Article
Computer Science, Artificial Intelligence
Haiping Ma, Yajing Zhang, Shengyi Sun, Ting Liu, Yu Shan
Summary: This paper provides a comprehensive survey of research on the non-dominated sorting genetic algorithm (NSGA-II) and its potential future research. It introduces the concept of multi-objective optimization and the foundation of NSGA-II, reviews its family and their modifications, and classifies their applications in engineering. The paper also presents interesting open research directions for NSGA-II in multi-objective optimization.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Mechanics
Quoc Hoa Pham, Trung Thanh Tran, Ashraf M. Zenkour, T. Nguyen-Thoi
Summary: This work studies the free vibration analysis and multi-objective optimization for L-shaped bi-functionally graded sandwich (L-BFGSW) plates. By using an effective finite element formulation and non-dominated sorting genetic algorithm, the optimal solution for the trade-off relationship between the maximum frequency and the minimum structural weight can be obtained.
COMPOSITE STRUCTURES
(2023)
Article
Thermodynamics
Aminu Yusuf, Nevra Bayhan, Hasan Tiryaki, Bejan Hamawandi, Muhammet S. Toprak, Sedat Ballikaya
Summary: This study examines the output performances of different equations in a hybrid system combining thermoelectric generators with concentrated photovoltaic cells, utilizing nanostructured thermoelectric materials. By optimizing parameters and selecting appropriate models, it was found that the system performs best when the load resistance is less than the internal resistance of the TEG.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Energy & Fuels
Zheng Jing, Chunhua Zhang, Panpan Cai, Yangyang Li, Zhaoyang Chen, Songfeng Li, An Lu
Summary: The study found that BMEP and CCR have a trade-off impact on BTE and BS emissions, with BMEP increase leading to higher BTE but also increased BSNOx, while higher CCR resulted in low BSNOx but deteriorated BTE, BSCO, and BSHC.
Article
Construction & Building Technology
Ying Liu, Ke You, Yutian Jiang, Zhangang Wu, Zhenyuan Liu, Gang Peng, Cheng Zhou
Summary: This paper presents the use of the non-dominated sorting genetic algorithm (NSGA-III) to solve the flexible earthwork scheduling problem (FESP). The results outperform other algorithms and are ranked using the analytic hierarchy process (AHP) to determine the optimal scheduling scheme.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Computer Science, Artificial Intelligence
Qiang Luo, Qing Fan, Qianwang Deng, Xin Guo, Guiliang Gong, Xiahui Liu
Summary: Previous research has overlooked the arrangement of on-hand inventory saved in the warehouse in the integrated scheduling problems. In this study, we propose a new production-inventory-distribution integrated scheduling problem that considers the production plan, allocation plan of on-hand inventory, and distribution decision simultaneously. We develop a mixed integer linear programming model and a modified NSGA-II algorithm to solve the problem. The proposed integration mode is shown to be effective compared with sequential scheduling methods commonly used in actual production.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Yanlu Gong, Junhai Zhou, Quanwang Wu, MengChu Zhou, Junhao Wen
Summary: This study proposes a length-adaptive non-dominated sorting genetic algorithm (LA-NSGA) for bi-objective high-dimensional feature selection. The algorithm uses an initialization method based on correlation and redundancy and a Pareto dominance-based length change operator to optimize search for individuals of various lengths. A dominance-based local search method is also employed for further improvement. Experimental results show that the Pareto front of feature subsets produced by LA-NSGA outperforms existing algorithms.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Construction & Building Technology
Elnaz Tafrihi Bailey, Luisa Caldas
Summary: This study proposes a new application of the NSGA-II algorithm and the Pymoo framework to create valuable massing solutions through a generative design process in the field of Operative Design. Nine experiments are conducted to test the algorithm's geometry optimization capabilities based on common architectural design goals, including Floor Area Ratio, Non-Passive Zone, Roofs and Best Oriented Surfaces, and Usable Open Space. Selected cases are visualized to demonstrate the trade-offs between different objectives and the generation of successful building designs. In the future, this generative design workflow can be implemented independently within immersive environments.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Green & Sustainable Science & Technology
Chen Zhang, Tao Yang
Summary: The frequent failures and high maintenance costs of wind turbines in wind farms have a significant impact on the stable development of wind power. This study establishes an optimal model for maintenance planning and resource allocation in wind farms under various constraints, aiming to address the dynamic maintenance needs efficiently.
Article
Materials Science, Paper & Wood
Baogang Wang, Chunmei Yang, Yucheng Ding
Summary: This study proposed an improved multi-objective algorithm for solid wood panel layout optimization, addressing the issues of weak convergence ability, single-objective optimization, and poor optimization effect of traditional genetic algorithms. By increasing the search capability, improving the optimization effect, and achieving simultaneous optimization of multiple objectives, the improved algorithm showed better optimization and stability compared to the traditional algorithm.
Article
Computer Science, Interdisciplinary Applications
Behzad Maleki Vishkaei, Mahdi Fathi, Marzieh Khakifirooz, Pietro De Giovanni
Summary: This paper introduces a bi-objective optimization solution for the Public Bicycle Sharing System (PBSS), aiming to minimize the mean number of rejected requests and free docks per each satisfied demand, while also reducing the total number of bicycles and docks in the system to minimize users' dissatisfaction with the least possible fleet size considering capacity constraints. The model is discussed under Jackson Network and the Mean Value Analysis (MVA), with the Non-dominated Sorting Genetic Algorithm (NSGA-II) being used to examine the efficiency of the proposed model through different numerical examples.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Shengchen Li, Zixin Deng, Jian Liu, Defu Liu
Summary: In this paper, a multi-objective optimization approach based on computational fluid dynamics and non-dominated sequencing genetic algorithm was applied to optimize the performance of a plate-fin heat exchanger. The optimal parameter combination was determined using support vector machine regression and technique for order preference by similarity to an ideal solution method. The results showed that the plate-fin heat exchanger achieved its best heat transfer performance under specific parameter settings.
APPLIED SCIENCES-BASEL
(2022)
Article
Environmental Sciences
Masoumeh Zare, Mohammad Reza Nikoo, Banafsheh Nematollahi, Amir H. Gandomi, Raziyeh Farmani
Summary: Groundwater vulnerability mapping is crucial in environmental management due to increasing contamination caused by population growth. This study developed an innovative risk-based multi-objective optimization model using three different models. The results showed that the optimized SI and DRASTICA models improved the correlation for Nitrate and Sulfate contamination, with the highest correlation values of 0.6 and 0.7.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Computer Science, Information Systems
Musheer Ahmad, Reem Alkanhel, Walid El-Shafai, Abeer D. Algarni, Fathi E. Abd El-Samie, Naglaa F. Soliman
Summary: This paper presents a multi-objective optimization-based method for constructing S-boxes that satisfy multiple performance criteria. The method utilizes the chaos-assisted nondominated sorting genetic algorithm-II to generate S-box solutions with good Pareto-optimal security features. The obtained Pareto-optimal S-box is further utilized for a medical image encryption algorithm in secure telemedicine services.
Article
Computer Science, Interdisciplinary Applications
Camilo Andres Rodriguez-Espinosa, Eliana Maria Gonzalez-Neira, Gabriel Mauricio Zambrano-Rey
Summary: This paper addresses a bi-objective problem in flexible job shop scheduling (FJSS) with stochastic processing times. The first objective is to minimize deterministic Earliness+Tardiness, and the second objective is to minimize the Earliness+Tardiness Risk. The proposed approach is a simheuristic that hybridizes the non-dominated sorting genetic algorithm (NSGA-II) with Monte Carlo simulation to obtain the Pareto frontier of both objectives. The computational results demonstrate the effectiveness of the proposed algorithm under different variability environments.
JOURNAL OF SIMULATION
(2023)
Article
Management
An-Da Li, Zhen He, Qing Wang, Yang Zhang
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2019)
Article
Computer Science, Information Systems
An-Da Li, Bing Xue, Mengjie Zhang
INFORMATION SCIENCES
(2020)
Article
Engineering, Multidisciplinary
Yang Zhang, Yanfen Shang, An-Da Li
Summary: The study focuses on improving the performance of weighted CUSUM charts in monitoring Poisson count data by developing self-information weight functions, which show superior performance in detecting small shifts compared to traditional methods. Simulation studies demonstrate that the weighted CUSUM charts with self-information weight outperform benchmark methods.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2021)
Article
Computer Science, Artificial Intelligence
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes an improved sticky binary PSO algorithm for feature selection problems, which aims to enhance evolutionary performance through new mechanisms such as an initialization strategy, dynamic bits masking, and genetic operations. Experimental results show that ISBPSO achieves higher accuracy with fewer features and reduces computation time compared to benchmark PSO-based FS methods.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Zhen He, Hao Hu, Min Zhang, Yang Zhang, An-Da Li
Summary: The paper proposes a data-driven method to effectively identify key quality characteristics in production processes, utilizing a multi-objective feature selection approach of maximizing geometric mean and minimizing the number of selected features. Experimental results show that the method exhibits good search performance on four production datasets.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Xiaojie Liu, An-Da Li
Summary: Product Portfolio Planning (PPP) is crucial for companies to gain a competitive edge. This paper proposes a Probability-based Discrete Particle Swarm Optimization (PDPSO) algorithm to solve the PPP problem. Experimental results show that PDPSO outperforms Genetic Algorithm (GA) and Simulated Annealing (SA) in optimizing and obtaining desirable solutions for various PPP problem cases. A case study of notebook computer portfolio planning is also presented to demonstrate the efficiency and effectiveness of PDPSO.
Article
Computer Science, Information Systems
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes a feature selection method to identify key quality features in complex manufacturing processes. A multi-objective binary particle swarm optimization algorithm is proposed, which includes three new components to optimize a bi-objective feature selection model. Experimental results show that this method can identify a small number of key quality features with good predictive ability.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
An-Da Li, Zhen He
COMPUTERS & INDUSTRIAL ENGINEERING
(2020)
Proceedings Paper
Engineering, Industrial
Xiaojie Liu, Yi Xia, Mo Chen, An-Da Li
PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND SYSTEMS MANAGEMENT (IESM 2019)
(2019)
Article
Computer Science, Interdisciplinary Applications
Francesco Pistolesi, Michele Baldassini, Beatrice Lazzerini
Summary: More than one in four workers worldwide suffer from back pain, resulting in the loss of 264 million work days annually. In the U.S., it costs $50 billion in healthcare expenses each year, rising up to $100 billion when accounting for decreased productivity and lost wages. The impending Industry 5.0 revolution emphasizes worker well-being and their rights, such as privacy, autonomy, and human dignity. This paper proposes a privacy-preserving artificial intelligence system that monitors the posture of assembly line workers. The system accurately assesses upper-body and lower-body postures while respecting privacy, enabling the detection of harmful posture habits and reducing the likelihood of musculoskeletal disorders.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Xavier Boucher, Camilo Murillo Coba, Damien Lamy
Summary: This paper explores the new business strategies of digital servitization and smart PSS delivery, and develops conceptual prototypes of smart PSS value offers for early stages of the design process. It presents the development and experimentation of a modelling language and toolkit, and applies it to the design of a smart PSS in the field of heating appliances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Dieudonne Tchuente, Jerry Lonlac, Bernard Kamsu-Foguem
Summary: Artificial Intelligence (AI) is becoming increasingly important in various sectors of society. However, the black box nature of most AI techniques such as Machine Learning (ML) hinders their practical application. This has led to the emergence of Explainable artificial intelligence (XAI), which aims to provide AI-based decision-making processes and outcomes that are easily understood, interpreted, and justified by humans. While there has been a significant amount of research on XAI, there is currently a lack of studies on its practical applications. To address this research gap, this article proposes a comprehensive review of the business applications of XAI and a six-step framework to improve its implementation and adoption by practitioners.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Francois-Alexandre Tremblay, Audrey Durand, Michael Morin, Philippe Marier, Jonathan Gaudreault
Summary: Continuous high-frequency wood drying, integrated with a traditional wood finishing line, improves the value of lumber by correcting moisture content piece by piece. Using reinforcement learning for continuous drying operation policies outperforms current industry methods and remains robust to sudden disturbances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Luyao Xia, Jianfeng Lu, Yuqian Lu, Wentao Gao, Yuhang Fan, Yuhao Xu, Hao Zhang
Summary: Efficient assembly sequence planning is crucial for enhancing production efficiency, ensuring product quality, and meeting market demands. This study proposes a dynamic graph learning algorithm called assembly-oriented graph attention sequence (A-GASeq), which optimizes the assembly graph structure to guide the search for optimal assembly sequences. The algorithm demonstrates superiority and broad utility in real-world scenarios.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Mutahar Safdar, Padma Polash Paul, Guy Lamouche, Gentry Wood, Max Zimmermann, Florian Hannesen, Christophe Bescond, Priti Wanjara, Yaoyao Fiona Zhao
Summary: Metal-based additive manufacturing can achieve fully dense metallic components, and the application of machine learning in this field has been growing rapidly. However, there is a lack of framework to manage these machine learning models and guidance on the fundamental requirements for a cross-disciplinary platform to support process-based machine learning models in industrial metal AM.
COMPUTERS IN INDUSTRY
(2024)
