Decision Analytics Journal最新文献_第8页

An analysis of the equilibrium strategies for route-choosing customers in a two-station queueing system 双站排队系统中顾客选路均衡策略分析

Decision Analytics Journal Pub Date : 2024-07-06 DOI: 10.1016/j.dajour.2024.100500

Geofferey Jiyun Kim , Jerim Kim

引用次数: 0

A simulation-based optimization model for balancing economic profitability and working capital efficiency using system dynamics and genetic algorithms 基于仿真的优化模型，利用系统动力学和遗传算法平衡经济盈利能力和营运资本效率

Decision Analytics Journal Pub Date : 2024-07-05 DOI: 10.1016/j.dajour.2024.100498

Ehsan Badakhshan, Ramin Bahadori

{"title":"A simulation-based optimization model for balancing economic profitability and working capital efficiency using system dynamics and genetic algorithms","authors":"Ehsan Badakhshan, Ramin Bahadori","doi":"10.1016/j.dajour.2024.100498","DOIUrl":"10.1016/j.dajour.2024.100498","url":null,"abstract":"<div><p>Economic uncertainty has been increasing, as evidenced by recent fluctuations in global markets and unpredictable economic indicators such as volatile demand, stock market fluctuations, and unpredictable interest rates. Economic profitability and working capital efficiency are pivotal indicators of a business’s financial health, both of which are adversely impacted by economic uncertainty. However, these metrics may diverge as distinct objectives drive them. There exists a gap in the literature regarding effective strategies for managing the trade-off between these metrics under economic uncertainty. This study addresses this gap by introducing a simulation-based optimization model that integrates system dynamics simulation and genetic algorithms. The proposed model aims to balance economic profitability and working capital efficiency within inventory management under partial trade credit. A recent real case study demonstrates the model’s applicability and reveals its superiority over conventional system dynamics simulation modeling. With its capacity to inform strategic and tactical decision-making, this model emerges as a valuable tool for supply chain and financial managers seeking to ensure financial stability amidst economic volatility.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100498"},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001024/pdfft?md5=62f59f94ce962ac5970764df10c38a7c&pid=1-s2.0-S2772662224001024-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An explainable artificial intelligence model for multiple lung diseases classification from chest X-ray images using fine-tuned transfer learning 利用微调迁移学习从胸部 X 光图像中对多种肺部疾病进行分类的可解释人工智能模型

Decision Analytics Journal Pub Date : 2024-07-02 DOI: 10.1016/j.dajour.2024.100499

Eram Mahamud , Nafiz Fahad , Md Assaduzzaman , S.M. Zain , Kah Ong Michael Goh , Md. Kishor Morol

{"title":"An explainable artificial intelligence model for multiple lung diseases classification from chest X-ray images using fine-tuned transfer learning","authors":"Eram Mahamud , Nafiz Fahad , Md Assaduzzaman , S.M. Zain , Kah Ong Michael Goh , Md. Kishor Morol","doi":"10.1016/j.dajour.2024.100499","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100499","url":null,"abstract":"<div><p>Traditional deep learning models are often considered “black boxes” due to their lack of interpretability, which limits their therapeutic use despite their success in classification tasks. This study aims to improve the interpretability of diagnoses for COVID-19, pneumonia, and tuberculosis from X-ray images using an enhanced DenseNet201 model within a transfer learning framework. We incorporated Explainable Artificial Intelligence (XAI) techniques, including SHAP, LIME, Grad-CAM, and Grad-CAM++, to make the model’s decisions more understandable. To enhance image clarity and detail, we applied preprocessing methods such as Denoising Autoencoder, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Gamma Correction. An ablation study was conducted to identify the optimal parameters for the proposed approach. Our model’s performance was compared with other transfer learning-based models like EfficientNetB0, InceptionV3, and LeNet using evaluation metrics. The model that included data augmentation techniques achieved the best results, with an accuracy of 99.20%, and precision and recall of 99%. This demonstrates the critical role of data augmentation in improving model performance. SHAP and LIME provided significant insights into the model’s decision-making process, while Grad-CAM and Grad-CAM++ highlighted specific image features and regions influencing the model’s classifications. These techniques enhanced transparency and trust in AI-assisted diagnoses. Finally, we developed an Android-based system using the most effective model to support medical specialists in their decision-making process.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100499"},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001036/pdfft?md5=b430bd720529ecff7f7f19d1a65e9d47&pid=1-s2.0-S2772662224001036-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141607905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust decision-making in complex business markets: Emerging models and applications 复杂商业市场中的稳健决策：新兴模式与应用

Decision Analytics Journal Pub Date : 2024-06-26 DOI: 10.1016/j.dajour.2024.100497

Mohammad Shabaz, Salman Ahmad, Shah Nazir, Syed Hassan Ahmed Shah, Alexandru Capatina

引用次数: 0

An integrated data envelopment analysis framework for operational efficiency assessment in Brazilian international airports 用于评估巴西国际机场运营效率的综合数据包络分析框架

Decision Analytics Journal Pub Date : 2024-06-13 DOI: 10.1016/j.dajour.2024.100493

Maiquiel Schmidt de Oliveira , Vilmar Steffen , Franklin Angelo Krukoski , Maressa Fontana Mezoni , Flávio Trojan

{"title":"An integrated data envelopment analysis framework for operational efficiency assessment in Brazilian international airports","authors":"Maiquiel Schmidt de Oliveira , Vilmar Steffen , Franklin Angelo Krukoski , Maressa Fontana Mezoni , Flávio Trojan","doi":"10.1016/j.dajour.2024.100493","DOIUrl":"10.1016/j.dajour.2024.100493","url":null,"abstract":"<div><p>International airports play a significant role in a country’s economic development. We propose an integrated framework for operational efficiency analysis using Data Envelopment Analysis, Windows Analysis, and Malmquist Index to identify inefficiencies and areas of improvement in the air transportation industry. This study analyzed 23 Brazilian international airports from 2010 to 2021, using the DEA Window Analysis (DEAWA) combined with the Index (MI). The airports were grouped geographically in southern Brazil. The integration of these methods brought an important insight to the evaluation processes, even for those previously analyzed using DEA. The analyses found that COVID-19 potently influenced the drop in the efficiency index. No airport was fully efficient over the analyzed periods. Private airports achieved an efficiency index slightly higher than public ones, but the difference of the efficiency index by public–private management type is not clear. This type of analysis provides a strategic and comparative tool, making it possible to understand better the weaknesses of the air transportation system.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100493"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000973/pdfft?md5=a16133fdefa31f919d9777ed8c01629c&pid=1-s2.0-S2772662224000973-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141403907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing 研究用于预测增材制造中印刷部件机械性能的集合机器学习技术

Decision Analytics Journal Pub Date : 2024-06-08 DOI: 10.1016/j.dajour.2024.100492

Jayanta Bhusan Deb , Shilpa Chowdhury , Nur Mohammad Ali

{"title":"An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing","authors":"Jayanta Bhusan Deb , Shilpa Chowdhury , Nur Mohammad Ali","doi":"10.1016/j.dajour.2024.100492","DOIUrl":"10.1016/j.dajour.2024.100492","url":null,"abstract":"<div><p>This study investigates the ensemble machine learning models to predict the mechanical properties of the 3D-printed Polylactic Acid (PLA) specimens. We studied the effects of five process parameters, including the build orientation, infill angle, layer thickness, printing speed, and nozzle temperature, on the printed parts tensile strength and surface roughness. Machine learning models are developed using the experimental data collected from the printed 27 specimens. Gradient Boosting Regression, Extreme Gradient Boosting Regression, Adaptive Boosting Regression, Random Forest Regression, and Extremely Randomized Tree Regression models were developed during the machine learning modeling stage to predict the surface roughness and tensile strength of the printed parts. This research demonstrates the effectiveness of Extremely Randomized Tree Regression model in providing accurate tensile strength predictions with root mean square error (RMSE) of 1.03, mean absolute error (MAE) of 0.82, and mean absolute percentage error (MAPE) of 2.20%. Similarly, Random Forest Regression model shows better accuracy in predicting surface roughness having RMSE of 0.408, MAE of 0.31, and MAPE of 9.28%. Moreover, the comparative study confirms that ensemble machine learning techniques are more useful than the traditional support vector and k-nearest neighbor machine learning models for predicting the surface roughness and tensile strength of the printed parts. The results highlight a novel approach of using ensemble machine learning models in identifying complex correlations in the dataset, establishing the foundation for improved product design and mechanical property optimization through adjustment of the process parameters combination.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100492"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000961/pdfft?md5=943b21fd5f416af0ea35b11331903194&pid=1-s2.0-S2772662224000961-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141412019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in big data optimization models, algorithms, and applications 大数据优化模型、算法和应用的进展

Decision Analytics Journal Pub Date : 2024-06-06 DOI: 10.1016/j.dajour.2024.100491

Irfan Ali, Golam Kabir, Umar Muhammad Modibbo, Fariba Goodarzian, Ernesto D.R. Santibanez Gonzalez

引用次数: 0

A mathematical analysis of the effects of control strategies on the transmission dynamics of Chlamydiosis 控制策略对衣原体病传播动态影响的数学分析

Decision Analytics Journal Pub Date : 2024-06-06 DOI: 10.1016/j.dajour.2024.100490

N. Nyerere , Y.A. Liana

{"title":"A mathematical analysis of the effects of control strategies on the transmission dynamics of Chlamydiosis","authors":"N. Nyerere , Y.A. Liana","doi":"10.1016/j.dajour.2024.100490","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100490","url":null,"abstract":"<div><p>Chlamydiosis remains a major public health concern in both developed and developing countries due to its effects on the human reproductive system. While limited modeling studies have been conducted on the transmission dynamics of the disease, none of them have examined the impacts of the combination of environmental hygiene and other control strategies. In this paper, we present a mathematical model to investigate the impacts of public health education, vaccination of susceptible individuals, treatment of symptomatic infected individuals, and environmental hygiene. The basic reproduction number is computed using the next-generation operator and is employed in the stability analysis of equilibrium points. We also perform a sensitivity analysis of the model using a normalized forward sensitivity index to identify the parameters significantly affecting the effective reproduction number. Findings from analytical solutions and numerical simulations demonstrate that, public health education, vaccination, treatment, and environmental hygiene significantly reduce chlamydiosis incidence and prevalence in the population. Consequently, the study recommends implementing these control measures, particularly in regions where the disease is endemic. Thus, findings from this study could be utilized to support decision-making in Chlamydiosis control strategies.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100490"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000948/pdfft?md5=0efe2619ef907392b6e94a7068c4d374&pid=1-s2.0-S2772662224000948-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141303367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Systematic Review for Classification and Selection of Deep Learning Methods 深度学习方法的分类和选择系统综述

Decision Analytics Journal Pub Date : 2024-06-05 DOI: 10.1016/j.dajour.2024.100489

Nisa Aulia Saputra , Lala Septem Riza , Agus Setiawan , Ida Hamidah

{"title":"A Systematic Review for Classification and Selection of Deep Learning Methods","authors":"Nisa Aulia Saputra , Lala Septem Riza , Agus Setiawan , Ida Hamidah","doi":"10.1016/j.dajour.2024.100489","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100489","url":null,"abstract":"<div><p>The effectiveness of deep learning in completing tasks comprehensively has led to a rapid increase in its usage. Deep learning encompasses numerous diverse methods, each with its own distinct characteristics. The aim of this study is to synthesize existing literature in order to classify and identify an appropriate deep learning method for a given task. A systematic literature review was conducted as a comprehensive method of study, utilizing literature spanning from 2012 to 2024. The findings revealed that deep learning plays a significant role in eight main tasks, including prediction, design, evaluation and assessment, decision-making, creating user instructions, classification, identification, and learning models. The effectiveness of various deep learning methods, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Autoencoders (AE), Generative Adversarial Networks (GAN), Deep Neural Networks (DNN), Backpropagation (BP), and Feed-Forward Neural Networks (FFNN), in different tasks was confirmed. These findings provide researchers with a comprehensive understanding for selecting appropriate and effective deep learning methods for specific tasks.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100489"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000936/pdfft?md5=ee18e9e0e0094cbefd5a7dd255052997&pid=1-s2.0-S2772662224000936-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141322739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multiple criteria decision-making model for enhancing informative service quality at airports 提高机场信息服务质量的多标准决策模型

Decision Analytics Journal Pub Date : 2024-06-04 DOI: 10.1016/j.dajour.2024.100487

Shinyi Lin

{"title":"A multiple criteria decision-making model for enhancing informative service quality at airports","authors":"Shinyi Lin","doi":"10.1016/j.dajour.2024.100487","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100487","url":null,"abstract":"<div><p>The hospitality and tourism industry is witnessing unprecedented growth, driven by a relentless pursuit of service excellence and customer satisfaction. In this dynamic landscape, meeting and exceeding customer expectations has become paramount. Customers not only wield significant influence in shaping the service experience but also arrive with predefined standards for quality and service delivery. This study evaluates the quality of informative service settings within airport environments and their profound impact on overall service satisfaction. This research offers diverse strategic interventions to elevate service quality standards by leveraging multiple criteria decision-making. The insights gleaned from this investigation provide invaluable guidance for managers within the air-service industry, equipping them with the requisite knowledge to navigate and address the evolving needs of travelers while actively enhancing the Informative service setting infrastructure within airports. Through a nuanced understanding of these strategies, industry stakeholders can proactively tailor their approaches to ensure heightened customer satisfaction and service excellence.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100487"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000912/pdfft?md5=ace313cecb8c49e184fb2a04096a6afc&pid=1-s2.0-S2772662224000912-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141322740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0