Archives of Computational Methods in Engineering最新文献_第10页

Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems 解决多目标工程优化问题的自然启发式框架趋势

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-25 DOI: 10.1007/s11831-024-10090-x

Clifford Choe Wei Chang, Tan Jian Ding, Chloe Choe Wei Ee, Wang Han, Johnny Koh Siaw Paw, Iftekhar Salam, Mohammad Arif Sobhan Bhuiyan, Goh Sim Kuan

{"title":"Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems","authors":"Clifford Choe Wei Chang, Tan Jian Ding, Chloe Choe Wei Ee, Wang Han, Johnny Koh Siaw Paw, Iftekhar Salam, Mohammad Arif Sobhan Bhuiyan, Goh Sim Kuan","doi":"10.1007/s11831-024-10090-x","DOIUrl":"10.1007/s11831-024-10090-x","url":null,"abstract":"<div><p>Nowadays, nature-inspired artificial intelligent metaheuristic optimization algorithms (MHOAs) have gained many attentions from researchers all over the world due to their capabilities in solving various decision-making problems. These algorithms are inspired and modelled based on the searching behaviour of animals in real life. This review paper provides in-depth discussions on various challenges and breakthroughs in numerous state-of-the-art nature-inspired artificial intelligence (AI) algorithms in solving multi-objective optimization engineering problems with emphasis on the mathematical modelling and algorithm developments. From conventional analysis such as speeds and accuracies to relatively advanced benchmarks such as complexities and convergence patterns, the comparison criteria of population-based and nature-inspired search mechanisms have evolved in the effort to further enhance the overall performance and reachability of these heuristic algorithms. This paper provides a platform for young readers and new researches who are about to indulge in the realm of various AI optimization techniques. Comprehensive analysis and discussions are presented on various state-of-the-art methods, with possible fields of applications proposed. Suitability of search mechanisms to specific optimization problem categories has also been investigated and presented, with combined or hybrid methods under scrutiny.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 6","pages":"3551 - 3584"},"PeriodicalIF":9.7,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140298975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Big Data—Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques 用于预测的大数据-供应链管理框架：数据预处理和机器学习技术

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-24 DOI: 10.1007/s11831-024-10092-9

Md Abrar Jahin, Md Sakib Hossain Shovon, Jungpil Shin, Istiyaque Ahmed Ridoy, M. F. Mridha

{"title":"Big Data—Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques","authors":"Md Abrar Jahin, Md Sakib Hossain Shovon, Jungpil Shin, Istiyaque Ahmed Ridoy, M. F. Mridha","doi":"10.1007/s11831-024-10092-9","DOIUrl":"10.1007/s11831-024-10092-9","url":null,"abstract":"<div><p>This article systematically identifies and comparatively analyzes state-of-the-art supply chain (SC) forecasting strategies and technologies within a specific timeframe, encompassing a comprehensive review of 152 papers spanning from 1969 to 2023. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 6","pages":"3619 - 3645"},"PeriodicalIF":9.7,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140200109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable Neural Networks: Achieving Interpretability in Neural Models 可解释的神经网络：实现神经模型的可解释性

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-21 DOI: 10.1007/s11831-024-10089-4

Manomita Chakraborty

{"title":"Explainable Neural Networks: Achieving Interpretability in Neural Models","authors":"Manomita Chakraborty","doi":"10.1007/s11831-024-10089-4","DOIUrl":"10.1007/s11831-024-10089-4","url":null,"abstract":"<div><p>Data mining is the most widely used method for discovering knowledge. There are numerous data mining tasks, with classification being the most frequently encountered task in various application domains such as fraud detection, disease diagnosis, text classification, and so on. Many classification techniques, such as Bayesian classifiers, decision trees, genetic algorithms, neural networks (NNs), and so on, are available to help researchers solve problems in a variety of domains. However, NNs are the most frequently used classification approach because they are effective at solving classification problems that cannot be divided into linear and non-linear categories, have high classification accuracy on large datasets, and require minimal processing effort. Despite having good classification performances, NNs have a pitfall associated with them which hinders their applicability in some real-world applications. NNs are black boxes in nature, which means they cannot make transparent decisions that humans can interpret. Because of this limitation, NNs are unsuitable for many applications that require transparency in decision-making as well as high accuracy, such as audit mining or medical diagnosis. The well-known solution to this inherent disadvantage of NNs is to extract explainable decision rules from them. The extracted rules provide a detailed understanding of how NNs work in a human-readable format. Rule extraction is a well-established technique with a plethora of literature on the subject. However, there are very few papers whose primary goal is to survey the existing literature. As a result, the goal of this work is to provide a detailed analysis of the existing literature and to create a framework for existing and new researchers to conduct research in this field. The paper examines the state-of art from the perspective of designing framework of the algorithms, evaluation criteria, and applications.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 6","pages":"3535 - 3550"},"PeriodicalIF":9.7,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140200015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Comparative Review of Various Advanced Finite Elements to Alleviate Shear, Membrane and Volumetric Locking 缓解剪切、膜和体积锁定的各种先进有限元的综合比较评述

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-20 DOI: 10.1007/s11831-023-10050-x

Dhiraj S. Bombarde, Lakshmi Narayan Silla, Sachin S. Gautam, Arup Nandy

{"title":"A Comprehensive Comparative Review of Various Advanced Finite Elements to Alleviate Shear, Membrane and Volumetric Locking","authors":"Dhiraj S. Bombarde, Lakshmi Narayan Silla, Sachin S. Gautam, Arup Nandy","doi":"10.1007/s11831-023-10050-x","DOIUrl":"10.1007/s11831-023-10050-x","url":null,"abstract":"<div><p>Finite element analysis (FEA) is an extensively exercised numerical procedure to address numerous problems in several engineering fields. However, the accuracy of conventional FEA solutions is significantly affected in specific circumstances where the problem demands near-incompressibility or incompressibility of domain or analysis of thin structural geometries. Over time, several advanced FE models are developed to improve the quality of solutions in stated situations. However, the extensive comparative aspects of these methods are spared limited attention. In the present paper, a comprehensive review and comparison of the selected FE models have been presented. The detailed implementation procedure, along with the relative efficacy of the methods, has been derived for selective reduced integration (SRI), enhanced assumed strain (EAS), assumed natural strain (ANS), and a specific class of hybrid stress elements alongside the conventional FE formulation. The quality of results is assessed by evaluating the relative error norms in displacement and stress on well-established benchmark numerical examples. Furthermore, the paper investigates the methods for several parameters that include the method’s best-suited environment, robustness, and efficiency. The findings in the paper provide an elaborate understanding of the optimal choice of the method in locking-dominated problems.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 4","pages":"1979 - 2013"},"PeriodicalIF":9.7,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140200157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bird’s Eye View Approach on the Usage of Deep Learning Methods in Lung Cancer Detection and Future Directions Using X-Ray and CT Images 利用 X 射线和 CT 图像鸟瞰深度学习方法在肺癌检测中的应用及未来发展方向

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-20 DOI: 10.1007/s11831-023-10056-5

P. K. Kalkeseetharaman, S. Thomas George

引用次数: 0

Theoretical Assessment for Weather Nowcasting Using Deep Learning Methods 利用深度学习方法对天气预报进行理论评估

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-19 DOI: 10.1007/s11831-024-10096-5

Abhay B. Upadhyay, Saurin R. Shah, Rajesh A. Thakkar

引用次数: 0

Decision-Making Model Construction of Emergency Material Allocation for Critical Incidents Based on BP Neural Network Algorithm: An Overview 基于 BP 神经网络算法的突发事件应急物资分配决策模型构建：概述

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-14 DOI: 10.1007/s11831-024-10086-7

Yan Yan

引用次数: 0

Design and Performance Measures of AVS/R Systems: A Bibliometric Literature Review AVS/R 系统的设计和性能测量：文献计量学文献综述

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-09 DOI: 10.1007/s11831-024-10084-9

Elif Burcu Kızılırmak, Sinan Öztaş, Nadide Çağlayan, Mahmut Tutam

{"title":"Design and Performance Measures of AVS/R Systems: A Bibliometric Literature Review","authors":"Elif Burcu Kızılırmak, Sinan Öztaş, Nadide Çağlayan, Mahmut Tutam","doi":"10.1007/s11831-024-10084-9","DOIUrl":"10.1007/s11831-024-10084-9","url":null,"abstract":"<div><p>This paper provides an overview of research focused on autonomous vehicle storage and retrieval system (AVS/RS) and aims to conduct a comprehensive analysis of current studies to identify potential future research directions. The analysis includes both bibliometric and systematic approaches. The former concentrates on quantitative aspects such as trends in studies, distributions, citations, and keywords of publications, while the latter evaluates papers by considering design factors and performance measures, categorizing them based on modeling approaches employed. This analysis can be instrumental in identifying shortcomings in prior studies and emphasizing the areas within the current literature where gaps exist. Our observation indicates that certain crutial design factors, even though widely employed in practice, have not been adequately addressed in the literature. It’s imperative to revisit and conduct more comprehensive exploration of performance measures, such as energy consumption and environmental impact.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 6","pages":"3455 - 3472"},"PeriodicalIF":9.7,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10084-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140100294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart City Public Transportation Route Planning Based on Multi-objective Optimization: A Review 基于多目标优化的智慧城市公共交通线路规划：综述

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-08 DOI: 10.1007/s11831-024-10076-9

Ming Xiao, Lihua Chen, Haoxiong Feng, Zhigao Peng, Qiong Long

{"title":"Smart City Public Transportation Route Planning Based on Multi-objective Optimization: A Review","authors":"Ming Xiao, Lihua Chen, Haoxiong Feng, Zhigao Peng, Qiong Long","doi":"10.1007/s11831-024-10076-9","DOIUrl":"10.1007/s11831-024-10076-9","url":null,"abstract":"<div><p>This paper investigates the implementation of a Multi-Objective Optimization technique for improving public transportation route planning in the setting of smart cities. Recognizing the difficulties of urban mobility, our technique incorporates a variety of criteria, including traffic patterns, cost-effectiveness, and environmental impact, to create an efficient route design system. The research applies complex algorithms to overcome the issues present in existing route planning procedures, using real-world data sources such as GPS data and traffic reports. We illustrate the efficacy of our strategy in boosting time efficiency, lowering costs, and decreasing environmental footprints via extensive case studies. The assessment measures used emphasise the suggested system’s advantages over current techniques. The debate digs into the larger implications for smart city development, recognising limits and providing possibilities for further study. This study adds vital insights and practical answers to the developing subject of smart city transportation, providing a solid basis for the continuing growth of urban mobility.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 6","pages":"3351 - 3375"},"PeriodicalIF":9.7,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fractional Spectral and Fractional Finite Element Methods: A Comprehensive Review and Future Prospects 分数谱和分数有限元方法：全面回顾与未来展望

IF 9.7 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2024-03-05 DOI: 10.1007/s11831-024-10083-w

Muhammad Bilal Hafeez, Marek Krawczuk

{"title":"Fractional Spectral and Fractional Finite Element Methods: A Comprehensive Review and Future Prospects","authors":"Muhammad Bilal Hafeez, Marek Krawczuk","doi":"10.1007/s11831-024-10083-w","DOIUrl":"10.1007/s11831-024-10083-w","url":null,"abstract":"<div><p>In this article, we will discuss the applications of the Spectral element method (SEM) and Finite element Method (FEM) for fractional calculusThe so-called fractional Spectral element method (f-SEM) and fractional Finite element method (f-FEM) are crucial in various branches of science and play a significant role. In this review, we discuss the advantages and adaptability of FEM and SEM, which provide the simulations of fractional derivatives and integrals and are, therefore, appropriate for a broad range of applications in engineering, biology, and physics. We emphasize that they can be used to simulate a wide range of real-world phenomena because they can handle fractional differential equations that are both linear and nonlinear. Although many researchers have already discussed applications of FEM in a variety of fractional differential equations (FDEs) and delivered very significant results, in this review article, we aspire to enclose fundamental to advanced articles in this field which will guide the researchers through recent achievements and advancements for the further studies.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 6","pages":"3443 - 3454"},"PeriodicalIF":9.7,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140036813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0