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Green flexible job shop integrated scheduling optimization for machines and AGVs based on the Insga-ii algorithm

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2025年

作者： Li, Shiyun Han, Kai Pei, Zhi Yi, Wenchao Lv, Ruifeng Zhejiang Univ Technol Coll Mech Engn Room C509Mech Engn Build288 Liuhe Rd Hangzhou 310023 Zhejiang Peoples R China

Reducing carbon emissions is becoming increasingly important in manufacturing, highlighting the need for green scheduling in flexible systems. In real-world manufacturing, jobs often require transportation between machines, making transportation resources essential for effective scheduling and system optimization. This paper addresses the green flexible job shop scheduling problem for Automated Guided Vehicles (AGV) (EVFJSP-AGV), improving coordination between machine operations and AGV allocation to minimize the makespan, energy consumption, and AGV transportation distance. Detailed energy consumption models for both machines and AGVs are developed to promote energy savings. An AGV speed selection mechanism enhances production and transportation coordination while charging constraints ensure practical scheduling. To solve this multi-objective optimization problem, an improved nsga-ii algorithm (Insga-ii) is proposed, incorporating a four-segment coding method, a reverse population strategy, and a multi-neighborhood structure search strategy. Extensive numerical experiments evaluate the performance of the Insga-ii algorithm. Sensitivity analysis across various scales demonstrates the model and algorithm effectively address the green flexible job shop AGV scheduling problem.

关键词： Green workshop integrated scheduling automated guided vehicle (AGV) multi-objective optimization nsga-ii algorithm

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Seru scheduling problem with lot streaming and worker transfers: A multi-objective approach

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COMPUTERS & OPERATIONS RESEARCH 2025年 177卷

作者： Yilmaz, Beren Gursoy Yilmaz, Omer Faruk Akcali, Elif Cevikcan, Emre Istanbul Tech Univ Dept Ind Engn TR-34367 Istanbul Turkiye Univ Florida Dept Ind & Syst Engn Supply Chain & Logist Engn Lab Gainesville FL 32611 USA Karadeniz Tech Univ Dept Ind Engn TR-61080 Trabzon Turkiye Univ Florida Ctr Appl Optimizat Dept Ind & Syst Engn Gainesville FL USA

Seru production system (SPS) offers the flexibility of job shop production environments with the efficiency of traditional assembly lines. The SPSs are particularly attractive to industries characterized by high product variety and micro production volumes, and effective utilization of production and workforce resources is a critical challenge for SPSs. This paper addresses the seru scheduling problem with lot streaming and worker transfers for a SPS using a multi-objective approach. To this end, first, a multi-objective mixed-integer linear programming (MILP) model is developed for the minimization of makespan, average flow time, and maximum workload imbalance. Six different algorithms based on non-dominating sorting genetic algorithm ii (nsga-ii) are developed, each corresponding to an operational setting dictated by the lot streaming and worker transfers strategies in effect. A design of experiment (DoE) framework is utilized to generate realistic problem instances based on the several controllable factors and their levels. Analysis of comprehensive computational results demonstrates the effectiveness of the proposed algorithm (NS2) in finding high-quality and diversified solutions by simultaneous utilization of lot streaming with variable-sized sublots and worker transfers. The results indicate that the performance improvement achieved by the NS2 ranges between 10% and 20% compared to other algorithms. Furthermore, Analysis of Variance (ANOVA) confirms the significance of the number of workers and number of serus as critical parameters for the design or redesign of SPSs. Drawing on these findings, managerial insights are provided regarding the impact of lot streaming and worker transfers on SPS performance. This study offers practical and theoretical insights for decision-makers seeking to enhance SPS performance and bridge the gap between the conceptual analysis and practical implementation of SPSs.

关键词： Seru production system Lot streaming Worker transfers Shojinka Flexible staffing Multi-objective modeling nsga-ii algorithm

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Optimizing heat transfer enhancement in helical tube with twisted tape using nsga-ii for multi-objective design

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2025年 210卷

作者： Sun, Chen Wang, Wei Tian, Xi-Wei Suo, Wen-Juan Qian, Si-Hao Bao, Hong Xidian Univ Key Lab Elect Equipment Struct Design Minist Educ Xian 710071 Peoples R China Xidian Univ Shannxi Key Lab Space Solar Power Stn Syst Xian 710071 Peoples R China

This study develops a refined method combining geometric characterization and optimization for the helical tube with twisted tape to enhance heat transfer. Mathematical representations for important geometric parameters affecting the hydrothermal performance are constructed. Pareto fronts of the friction factor and Nusselt number, under constant wall heat rate (CWHR) and constant wall temperature (CWT) boundary conditions, are identified using the nsga-ii optimization algorithm in conjunction with computational fluid dynamics models. The optimized irregular design substantially enhances the heat transfer amount and suppresses flow losses. Numerical results show that the optimal compromise design improves the Nusselt number by 79.31 % and reduces the friction factor by 40.00 % compared to the plain helical tube under CWT conditions. Moreover, under CWHR conditions, the Nusselt number in the optimal compromise design achieves a maximum increase of 72.58%, with a slight rise in the friction factor, which stays below that of the uniform helical tube with twisted tape. Furthermore, even for the same geometric model, the hydrothermal parameters exhibit significant differences when applying different boundary conditions. The approach underscores the potential in high heat load environments and unlocks more possibilities for further efficient heat transfer.

关键词： Heat transfer enhancement Parametric modeling Helical tube Twisted tape nsga-ii algorithm

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Low-Carbon Multiobjective Optimization for Repetitive Projects Based on Crew-Based Scheduling Strategy

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JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT 2025年第1期151卷

作者： Yao, Zongyu Zhang, Lihui Hua, Zhilei Luo, Cheng North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China Hong Kong Univ Sci & Technol Dept Mech & Aerosp Engn Hong Kong Peoples R China

Repetitive projects represent a significant proportion of the construction industry. Effectively reducing carbon emissions from such projects plays an important role in mitigating climate change. This study employs the Line-of-Balance (LOB) method as a scheduling tool to explore the trade-offs among carbon emissions, duration, and costs in repetitive projects. Initially, this study addresses the limitations of traditional LOB scheduling strategies and proposes a new scheduling strategy from the perspective of crews. Moreover, the "controlled acceleration" routine is extended within the LOB framework. Subsequently, this study elucidates a calculation method for total carbon emissions during the construction phase of repetitive projects and establishes a trade-off model among carbon emissions, duration, and costs based on the proposed strategy. The nsga-ii algorithm was then devised to solve the Pareto set. Finally, the effectiveness of the proposed strategy was validated through a highway project. The research findings demonstrate that adopting the Crew-based Scheduling Strategy significantly reduces project carbon emissions and costs while shortening the duration when compared with traditional strategies. Furthermore, the controlled acceleration routine enhances resource utilization and achieves superior project performance. This study provides robust support for advancing the construction industry toward low-carbon and sustainable development.

关键词： Repetitive projects Carbon emissions Line-of-Balance (LOB) nsga-ii algorithm

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Optimal design of thermoelectric and mechanical performances of flexible thermoelectric generators under thermomechanical coupling

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2025年 60卷

作者： Fan, Shifa Rezaniakolaei, Alireza Taiyuan Univ Technol Coll Aeronaut & Astronaut Taiyuan 030024 Peoples R China Aalborg Univ AAU Energy Pontoppidanstr 111 DK-9220 Aalborg Denmark

Wearable flexible thermoelectric generators (FTEGs) offer promising potential as a low-maintenance power source for wearable technology. However, current designs frequently overlook the vital aspect of thermomechanical coupling, particularly concerning mechanical performance. This paper presents a multi-objective optimization framework utilizing the nsga-ii algorithm and TOPSIS technique to enhance both thermoelectric and mechanical performances of FTEGs. Firstly, the output power and voltage of the FTEGs are optimized by nsga-ii algorithm. Then the resulting Pareto solution and associated structural parameters are utilized to simulate the mechanical behavior of the FTEG under thermal and bending loads. The findings reveal that increasing the number of thermocouples can significantly reduce the stress level of the device, thus improving its mechanical reliability. Subsequently, FTEGs with excellent thermoelectric and mechanical performances are selected using the TOPSIS method with different weight coefficients. When maximizing thermoelectric output is given priority, the optimized FTEG achieves an output power of 0.879 W, representing an 89.5 % improvement over previous devices. Additionally, based on the weight coefficient (1/3, 1/3, 1/3), the optimized FTEG demonstrates a bending radius of 6 mm and a stress level of 74.5 MPa, which significantly lower than the yielding stress of Bi2Te3. These results are expected to significantly impact the optimal design and implementation of wearable FTEGs.

关键词： Optimal design Flexible thermoelectric generators Thermoelectric and mechanical performances Thermomechanical coupling nsga-ii algorithm

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A novel multi-objective optimization framework for the thermal management system of high-power density switching power supply

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APPLIED THERMAL ENGINEERING 2025年 264卷

作者： Ou, Zhenzhen Yin, Ying Gao, Feng Zhu, Lingjian Xian Univ Technol Coll Mech & Precis Instrument Engn Xian 710048 Peoples R China Beijing Inst Technol Shenzhen Automot Res Inst Shenzhen 518118 Guangdong Peoples R China

Significant heat generation can reduce device reliability and increase system failure in high-power density switching power supplies. However, conventional thermal designs are often constrained by model complexity and computational time. To effectively reduce the operating temperatures and cooling system volume, on the basis of improving the thermal resistance calculation method of the forced air cooling system, a novel multiobjective optimization framework based on the non-dominated sorting genetic algorithm ii (nsga-ii) and the entropy weight technique for order preference by similarity to ideal solution (TOPSIS) is presented in this study. The framework optimizes design parameters such as the length and width of the radiator, fin height, fin duct width, and number of fin ducts. Results show that the multi-objective optimization framework successfully balances low thermal resistance and volume constraints, achieving a 15.07% reduction in thermal resistance and a 4.67% reduction in system volume compared to conventional designs. Furthermore, the optimization results were validated by finite element simulations and experimental tests on a physical prototype, demonstrating the effectiveness of the proposed framework for thermal management in high-power density applications.

关键词： Multi-objective optimization Thermal management High-power density nsga-ii algorithm Entropy weight TOPSIS

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Tolerance allocation of mechanical assemblies including thermal deformation: A machine learning - based method

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JOURNAL OF THERMAL STRESSES 2025年第1期48卷 64-81页

作者： Soroush, Hossien Khodaygan, Saeed Nik, Javad Hemati Sharif Univ Technol Mech Engn Dept Tehran Iran

Tolerance allocation is one of the prominent tools to achieve the optimal manufacturing cost and the best performance for mechanical assemblies. Variations in dimensions and geometry shape due to different working temperatures for various components within an assembly can make initially assigned tolerances ineffective. Therefore, it is necessary to consider the component's deformation caused by thermal gradients during the tolerance design. In this paper, a new method is presented for optimal tolerance allocation of mechanical assemblies under thermal deformation. First, the system's temperature working conditions and tolerance design goals are specified, followed by the finite element simulation of the assembly at the desired working temperature. Then, the assembly equation is determined according to the complexity of the problem by using analytical methods and some machine learning (ML) algorithms such as XGBoost. The optimal tolerance design is formulated into a constrained multi-objective optimization problem. Then, to obtain the Pareto front of optimal tolerances, the multi-objective optimization is solved by the nsga-ii algorithm. Finally, to illustrate the capability of the proposed method, two case studies are considered, and obtained results from the presented method and conventional approaches are compared.

关键词： Finite element simulation nsga-ii algorithm thermal deformation thermal gradients tolerance allocation XGBoost algorithm

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Preliminary Multi-Objective Optimization of Mobile Drip Irrigation System Design and Deficit Irrigation Schedule: A Full Growth Cycle Simulation for Alfalfa Using HYDRUS-2D

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WATER 2025年第7期17卷 966-966页

作者： Zhang, Haohui Ma, Feng Wang, Wentao Ding, Feng Hui, Xin Yan, Haijun China Agr Univ Coll Water Resources & Civil Engn Beijing 100083 Peoples R China Xinjiang Acad Agr Sci Inst Agr Resources & Environm Urumqi 830091 Peoples R China Northwest A&F Univ Coll Water Resources & Architecture Engn Yangling 712100 Peoples R China State Key Lab Efficient Utilizat Agr Water Resourc Beijing 100083 Peoples R China

Mobile drip irrigation (MDI) systems integrate the technological advantages of center-pivot irrigation (CPI) systems and drip irrigation systems, boasting a high water-saving potential. To further enhance water use efficiency in alfalfa production in northern China, this preliminary study verified the accuracy of the HYDRUS-2D soil water movement numerical model through field experiments. Using the numerical model, four drip-line installation distances (60, 75, 90, and 105 cm), three deficit irrigation thresholds (45-50% FC, 55-60% FC, and 65-70% FC), and four irrigation depths (70% W, 85% W, 100% W, and 115% W) were set to simulate root water uptake, soil surface evaporation, total irrigation amount, and deep percolation during the entire growth cycle of alfalfa, respectively. Objective functions were constructed according to the simulation results, and the nsga-ii algorithm was used for multi-objective optimization of the deficit irrigation schedule. The preliminary results indicated that HYDRUS-2D can accurately simulate soil water movement under MDI systems, as the RMSE values of soil water content at all measured depths were less than 0.021 cm(3)/cm(3), with the NRMSE values being below 23.3%, and the MAE values below 0.014 cm(3)/cm(3). Increasing the deficit irrigation threshold from F1 to F3 enhanced root water uptake by 12.24-15.34% but simultaneously increased the total irrigation amount, soil surface evaporation (by up to 29.58%), and the risk of deep percolation;similar trends were observed with increasing irrigation depth. The drip-line installation distance had no significant impact on irrigation performance. The nsga-ii multi-objective optimization algorithm was used to obtain Pareto-optimal solutions that balance conflicting objectives. For this case study, a drip-line installation distance of 105 cm, a deficit irrigation threshold of 50-55% FC, and an irrigation depth of 112% W were recommended to achieve balance among the various optimization object

关键词： multi-objective optimization HYDRUS-2D nsga-ii algorithm mobile drip irrigation system water management

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Multi-objective optimization of a sustainable two echelon vehicle routing problem with simultaneous pickup and delivery in construction projects

JOURNAL OF ENGINEERING RESEARCH

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JOURNAL OF ENGINEERING RESEARCH 2025年第1期13卷 39-46页

作者： Sun, Jie Wang, Ruipu Shanghai Urban Construct Vocat Coll Sch Urban Operat & Management Shanghai 200438 Peoples R China

In many construction projects, the delivery of raw materials from the warehouse to the project place and the return of waste materials from the project place to the warehouse are planned simultaneously. This issue has many complexing assumptions. Accordingly, in this research, the multi-objective two-echelon vehicle routing with simultaneous pickup and delivery is investigated. For this purpose, a multi-objective mathematical model has been developed. Objective functions include economic, environmental and social criteria to achieve sustainability. Moreover, as the problem is known as a hard-NP problem, two meta-heuristic algorithms, nsga-ii and MOPSO, are proposed to solve this problem. In order to evaluate the performance of the proposed metaheuristic algorithms, 10 test problems of different sizes were generated. The solutions obtained with the proposed algorithms for each of these example problems were evaluated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method in eight indicators. The achieved numerical results show that the proposed model and the applied algorithms have sufficient efficiency. Moreover, it is revealed that the nsgaii and MOPSO algorithms have a desirability weight of 0.4321 and0.5679, respectively. Therefore, it can be concluded that the MOPSO algorithm has higher efficiency for solving the model.

关键词： Two-echelon vehicle routing problem Multi-objective optimization Simultaneous pickup and delivery nsga-ii algorithm MOPSO algorithm

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Optimization of emission strategies for airborne radioactive material from nuclear facilities under normal operating conditions

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NUCLEAR ENGINEERING AND TECHNOLOGY 2025年第6期57卷

作者： Chen, Renjie Guo, Qian Chen, Zhenping Lu, Zeyu Liu, Qiaoxu Zhao, Zhiyuan Zhang, Zhitao Univ South China Sch Resource Environm & Safety Engn Hengyang 421001 Hunan Peoples R China Minist Educ Key Lab Adv Nucl Energy Design & Safety Hengyang 421001 Hunan Peoples R China

To ensure public health and environmental safety while adhering to the ALARA (As Low As Reasonably Achievable) principle for radiation protection, this paper presents a method for optimizing meteorological strategies for airborne radioactive emissions using the nsga-ii algorithm. The proposed method considers the individual dose, regional collective dose, and population dose around the nuclear facility. By constructing a multi-objective optimization model and integrating it with the Gaussian diffusion model and radiation dose assessment model, this approach identifies the optimal set of emission meteorological strategies to minimize both individual effective dose and collective dose within the evaluation area. Using a pressurized water reactor (PWR) nuclear power plant and the surrounding population distribution data as a case study, simulations and solutions are provided. The results demonstrate that the optimized meteorological emission strategy can significantly reduce both individual and collective dose.

关键词： Radiation dose Meteorological parameters Emission strategies Multi-objective optimization nsga-ii algorithm

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