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Strengthened Dominance Relation NSGA-Ⅲ algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

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Computers, Materials & Continua 2024年第1期78卷 375-392页

作者： Liang Zeng Junyang Shi Yanyan Li Shanshan Wang Weigang Li School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan430081China

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing *** involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple *** non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling ***,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and *** enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence ***,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex *** validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test *** experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

关键词： Multi-objective job shop scheduling non-dominated sorting genetic algorithm differential evolution simulated binary crossover

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Application of multi-objective optimization genetic algorithm to design terahertz metamaterials with fano resonances

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PHYSICA SCRIPTA 2024年第6期99卷 065555-065555页

作者： Qin, Binyi Li, Yun Liao, Qitao Qiu, Jie Yang, Ruizhao Zheng, Jincun Zhong, Hesen Yulin Normal Univ Ctr Appl Math Guangxi Yulin 537000 Peoples R China Yulin Normal Univ Guangxi Coll & Univ Key Lab Complex Syst Optimizat & Big Data Proc Yulin 537000 Peoples R China Yulin Normal Univ Res Ctr Intelligent Informat & Commun Technol Sch Phys & Telecommun Engn Yulin 537000 Peoples R China Yulin Normal Univ Sch Chem & Food Sci Yulin 537000 Peoples R China Yulin Normal Univ Sch Phys & Telecommun Engn Yulin 537000 Peoples R China Yulin Normal Univ Sch Comp Sci & Engn Yulin 537000 Peoples R China Yulin Normal Univ Optoelect Informat Res Ctr Sch Phys & Telecommun Engn Yulin 537000 Peoples R China

In the prosperous development of terahertz (THz) metamaterials, Fano resonances have gained attention due to their potential applications in ultrasensitive systems. The performance of Fano resonance is directly influenced by the geometrical parameters of the element structure. However, the traditional design rules for Fano resonances in metamaterials rely on an empirical trial-and-error strategy, necessitating significant effort to achieve optimal results. To address this issue, we propose a design method in this study that utilizes the finite integration technique in time domain (FITD) along with a multi-objective optimization genetic algorithm for the intelligent design of metamaterial structures exhibiting the Fano resonance phenomenon. The FITD method is primarily used to calculate the Fano resonance with different metamaterial geometric structure parameters, while the genetic algorithm efficiently selects the optimal solution. Our method, characterized by high efficiency and complete independence from prior knowledge, could offer a new design technique for metamaterials with specific functions, thereby contributing to the development of THz applications.

关键词： Terahertz metamaterials design fano resonances multi-objective optimization non-dominated sorting genetic algorithm

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An Integrated Approach Based on MADM and MODM for Order Picking System Considering Human Factors

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第5期23卷 2017-2064页

作者： Nikkhoo, Fatemeh Kashan, Ali Husseinzadeh Ostadi, Bakhtiar Nikbakhsh, Ehsan Tarbiat Modares Univ Fac Ind & Syst Engn Tehran Iran

Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem's criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively.

关键词： Order picking system human factors multi-attribute decision-making multi-objective optimization problems non-dominated sorting genetic algorithm improved augmented epsilon constraint method

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Multi-objective optimisation of a 2D backward-sfacing step channel with porous baffles

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2024年第10期149卷 4755-4770页

作者： Costa, Sergio Cavaleiro Janeiro, Fernando M. Malico, Isabel Univ Evora IDMEC Escola Ciencias & Tecnol R Romao Ramalho 59 P-7000671 Evora Portugal Univ Evora Escola Ciencias & Tecnol R Romao Ramalho 59 P-7000671 Evora Portugal Inst Super Tecn Inst Telecomunicacoes Ave Rovisco Pais P-1049001 Lisbon Portugal

Porous baffles can be used to enhance heat transfer in various engineering applications, including electronic cooling, gas turbine blades, and chemical reactors. Also, the backward-facing step is a widely used configuration in fluid dynamics studies due to its simplicity and relevance to real-world geometries. This study examines heat transfer and flow characteristics in a backward-facing step channel featuring a heated bottom wall and two porous baffles. A computational fluid dynamics model, validated against prior research, is used to investigate flow and temperature fields. The innovation of this work lies in the application of multi-objective optimisation to search for a set of solutions that establish a trade-off between the average Nusselt number and the pressure drop. The optimisation specifically considers various parameters of the porous baffles, including height, width, distance from the step, and Darcy number, to identify optimal design configurations. Results show that porous baffles significantly improve heat transfer compared to a backward-facing step channel without them, despite an increase in pressure drop due to their presence. This work offers valuable insights into the trade-off between heat transfer performance and pressure drop, crucial for designing efficient heat transfer systems. By exploring the Pareto-Frontier, which represents various optimal design solutions, the study provides practical guidance when seeking to optimise heat transfer in backward-facing step channels with porous baffles. The findings contribute to advancing the understanding of heat transfer enhancement, highlighting the potential of porous baffles as a viable solution for improving thermal management in engineering systems.

关键词： Computational fluid dynamics Backward-facing step Multi-objective optimisation Porous media non-dominated sorting genetic algorithm

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Optimization of grid-connected solar PV systems with Hybrid Energy Storage System: A case study of the Sri Lankan power system

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JOURNAL OF ENERGY STORAGE 2025年 114卷

作者： Ganege, H. C. Chandima, D. P. Karunadasa, J. P. Wheeler, Patrick Univ Moratuwa Fac Engn Dept Elect Engn Moratuwa 10400 Sri Lanka Univ Ruhuna Fac Technol Dept Engn Technol Matara 81000 Sri Lanka Univ Nottingham Dept Elect & Elect Engn Univ Pk Nottingham NG7 2RD England

Greenhouse gas emissions from fossil fuel-based electricity generation significantly contribute to climate change. This research aims to mitigate these emissions by reducing reliance on fossil fuels and maximizing photovoltaic (PV) energy generation. It is supported by a grid-connected Hybrid Energy Storage System (HESS) integrating lithium-ion Battery Storage (BS) and Pumped Hydro Storage (PHS). An optimization algorithm is employed to minimize total expenditures, including capital, operational, replacement costs, as well costs associated with solar curtailment and supplemental thermal energy over the lifetime of the system. The non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem through the open-source Python framework Pymoo (multi-objective optimization in Python). Optimal capacities for solar PV and BS are determined for short-term intervals, while PHS capacities are evaluated based on regional geological feasibility. A novel energy management strategy ensures an effective balance between energy generation and storage. Optimization results are visualized through Pareto optimal charts supplemented by economic and environmental analyses to identify sustainable scenarios. Sensitivity analysis further refines the optimal capacities for solar PV, BS, and PHS. The proposed methodology is validated the Sri Lankan power system. A detailed roadmap is developed to guide the incremental additions of solar BS, and PHS capacities. Final results indicate minimal total cost variations, ranging from -14.05% to 56.69%, and an increase in solar PV generation between 2.19% and 5.13%, due to fluctuations in the interest rate the country.

关键词： non-dominated sorting genetic algorithm Multi-objective optimization in Python Hybrid Energy Storage Maximize solar Solar curtailment

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A parallel genetic algorithm for multi-criteria path routing on complex real-world road networks

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APPLIED SOFT COMPUTING 2025年 170卷

作者： Sharma, Harish Galvan, Edgar Mooney, Peter Natl Univ Ireland Maynooth Dept Comp Sci Nat Inspired Computat Res Grp Maynooth Ireland Natl Univ Ireland Maynooth Hamilton Inst Maynooth Ireland Natl Univ Ireland Maynooth Dept Comp Sci Nat Inspired Computat Res Grp Lero Maynooth Ireland

A Parallel genetic algorithm (PGA) specifically designed for multi-criteria vehicle routing is described in this work. The algorithm aims to enhance the existing routing methods by offering users the ability to choose their preferred path from a set of optimal paths optimised on multiple objectives. The objectives are optimised using a novel fitness metric that prioritises minimising path length while also maximising access to specific amenities such as pubs, hotels, and charging stations. The developed approach, called Parallel Optimal-route Search (POS), follows a hybrid model using both global parallelisation and island-based approaches. A Loop-Free Path-Composer (LFPC) is described and this genetic operator generates new paths for evaluation and is shown to yield a more diverse set of solutions in contrast to other commonly used approaches, such as Node Based Crossover and Path Mutation (NBCPM). Our approach is validated on highly complex, large-scale real-world road networks, with sizes ranging from 3,000 and 10,000 nodes. We present a systematic study comparing the performance of our proposed LFPC operator against the traditional NBCPM operators. Additionally, we evaluate the effectiveness of our proposed POS algorithm in comparison to the well-known non-dominated sorting genetic algorithm II and III.

关键词： Vehicle routing problem (VRP) Parallel genetic algorithm (PGA) Hybrid model Improved crowding distance (ICD) non-dominated sorting genetic algorithm (NSGA) II and III Pareto-optimal solutions

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A Multi-Objective Sensor Placement Method Considering Modal Identification Uncertainty and Damage Detection Sensitivity

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BUILDINGS 2025年第5期15卷 821-821页

作者： Pei, Xue-Yang Hou, Yuan Huang, Hai-Bin Zheng, Jun-Xing Yancheng Inst Technol Sch Civil Engn Yancheng 224051 Peoples R China Hebei Univ Technol Sch Civil & Transportat Engn Tianjin 300401 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

Structural Health Monitoring relies on accurate modal identification and effective damage detection to assess structural performance and safety. However, traditional sensor placement methods struggle to balance modal identification uncertainty, which arises from limited sensor coverage and measurement noise and damage detection sensitivity, which requires sensors to be optimally positioned to capture structural stiffness variations. To address this challenge, this study proposes a multi-objective sensor placement optimization method based on the non-dominated sorting genetic algorithm. The method introduces two key objective functions: minimizing modal identification uncertainty by leveraging Bayesian modal identification theory and information entropy and maximizing damage detection sensitivity by incorporating an entropy-based measure to quantify the uncertainty in stiffness variation estimation. By formulating the problem as Pareto-based multi-objective optimization, the method efficiently explores a trade-off between the two competing objectives and provides a diverse set of optimal sensor placement solutions. The proposed approach is validated through numerical experiments on a simply supported beam and a benchmark bridge structure, demonstrating that different optimization objectives lead to distinct sensor placement patterns. The results show that solutions prioritizing modal identification distribute sensors across the structure to improve global response estimation, while solutions favoring damage detection concentrate sensors in critical areas to enhance sensitivity. The proposed method significantly improves sensor placement strategies by offering a systematic and flexible framework for SHM applications, enabling engineers to tailor monitoring strategies based on specific structural assessment needs.

关键词： multi-objective sensor placement modal identification uncertainty damage detection sensitivity Pareto optimization non-dominated sorting genetic algorithm

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Research on Balanced Allocation of English Educational Resources Based on Multi-objective Optimisation and NSGA-II

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2025年第2期24卷

作者： Liu, Dongyang Henan Univ Anim Husb & Econ Sch Foreign Languages Zhengzhou 450046 Peoples R China

With the in-depth development of globalisation, English, as an international common language, is of great importance to the reasonable allocation of its educational resources for the improvement of national educational equity and overall educational quality. However, there has long been a serious imbalance in the allocation of English education resources in China. In terms of resource allocation between urban and rural areas and different schools, differences in the number of teachers, teaching facilities, teaching materials and other aspects lead to gaps in the opportunities and quality of English education for students. This imbalance in resource allocation not only affects educational equity, but also limits students' English learning outcomes. Therefore, this paper proposes an optimal allocation method of English educational resources based on multi-objective optimisation (MOO) and non-dominated sorting genetic algorithm (NSGA-II). This method aims to maximise the utilisation rate of educational resources, narrow the gap between urban and rural areas and schools, and promote educational equity and efficient use of resources by optimising multiple objectives at the same time, such as teacher allocation, balanced use of teaching facilities and fair distribution of teaching materials resources. The experimental results show that the proposed method has a high degree of fit on two different datasets, reaching 94.58% and 96.87%, respectively, and the resource balance is greatly improved in the process of resource allocation. In addition, the algorithm has high operating efficiency under large-scale data, and the training time can be stabilised at 57.84 s when the sample size reaches 24,200. The experiment also shows that the application of this method in districts and counties significantly reduces the fluctuation of educational resources and optimises the allocation level of educational resources in each district and county. The research provides a new way to solve

关键词： Balanced distribution education resources English multi-objective optimisation non-dominated sorting genetic algorithm

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A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design

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APPLIED SOFT COMPUTING 2024年 150卷

作者： Ala, Ali Goli, Alireza Mirjalili, Seyedali Simic, Vladimir Shanghai Jiao Tong Univ Sch Mech Engn Dept Ind Engn & Management Shanghai 200240 Peoples R China Univ Isfahan Fac Engn Dept Ind Engn & Future Studies Esfahan Iran Torrens Univ Ctr Artificial Intelligence Res & Optimizat Brisbane Australia Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11010 Serbia Yuan Ze Univ Coll Engn Dept Ind Engn & Management Taoyuan Taiwan Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

Healthcare supply chains play a crucial role, which enables the implementation of optimization strategies that have rapidly emerged as highly effective means for improving the overall structure of pharmaceutical and healthcare supply chains.. In the healthcare industry, parameters such as increasing the quality of service, as well as optimizing costs, environmental, and social factors play a unique role in supply chain management. To improve the healthcare supply chain network, this study proposed a novel optimization model to optimize multiple objectives, including minimizing the total costs and environmental impacts, while maximizing the social factors by creating jobs simultaneously. To address the effects of uncertain parameters, a fuzzy optimization method alongside the multi-objective gray wolf optimizer (MOGWO), non-dominated sorting genetic algorithm II (NSGA-II), multi-objective differential evolution algorithm (MODEA), and epsilon-constraint are applied to optimize the model. Also, a case study of the pharmaceutical industry demonstrates the model's efficacy in a real-life context. The numerical results show the MOGWO manages to create high-quality Pareto solutions with a good spread at the Pareto boundary within a short time compared to the epsilon-constraint approach. Further, it has shown a more robust performance compared to MODEA and NSGA-II, indicating the efficiency of MOGWO, among other solution methods and other objective indicators.

关键词： Healthcare Supply Chain Sustainability Multi-objective Gray Wolf Optimizer non-dominated sorting genetic algorithm Fuzzy Mathematical Programming Uncertainty Optimization algorithm

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Efficient economic energy scheduling in smart cities using distributed energy resources

SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION

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SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION 2024年 79卷

作者： Manzoor, Awais Akram, Waseem Judge, Malik Ali Khan, Naveed Khattak, Hasan Ali COMSATS Univ Islamabad Dept Comp Sci Islamabad 44500 Pakistan Univ Palermo Dept Engn I-90133 Palermo Italy Ulster Univ Fac Comp Engn & Built Environm Newtownabbey BT37 0QB North Ireland King Fahd Univ Petr & Minerals KFUPM Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia Sohar Univ Fac Comp & Informat Technol Sohar 311 Oman Natl Univ Sci & Technol NUST Islamabad Pakistan

Machine learning provides a powerful mechanism to enhance the capabilities of the next generation of smart cities. Whether healthcare monitoring, building automation, energy management, or traffic management, use cases of capability enhancement using machine learning have been significant in recent years. This paper proposes a modeling approach for scheduling energy consumption within smart homes based on a non-dominated sorting genetic algorithm (NSGA). Distributed energy management plays a significant role in reducing energy consumption and carbon emissions as compared to centralized energy generation. Multiple energy consumers can schedule energy-consuming household tasks using home energy management systems in coordination to reduce economic costs and greenhouse gas emissions. In this work, such a home energy management system is used to collect energy price data from the electricity company via an embedded device-enabled smart meter and schedule energy consumption tasks based on this data. We schedule daily power consumption tasks using a multiobjective optimization method that considers environmental and economic sustainability. Two conflicting objectives are minimizing daily energy costs and reducing carbon dioxide emissions. Based on electricity tariffs, CO2 intensity, and the window of time during which electricity is consumed, energy consumption tasks involving distributed energy resources (DERs) and electricity consumption are scheduled. The proposed model is implemented in a model smart building consisting of 30 homes under 3 pricing schemes. The energy demand is spread out across a 24-hour period for points A2-A4 under CPP-PDC, which produces a more flattened curve than point A1. There are competing goals between electricity costs and carbon footprints at points B2-B4 under the CPP-PDC, where electricity demand is set between 20:00 and 0:00. Power grids' peak energy demand is comparatively low when scheduling under CPP-PDC for points A5 and B5. Reducing

关键词： Micro grid Demand response Multiobjective optimization Home energy management Distributed energy resources non-dominated sorting genetic algorithm

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