengdu 610065 Peoples R China

As a green and low-carbon cooling technology, the improvement of evaporative cooling performance has always been the focus of attention. However, traditional research often lacks concurrent consideration of trade-offs in design parameters and multi-objective contradictions, focusing on unilateral decision-making and localized optimization in fixed environments. Therefore, this study innovatively couples non-dominated sorting genetic algorithm II with mathematical modeling. Based on MATLAB programming, a complex multi-objective optimization model of counter-flow dew-point evaporative cooler capable of parameter prediction, multi-scenario application and multi-dimensional optimization is developed. To reveal the driving mechanism of multivariable on performance parameters, comparative studies of single-objective optimization under three decision modes for two typical environments are reported. The results indicate that the better optimization is achieved by adopting five decision variables with a dew-point efficiency of 98.25 %. Compared with the original working condition, the cooling capacity and dew-point efficiency could be unilaterally increased by 128.73 % and 121.28 %, respectively, and the corresponding increment of space utilization rate reaches up to 56.39 % and 56.11 %. Subsequently, setting the cooling capacity, dew-point efficiency and fan energy consumption as the synergistic objective function, a trade-off optimization with multi-decision variables is performed for four different latitudes. The obtained Pareto frontier could flexibly invert the most potential structural and operation parameter recommendations. Especially in dry regions, a cooling capacity of 4800 W and a dew-point efficiency of 91.2 % could be realized. The method realizes the controllability of performance parameters and adjustability of energy-saving effect, which could provide a solution for the efficient design of cooling equipment.

关键词： Counter-flow dew-point evaporative cooling Model construction multi-objective optimization Non-dominated sorting genetic algorithm II Performance improvement

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of aluminum foam-filled battery boxes for electric vehicle safety

引用

LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2025年第3期22卷

作者： Yay, Ismail Demirci, Emre Ozcan, Ahmet Remzi Bursa Tech Univ Dept Mech Engn TR-16310 Bursa Turkiye Bursa Tech Univ Automot Applicat & Res Ctr TR-16310 Bursa Turkiye Bursa Tech Univ Dept Mechatron Engn TR-16310 Bursa Turkiye

In this study, a multi-objective optimization methodology is used to assess the crashworthiness of an aluminum foam-filled battery box designed for passenger cars. Unlike most research focusing on axial crushing, this work investigates the less-explored side pole impact scenario in electric vehicle battery boxes. Finite element simulations are conducted to reduce peak crushing force (PCF) and increase specific energy absorption (SEA) compared to the initial design. Key design variables include aluminum foam densities, wall thickness, and cross-sectional dimensions of battery box components. Four surrogate models are evaluated to approximate the simulation results, and the Non-Dominated Sorting Genetic Algorithm (NSGA-II) is employed to achieve optimal outcomes. The results show that the optimized design significantly improves crashworthiness, achieving a 50.71% increase in SEA and an 11.56% reduction in PCF. Foam density plays a crucial role in controlling deformation behavior under impact conditions. These findings offer a new approach to designing battery boxes with enhanced crashworthiness for electric vehicles.

关键词： Aluminum foam battery box crashworthiness electric vehicle multi-objective optimization

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：