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multi-objective optimization for layout planning of matrix manufacturing system

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2025年第3期12卷 1049-1067页

作者： Park, Jisoo Lee, Changha Oh, Seog-Chan Do Noh, Sang Sungkyunkwan Univ Dept Ind Engn 2066 Seobu Ro Suwon 16419 South Korea Gen Motors Res & Dev 30500 Mound Rd Warren MI 48090 USA

The automotive industry is experiencing rapid changes due to the rise of the Industry 4.0 manufacturing paradigm, which requires strategic implementation of advanced manufacturing systems to meet diverse customer needs. The Matrix Manufacturing System, characterized by modular facilities and autonomous mobile robots, offers greater flexibility compared to traditional dedicated production systems. This paper conducts a multi-objective optimization of facility layout planning within the matrix manufacturing system to enhance efficiency and responsiveness to market volatility. To solve the optimization problem, three heuristic algorithms-Simulated Annealing, Particle Swarm optimization, and Non-dominated Sorting Genetic Algorithm-II are employed and their performance is compared. For the comparative analysis, frequency maps are used, visualizing the optimization processes and outcomes between metaheuristic algorithms. The framework with methodologies presented in this report is expected to improve productivity and flexibility of a matrix manufacturing system in the automotive industry.

关键词： Matrix manufacturing system Layout planning multi-objective optimization Metaheuristic algorithm Discrete event simulation

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multi-objective optimization of Low-Velocity Impact and Compression Behavior of 3D-Printed PLA Cubic Samples

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POLYMERS 2025年第5期17卷 627-627页

作者： Dogan, Oguz Kamer, Muhammed S. Sahan, Mehmet F. Kahramanmaras Sutcu Imam Univ Fac Engn & Architecture Dept Mech Engn TR-46040 Kahramanmaras Turkiye Adiyaman Univ Fac Engn Dept Civil Engn TR-02040 Adiyaman Turkiye

This study investigates how various 3D printing parameters influence mechanical properties, specifically strength in compression and low-velocity impact (LVI) tests, and identifies the best printing parameters (layer thickness, nozzle diameter, and infill density) that lead to durable samples. Utilizing a Taguchi L9 orthogonal array, the study systematically examined the effects of three critical 3D printing parameters on the mechanical strength of cubic test samples. Nine experimental configurations were tested, each subjected to compression and LVI tests according to ASTM standards. Statistical analyses, including analysis of variance (ANOVA) and grey relational analysis (GRA), were employed to evaluate parameter significance and optimize results. Infill density significantly influenced the compression tests, while nozzle diameter was the most impactful parameter in LVI tests. Layer thickness had a minimal influence on both outcomes. Additionally, applying GRA revealed that optimal 3D printing parameters differ when considering the two mechanical properties simultaneously, highlighting the complexity of achieving balanced performance in 3D-printed structures. The application of the Taguchi method to optimize 3D printing parameters improved the mechanical properties of printed materials while significantly reducing the number of required experiments. By employing an efficient experimental design, this research demonstrates how to achieve high-quality results in compression and LVI tests with minimal resource use and time investment. Additionally, integrating GRA for the simultaneous optimization of multiple performance characteristics further enhances the practical applicability of the findings in additive manufacturing.

关键词： compression low-velocity impact 3D printing additive manufacturing multi-objective optimization

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multi-objective optimization of Building Ventilation Systems Using Model Predictive Control: Integrating Air Quality, Energy Cost, and Environmental Impact

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APPLIED SCIENCES-BASEL 2025年第1期15卷

作者： Andersen, Andreas Hyrup Jradi, Muhyiddine Univ Southern Denmark Maersk Mc Kinney Moller Inst Ctr Energy Informat DK-5230 Odense Denmark

This paper presents a flexible heating, ventilation and air conditioning (HVAC) modeling framework developed for building digital twin implementation. The framework is showcased for the modeling and simulation of four ventilation systems in a 8500 m2 university building. The developed model includes multiple objective model predictive control (MPC) with three objectives: electricity cost, indoor air quality and CO2 emission attributed to electricity consumption. A control strategy comparison is conducted between several MPC solutions with different objective weightings and a rule-based control strategy, which emulates the current system control. A novel approach for air quality evaluation is proposed and used for the MPC modeling and strategy comparison in this study. In this comparison, a "balanced" MPC strategy reduces energy costs by 18% compared to rule-based control while also providing significantly better air quality. An economic strategy achieves 24% savings with some air quality reduction, while an air-quality-focused strategy provides nearly "perfect" air quality with 11% savings. Finally, an environmental strategy shows the potential for prioritizing CO2 emissions over electricity costs. In this way, the strategy comparison illustrates the potential of MPC for the efficient operation and flexible objective prioritization according to stakeholder interests.

关键词： MPC HVAC ventilation building simulation system control multi-objective optimization component-based modelling

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multi-objective optimization of selective maintenance process considering profitability and personnel energy consumption

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Tian, Guangdong Wang, Miao Yang, Jianwei Mi, Hongjuan Ullah, Saif Aljuaid, Mohammed M. Fathollahi-Fard, Amir M. Beijing Univ Civil Engn & Architecture Sch Mech Elect & Vehicle Engn Beijing 100044 Peoples R China Yuanda Valve Grp Co Ltd Xingtai 055350 Hebei Peoples R China Univ Engn & Technol Taxila Dept Ind Engn Taxila Pakistan King Saud Univ Coll Business Adm Dept Hlth Adm Riyadh Saudi Arabia Univ Quebec Montreal Dept Analyt Operat & Technol Informat 315Sainte Catherine St East Montreal PQ H2X 3X2 Canada Macao Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau 999078 Peoples R China

Mechanical equipment naturally deteriorates and may malfunction during regular use, resulting in substantial financial losses and downtime. Regular maintenance can effectively address these issues. However, poor maintenance planning for products with numerous components often leads to inefficiencies for maintenance personnel, higher maintenance costs, and unnecessary resource consumption. Selective maintenance helps create effective maintenance programs under resource constraints, scientifically allocate maintenance resources, promptly repair faulty equipment, and sustain production activities. This study develops a multi-objective optimization model to enhance the efficiency of maintenance activities, avoid resource wastage, and increase maintenance revenue. This model optimizes the serial maintenance sequence by considering factors such as maintenance benefits, costs, personnel energy consumption, and resource constraints. Additionally, an improved metaheuristic algorithm, combining brainstorming optimization and large neighborhood search, is proposed to optimize the maintenance scheme for a specific type of carrier booster device system. Finally, an analysis of maintenance practices validates the applicability of the proposed model and algorithm, demonstrating their effectiveness in real-world scenarios.

关键词： Selective maintenance Serial sequence Maintenance planning multi-objective optimization Personnel energy consumption Preventive maintenance Corrective maintenance Resource assignment

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multi-objective optimization of tribological properties of diesel engine camshaft bearings

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STRUCTURAL AND multiDISCIPLINARY optimization 2025年第1期68卷 1-17页

作者： Zhao, Jingjing Li, Yuan Li, Yan Liu, Jinxiang Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Aerosp Prop Inst Beijing 100076 Peoples R China China North Ind Corp Beijing 100053 Peoples R China

In order to improve the tribological performance of camshaft bearings, a design method based on NSGA-II and TOPSIS decision methods was proposed. The structural-performance parameters sample dataset was obtained. The multi-objective optimization genetic algorithm and multi-criteria decision-making method were used to optimize the bearings structure with the goal of minimizing the total friction loss and the maximum wear height, as well as maximizing the average values of minimum oil film thickness. The optimal performance and structural parameters of camshaft bearings obtained through multi-objective optimization strategy have obvious directionality. The entropy weighted TOPSIS multi-criteria decision-making method effectively obtained the optimal solution. Compared with the original structure, the optimized structure significantly reduces the total friction loss and maximum wear height.

关键词： multi-objective optimization Deep neural network Camshaft bearing Friction loss Wear

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multi-objective optimization of variable altitude high-dimensional compression-ignition aviation piston engine based on Kriging model and NSGA-III

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ENERGY 2025年 320卷

作者： Xu, Yuchen Sun, Min Chen, Guisheng Xiao, Renxin Gong, Hang Yang, Jie Yang, Sen Kunming Univ Sci & Technol Yunnan Key Lab Internal Combust Engine Kunming 650500 Peoples R China Kunming Univ Sci & Technol Engn Training Ctr Kunming 650500 Peoples R China Kunming Univ Sci & Technol Coll Civil Aviat & Aeronaut Kunming 650500 Peoples R China

The operation of compression-ignition aviation piston engines in high-altitude environments is prone to critical issues such as power degradation and insufficient thrust. The research and optimization of the rapid coordination mechanism between fuel and air are crucial for preventing power loss during high-altitude operation and improving the service ceiling of the engine. Based on the constructed one-dimensional thermodynamic model of a compression-ignition aviation piston engine (CI APE), the Kriging surrogate model and non-dominated sorting genetic algorithm (NSGA) are utilized to optimize the brake specific fuel consumption (BSFC), maximum pressure rise rate (MPRR), and the maximum cylinder pressure (Pmax), exploring the optimal fuel-air combination at different altitudes. Firstly, the Pearson correlation coefficient analysis method is employed to confirm variables, and Latin hypercube sampling is used to generate training model samples. Secondly, a Kriging surrogate model of the engine with BSFC, MPRR, and Pmax as objective functions is constructed, and its accuracy is validated. Finally, the NSGA-III is employed for multi-objective optimization. The results indicate that injection timing, compression ratio, high-pressure stage blade opening, and low-pressure stage blade opening have the most significant impact on engine performance. The constructed surrogate models exhibit good predictive accuracy, with coefficient of determination (R2) values all greater than 0.9. At altitudes of 2000 m, 4000 m, 6000 m, and 8000 m, compared to before optimization, the BSFC decreased by 10.1 %, 11.5 %, 12.7 %, and 12 %, respectively. Compared to the power at 2000 m altitude before optimization, the optimized engine can achieve approximately 85 % of the power recovery target at 8000 m altitude.

关键词： Compression-ignition aircraft piston engine Kriging surrogate model NSGA-III algorithm multi-objective optimization

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multi-objective optimization framework for nitrogen-containing compounds generation in nitrogen-enriched pyrolysis: Integrating transfer learning and experimental validation

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JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS 2025年 189卷

作者： Wang, Hui Bi, Dongmei Qian, Qingqing Pan, Lei Liu, Shanjian Yi, Weiming Shandong Univ Technol Sch Agr Engn & Food Sci Zibo 255000 Peoples R China Southeast Univ Sch energy & Environm Nanjing 211102 Peoples R China Shandong Univ Technol Branch State Key Lab Woody Oil Resources Utilizat Zibo 255000 Peoples R China Shandong Res Ctr Engn & Technol Clean Energy Zibo 255000 Peoples R China

A multi-objective optimization approach, integrating machine learning and transfer learning, was proposed to optimize the generation of nitrogen-containing compounds in nitrogen-enriched pyrolysis of biomass. A highaccuracy Gradient Boosting Regression Tree (GBRT) model was developed using 827 experimental data sets, with transfer learning employed to accelerate training on specific target variables. This approach significantly enhanced both learning efficiency and predictive performance. The model achieved a Coefficient of Determination (R2) of 0.968 and a Mean Absolute Error (MAE) of 1.047 on the test set, demonstrating exceptional predictive capability. Through Principal Component Analysis (PCA) and model interpretability methods such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), key influencing factors were identified. The critical factors include nitrogen source ratio, pyrolysis temperature, and protective gas. The study identified a synergistic effect when the nitrogen source ratio was 50.00 % and the pyrolysis temperature was 550 degrees C. This condition led to the maximum generation of nitrogen-containing compounds. Additionally, increasing the nitrogen source ratio reduced the formation of volatile compounds, while higher lignin content promoted the formation of aldehydes and ketones. Experimental validation via nitrogenenriched pyrolysis of corn stover confirmed the practical applicability of the model. The model accurately predicted nitrogen-containing compounds generation, with the maximum prediction error constrained to within 6.20 %. This study combines data-driven methods with experimental validation. The approach provides a novel technological framework for optimizing complex chemical reactions and supporting the sustainable production of high-value nitrogen-based chemicals.

关键词： multi-objective optimization Machine learning Transfer learning Nitrogen-containing compounds Pyrolysis conditions

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multi-objective optimization of an Aeroengine Accessory Gearbox Transmission Based on a Heuristic Algorithm

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JOURNAL OF AEROSPACE ENGINEERING 2025年第2期38卷

作者： Hu, Mingzhu Zhu, Jiazan Gong, Liangliang Lu, Zehua Liu, Huaiju Chongqing Univ State Key Lab Mech Transmiss Adv Equipment Chongqing 400044 Peoples R China Aero Engine Corp China Sichuan Gas Turbine Res Est Mech Syst Dept Chengdu 610500 Peoples R China

The accessory gearbox transmission is an essential component of an aeroengine, and it affects an aircraft's safe operation. The high-reliability, lightweight design of accessory gearbox transmissions remains challenging owing to the complicated structure and loading conditions. In this study, a multi-objective optimization method based on the heuristic search nondominated sorting genetic algorithm II (HS-NSGA II) is proposed to obtain gratifying structural parameters. The proposed method optimized 30 structural parameters of the gearbox under 21 constraint conditions within 12 min. Compared with the initial design scheme, the optimized result reduced gearbox weight by 10.2% while improving its safety by 2.98%. Due to the introduction of the HS algorithm, the proposed method outperforms classic optimization methods in design results and calculation efficiencies, such as the multi-objective particle swarm optimization (MOPSO), ant colony optimization (ACO), and nondominated sorting genetic algorithm II (NSGA II). The proposed method has strong versatility and potentially can be extended to other gear transmission designs.

关键词： Aeroengine accessory gearbox multi-objective optimization Lightweight Safety Heuristic search nondominated sorting genetic algorithm II (HS-NSGA II)

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multi-objective optimization design of power pack structures based on floating projection topology optimization

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ENGINEERING optimization 2025年第2期57卷 361-383页

作者： Huang, Dengfeng Zhou, Shunshun Yan, Xiaolei Fujian Univ Technol Fujian Key Lab Automot Elect & Elect Drive Fuzhou Peoples R China

Battery packs are vital components for storing and releasing energy. However, during driving, the intensity of heat emission and mechanical performance of electric vehicles are interrelated but conflicting indicators. To address this problem, this study topologically optimizes the power pack support structure with multiple objectives of minimizing compliance and heat dissipation, aiming to achieve a comprehensive and integrated design scheme. A floating projection topology optimization (FPTO) method is proposed, which normalizes the objectives of structural compliance and heat dissipation and derives the sensitivity. FPTO demonstrates superior objective function values and smoother topology configuration compared to the solid isotropic material with penalization (SIMP) method. Finally, the proposed method is applied to the power pack support structure. The results demonstrate that the method obtains a design solution with excellent mechanical performance and high heat dissipation performance with a suitable trade-off. The lightweight design of the overall structure of the battery pack is achieved.

关键词： FPTO method thermal conduction topology optimization multi-objective optimization power battery pack

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multi-objective optimization of a novel photovoltaic-thermoelectric generator system based on hybrid enhanced algorithm

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ENERGY 2025年 319卷

作者： Zhao, Ru Zhu, Na Zhao, Xudong Luo, Zhenyu Chang, Jianpei Huazhong Univ Sci & Technol Sch Environm Sci & Engn Wuhan 430074 Hubei Peoples R China Univ Hull Ctr Sustainable Energy Technol Kingston Upon Hull HU6 7RX England

To address low efficiency of traditional photovoltaic-thermoelectric generator (PV-TEG) systems, a novel PVMCHP-PCM-TEG system was proposed. In this study, waste heat from back of PV cells is transferred to the hot side of TEG modules via a microchannel heat pipe (MHCP), while phase change material (PCM) is incorporated for thermal storage to extend operating time of TEG modules without solar radiation. The electrical efficiency and total life cycle cost were defined as multi-objective functions, and a comprehensive analysis, including sensitivity analysis and system optimization, was conducted to achieve global optimization over the entire life cycle. The mathematical model of PV-MCHP-PCM-TEG system was developed and experimentally validated in Wuhan, China. Sensitive factors, including PV reference efficiency, quantity of TEG modules, thickness of inner and outer PCM plates, and melting temperatures of PCM plates, were identified using Sobol global sensitivity analysis and subsequently optimized using the Non-dominated Sorting Genetic Algorithm II coupled with multi-objective Particle Swarm optimization (NSGA II-MOPSO) algorithm. After determining weights of the two objectives using entropy weighting method, Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) solution indicated that optimal electrical efficiency reached 25.6 %, while the minimum total life cycle cost was 335.4 CNY.

关键词： Photovoltaic Microchannel heat pipe Phase change material multi-objective optimization NSGA II-MOPSO algorithm

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