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Integrating user preferences and demand uncertainty in electric micro-mobility battery-swapping station planning: A data-driven three-stage model

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APPLIED ENERGY 2025年 389卷

作者： Zhang, Fan Lv, Huitao Kuai, Chenchen Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hung Hom Kowloon Hong Kong Peoples R China Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing Jiangsu Peoples R China Univ Calif Los Angeles Civil & Environm Engn Los Angeles CA USA

With the rapid adoption of electric micro-mobility vehicles (EMVs), the demand for efficient, user-centered battery-swapping infrastructure is rising. However, existing battery-swapping station (BSS) planning often falls short by neglecting critical elements such as user preferences and demand uncertainty. This study introduces a three-stage BSS planning framework that holistically addresses demand allocation, location-capacity optimization, and deployment adaptability under fluctuating demand. First, EMV users' preferences are integrated into a demand allocation model, capturing range anxiety and individual station selection criteria. This demand- sensitive allocation then informs a multi-objective bi-level planning model, balancing construction costs with user travel distances to BSS facilities. Finally, the model incorporates a demand uncertainty layer, supported by simulation scenarios, to create a robust facility deployment strategy that anticipates various demand fluctuations. An improved Non-dominated Sorting Genetic algorithm ii (nsga-ii) effectively optimizes this model, validated through a case study in Nanjing, producing 41 Pareto-efficient solutions. Sensitivity analysis highlights how factors like range anxiety and charging time impact BSS planning outcomes, underscoring the value of this data-driven approach. This work demonstrates that a comprehensive planning approach, integrating user behavior and uncertainty considerations, can significantly enhance the effectiveness and adaptability of EMV battery-swapping networks.

关键词： Electric micro-mobility vehicle Battery-swapping station Demand uncertainty User preferences nsga-ii algorithm

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Enhancing unmanned aerial vehicles logistics for dynamic delivery: a hybrid non-dominated sorting genetic algorithm ii with Bayesian belief networks

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ANNALS OF OPERATIONS RESEARCH 2025年 1-57页

作者： Mahmoodi, Armin Sajadi, Seyed Mojtaba Sadeq, Abdellatif M. Narenji, Masoud Eshaghi, Mehdi Jasemi, Milad Carleton Univ Dept Aerosp Engn Ottawa ON Canada Aston Univ Aston Business Sch Operat & Informat Management Dept Birmingham B4 7ET England Qatar Univ Mech & Ind Engn Dept Doha Qatar Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Univ Montevallo Stephens Coll Business Montevallo AL USA

To address the complexities of managing networks of unmanned aerial vehicles (UAVs) and Just-in-Time problem solving, this study introduces a cutting-edge multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, significantly enhancing the efficiency of UAV operations. It reduces battery consumption and transportation costs while optimizing delivery times and reducing operational risks. The model improves the refinement of delivery schedules by accounting for uncertain traffic scenarios, thereby increasing its accuracy and reliability in dynamic environments. Additionally, a Bayesian belief networks approach for risk assessment introduces a new layer to operational risk management. The model's performance and its trade-offs are demonstrated through advanced data visualizations such as 3D Pareto fronts, pair plots, and network graphs, with validation via the nsga-ii approach confirming its reliability and practical applicability. This research represents a major leap forward in drone routing strategies, focusing on efficiency, adaptability, and risk management in UAV logistics and provides a comprehensive framework that bridges the gap between theoretical exploration and practical application.

关键词： Routing problem Drone delivery nsga-ii algorithm Multi-objective optimization Bayesian belief networks Risk assessment

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Optimization of vehicle routing problems combining the demand urgency and road damage for multiple disasters

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JOURNAL OF SAFETY SCIENCE AND RESILIENCE 2025年第2期6卷 196-211页

作者： Li, Ran Ye, Xiaofei Pei, Shuyi Yan, Xingchen Wang, Tao Chen, Jun Zheng, Pengjun Ningbo Univ Fac Maritime & Transportat Ningbo 315211 Peoples R China Ningbo Univ Fac Maritime & Transportat Ningbo Port Trade Cooperat & Dev Collaborat Innova Ningbo 315211 Peoples R China Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing 210037 Peoples R China Guilin Univ Elect Technol Sch Architecture & Transportat Guilin 541004 Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China

In the context of the COVID-19 epidemic, a "double-hazard scenario" consisting of a natural disaster and a public health event simultaneously occurring is more likely to arise. However, compared with single-hazard, multiple disasters confront the challenges of complexity, diversity, and demand urgency. To improve the efficiency of emergency material distribution under multiple disasters, this study first divided multiple disasters into three categories: independent scenario, sequential scenario, and coupling scenario. A set of evaluation index systems for multiple disasters was established to quantify the urgency of demand. The routing optimization model of emergency vehicles for multiple disasters was proposed by combining demand urgency and road damage, and the non-dominated sorting genetic algorithm ii (nsga-ii) was used to simulate and validate the model. A coupling scenario considering two typical disasters of hurricanes and epidemics was selected as a validation example, and sensitivity analysis was also performed for different algorithms, scenarios, and constraints. The results demonstrated that the proposed model could effectively address the vehicle routing problem of emergency materials in the context of multiple disasters. Compared to the nsga, the nsga-ii was used to reduce the total delivery time, cost, and penalty cost by 15.98%, 13.60%, and 16.14%, respectively. Compared with the independent scenario, the coupling scenario increased the total delivery time and cost by 186.28% and 132.48% during the epidemic. However, it reduced the total delivery time by 4.00% and increased the delivery cost by 23.55% compared with the hurricane. Compared with the model without consideration, the model considering demand urgency and road damage reduced the total delivery time and cost by 17.88% and 8.73%, respectively. The model constructed in this study addressed the vehicle routing problem considering the demand urgency and road damage in the optimization process, p

关键词： Multiple disasters Emergency materials nsga-ii algorithm Vehicle routing problem (VRP) Urgency of demand Multi-objective

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Optimization Research of High-Speed Railway EMU Utilization Schedule Based on Three-Dimensional Space-Time Network

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ENGINEERING LETTERS 2025年第4期33卷 1008-1019页

作者： Qian, Mingjun Huang, Xin Li, Mingli Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China

With the continuous improvement of China High-Speed Railway network and the large number of EMUs being put into operation, it is essential to conduct in-depth research on the preparation and optimization of EMU operation schedules from the perspectives of energy consumption and carbon emissions during the operation and maintenance phases, in order to achieve the carbon peaking and carbon neutrality goals in the transportation sector. Firstly, addressing the space-time dynamic characteristics of EMU operation schedules, a three-dimensional space-time network model of EMU operation schedules is established by adding the maintenance status dimension to the traditional two-dimensional space-time network model. By utilizing the connection changes of different operational status arcs in the three-dimensional space-time network, important information such as the maintenance status, maintenance frequency, and carbon emissions can be clearly and effectively presented, thus fully and accurately describing the entire operation and maintenance process of the EMUs. Next, considering the maintenance requirements of the EMUs, connection time constraints, status arc occupation constraints, operation arc conflict resolution constraints, and other conditions, an optimization model for High-Speed Railway EMU operation schedules based on the three-dimensional space-time network is established. The objective is to minimize the total EMU operation time, reduce maintenance frequency, and achieve the balanced carbon emission level. A three-dimensional array coding nsga-ii genetic algorithm has been designed to solve the model. This algorithm employs a crossover strategy that retains cross information, maximizes the retention of non-cross information, and conditionally regenerates. Finally, the model and algorithm's effectiveness is verified using relevant data from the Beijing-Shanghai High-Speed Railway EMU operation schedules. The results indicate that the optimization model and algorith

关键词： High-Speed Railway EMU operation schedules Space-Time Network Carbon emissions nsga-ii algorithm

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Optimal ecological restoration strategy development based on value-cost trade-offs

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GLOBAL ECOLOGY AND CONSERVATION 2025年 58卷

作者： Liu, Yunxuan Zhang, Cui Xie, Miaomiao Xie, Jingyi China Univ Geosci Sch Land Sci & Technol Beijing 100083 Peoples R China Minist Nat Resources PR China Key Lab Land Consolidat Guanying Yuan West 37 Beijing 100035 Peoples R China

Ecological restoration can provide a holistic and systemic perspective for the implementation of ecosystem protection and restoration projects. However, making decisions on optimal ecological restoration strategies remains a challenge. This study supports strategy development by balancing ecological improvement and economic viability. In this study, we took Huanghua, a county under provincial jurisdiction in China, as an example. We identified areas prioritized for ecological restoration by employing the research paradigm of "sources-resistance surface-corridors-nodes". In addition, we searched for Pareto-optimal solutions using the nondominated sorting genetic algorithm ii (nsga-ii) to balance the trade-offs between different objectives. The results of this study revealed that 46 ecological barriers were identified as priority ecological restoration areas in Huanghua, mainly for cropland and construction land. Three optimal ecological restoration strategies with high value-cost ratio, high ecological value, and low implementation cost were proposed for typical ecological restoration priority areas for different decision-making needs. Area I has the highest value-cost ratio, whereas Area ii and Area iiI have the same value-cost ratio, and it is most appropriate to prioritize restoration for Area I. When the investment budget is lower than the implementation cost of ecological restoration, ecological restoration projects with a high value-cost ratio are often chosen. This study constructed a methodological framework for formulating optimal ecological restoration strategies, which effectively enhanced the science of the decision-making process. The results provide a highly valuable scientific basis for developing ecological restoration strategies in Huanghua.

关键词： Ecological restoration Priority areas Multi-objective optimization nsga-ii algorithm

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Designing multilayer graphene membranes with well seawater desalination performance using machine learning combined with multi-objective optimization

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2025年第3期13卷

作者： Xie, Qiang Qiao, Yu Guo, Huaxi Chen, Chen Liang, Lijun Zhang, Li Zhejiang Sci Tech Univ Sch Chem & Chem Engn Key Lab Surface & Interface Sci Polymer Mat Zhejia Hangzhou 310018 Peoples R China Nanjing Univ Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China Nanjing Univ Collaborat Innovat Ctr Adv Microstruct Dept Phys Nanjing 210093 Peoples R China Hangzhou Dianzi Univ Coll Life Informat Sci & Instrument Engn Hangzhou 310018 Peoples R China

Given the water scarcity problem, the development of seawater desalination technology with high efficiency and cost control is important. With outstanding separation efficiency, graphene-based membranes have already become a potential choice for this problem. In this work, we employed a combination of Machine Learning (ML) and the Non-dominated Sorting Genetic algorithm ii (nsga-ii) to assess the impact of various parameters on the desalination performance of multilayer graphene membrane systems. The machine learning result indicates that gap width is a critical factor influencing water flux, while layer spacing and offset significantly affect ions rejection rate. Molecular Dynamics (MD) simulations confirm that multilayer graphene membrane (dG = 1.35 nm, H = 0.85 nm, O = 1.96 nm, and Q = -0.09 e/atom) identified through the nsga-ii optimization process exhibit exceptional desalination performance, characterized by high water flux and increased ions rejection rate. Furthermore, the mechanistic analysis revealed that Na+ ions require to shed a greater number of water molecules when passing through the multilayer graphene membranes with smaller layer spacing, resulting in increased energy loss. The integration of ML and nsga-ii can facilitate the design of materials with excellent desalination performance.

关键词： Seawater desalination Machine learning Multilayer graphene membrane nsga-ii algorithm

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Multi-objective optimization of FCC separation system based on nsga-ii

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CHEMICAL ENGINEERING SCIENCE 2025年 302卷

作者： Liu, Yingjie Chu, Menghao Ye, Qing Li, Jinlong Han, Deqiu Changzhou Univ Sch Petrochem Engn Changzhou 213164 Jiangsu Peoples R China Changzhou Univ Inst Urban & Rural Min Changzhou 213164 Jiangsu Peoples R China Jiangsu Key Lab Adv Catalyt Mat & Technol Changzhou Peoples R China Yellow River Delta Chambroad Inst Co Binzhou 256500 Peoples R China

Based on the industrial production data, the simulation of the separation process of a 2 million tons/year FCC unit including the fractionation and the absorption-stabilization systems were conducted on Aspen Plus software. To optimize the operation process, multi-objective optimization was performed integrating the Aspen Plus platform and MATLAB environment utilizing the improved non-dominated sorting genetic algorithm (nsga-ii). The established multi-objective optimization model was used for the operating variable screening, the genetic algebra selection and the optimal solution determination. During the optimization, the total yield of LPG and stable gasoline was aimed as the first optimization objective while the energy consumption of the whole system as the second objective. Five expressions were applied as the constraint functions and nine operating variables were conducted as the decision variables. The results showed that the performances of FCC separation system have been greatly improved after optimization. Some optimization strategies are advised for the whole system. The total yield of LPG and stabilized gasoline increases by 2. 44 %, and the energy consumption of the separation system decreases by 41. 79 %. In addition, the CO2 emission is reduced by 23.59 t/h and the total annual cost (TAC) is reduced by $0.89 million/year. It has been revealed that the multi-objective optimization method based on nsga-ii algorithm is useful for the guidance of the optimization of industrial petrochemical process.

关键词： FCC separation system Multi-objective optimization nsga-ii algorithm The energy consumption

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A Machine learning model to predict fracture of solder joints considering geometrical and environmental factors

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THEORETICAL AND APPLIED FRACTURE MECHANICS 2025年 136卷

作者： Soroush, Hossein Nourani, Amir Farrahi, Gholamhossein Sharif Univ Technol Dept Mech Engn Tehran Iran

Predicting the fracture load and energy in solder joints is crucial for enhancing their reliability and preventing failure in electronic systems. This study introduced a novel framework by combining experimental data, machine learning (ML) models, and multi-objective optimization to predict and optimize the fracture behavior of solder joints. For this purpose, double cantilever beam (DCB) samples were fabricated and tested under displacementcontrol conditions and mode I crack propagation loading with a strain rate of 0.03 s-1, incorporating environmental factors (i.e., storage temperature and humidity) and geometrical constraints (i.e., adherend thickness, adherend width, and solder thickness). The one-way analysis of variance (ANOVA) test results revealed that while all studied factors affected the fracture load of the joints, only storage temperature, humidity, and adherend thickness meaningfully influenced the samples' fracture energy. Next, using various machine learning (ML) techniques such as artificial neural network (ANN) and random forest (RF), the solder joint's fracture load and energy were forecasted with 86 % and 80.5 % accuracy, respectively. According to the results, the ANN and RF models predicted the joint fracture load and energy more accurately than other ML algorithms. Finally, a multi-objective optimization was employed to achieve fracture load and energy optimal values in DCB specimens by implementing the nsga-ii algorithm. This integrated approach can minimize the need for experimental testing and enable accurate prediction of solder joint fracture behavior by employing various ML models and optimization algorithms considering different working conditions.

关键词： DCB sample Fracture energy Artificial neural network (ANN) Random forest (RF) model nsga-ii algorithm

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Experimental and numerical optimization study on CNTs/Al composites drilling based on response surface methodology

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2025年

作者： Zhao, Bintong Gao, Qi Liu, Jiayi Jia, Dongzhou Liaoning Univ Technol Sch Mech Engn & Automat Jinzhou 121001 Peoples R China

Carbon nanotube reinforced aluminum (CNTs/Al) composites have been widely used in the aviation field due to their excellent properties, but there are still problems in their drilling, such as unclear damage mechanisms and poor hole quality. This study investigates the removal mechanism of CNTs/Al composites during drilling by finite element simulation and single factor experiments, and the impact of process parameters on hole wall morphology and the quality of the entrance edge is revealed. Optimal process parameter combinations are determined based on the response surface methodology (RSM) and multiobjective optimization via the nsga-ii genetic algorithm, with method verification. The results show that during the drilling process of CNTs/Al composites, CNTs primarily experience fragmentation, rebound, and pull-out, while the Al matrix exhibits partial tearing and interfacial separation. The coating of the Al matrix and CNT agglomeration contribute to defects like burrs and microcracks. Entrance damage is notably more severe than exit damage, characterized by higher tearing factor values, longer side burrs, and increased trajectory scratch areas. The main process parameter that affects surface roughness and entrance tear is feed rate. The results of multiobjective optimization are n = 3830 r/min, f = 0.0674 m/min, h = 2.0558 mm, with Ra = 6.327 mu,m and LD1 = 1.01732. Experimental validation shows Ra = 6.331 mu m and LD1 = 1.01743, with errors of 0.063% and 0.011%, respectively.

关键词： Drilling edge defects surface roughness response surface methodology nsga-ii algorithm

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Study on Improving Multi-objective Flexible Job Shop Scheduling Based on Memetic algorithm in The nsga-ii Framework 2

Study on Improving Multi-objective Flexible Job Shop Schedul...

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2nd International Conference on Cloud Computing and Internet of Things (CCIOT)

作者： Xu, Liang Xia, Zhao Yi Ming, Huang Dalian Jiao Tong Univ Sch software Dalian Peoples R China

ISBN: (纸本)9781467398237

This paper was the research about multi-objective flexible job shop scheduling based on Memetic algorithm under the improved Non-dominated sorting genetic algorithm ii (nsga-ii). On the basis of nsga-ii, a strategy of improving elite which was based on circular crowding distance was designed to increase the diversity of population distribution, prevent algorithm trapping in locally optimal solution, and avoid the disadvantage of premature genetic algorithm. And the research of multi-objective flexible job shop scheduling was established to optimize indexes, which was based on maximum completion time and different equipment load index.

关键词： job-shop scheduling nsga-ii algorithm Memetic algorithm multi-objective flexible job shop

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