Decision Analytics Journal最新文献_第10页

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

A predictive modeling framework for forecasting cumulative sales of euro-compliant, battery-electric and autonomous vehicles 预测符合欧洲标准的电池电动汽车和自动驾驶汽车累计销量的预测模型框架

Decision Analytics Journal Pub Date : 2024-06-01 DOI: 10.1016/j.dajour.2024.100483

Anas Alatawneh , Adam Torok

{"title":"A predictive modeling framework for forecasting cumulative sales of euro-compliant, battery-electric and autonomous vehicles","authors":"Anas Alatawneh , Adam Torok","doi":"10.1016/j.dajour.2024.100483","DOIUrl":"10.1016/j.dajour.2024.100483","url":null,"abstract":"<div><p>The automotive industry faces a transformative time with the advent of advanced vehicle technologies and evolving consumer preferences. This research employs predictive modeling to forecast the adoption of Euro 6d and Euro 7 compliant vehicles, Battery Electric Vehicles (BEVs), and Autonomous Vehicles (AVs) in the European Union’s 27 member states and the United Kingdom. This study provides insightful projections and comparative analyses of these technologies’ future trajectories using the modified Gaussian and Logistic models. The modified Gaussian model projected relatively sharp growth curves for all vehicle types, signaling rapid initial adoption followed by saturation. Conversely, the Logistic model depicted more gradual and continuous growth patterns, suggesting sustained market interest over time. Comparative analyses highlight the unique strengths and limitations of each model. The modified Gaussian model proves effective for identifying early market responses and pivotal intervention points, while the Logistic model aids in strategic long-term planning and trend anticipation. However, disparities between the models show the complexity of forecasting automotive market dynamics, emphasizing the need for multifaceted frameworks and approaches. Thus, enhancing predictive accuracy by refining models and integrating additional variables will be pivotal in navigating the dynamic landscape of emerging automotive technologies. This research stands out for its approach to applying analytical methods to predict future market dynamics, offering valuable guidance for policymakers and industry stakeholders in strategizing for the forthcoming technological shift.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100483"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000870/pdfft?md5=1c1985320f03841e384eeddb64cdea58&pid=1-s2.0-S2772662224000870-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141145673","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

Editorial — Emerging trends in data science and business analytics models and applications 社论 - 数据科学和商业分析模型及应用的新趋势

Decision Analytics Journal Pub Date : 2024-06-01 DOI: 10.1016/j.dajour.2024.100480

Mark Broere , Robin Christmann , Andreas Dellnitz , Mohsen Afsharian

引用次数: 0

Emerging trends in Healthcare Decision Support Systems 医疗决策支持系统的新趋势

Decision Analytics Journal Pub Date : 2024-06-01 DOI: 10.1016/j.dajour.2024.100484

Deepika Koundal, Yanhui Guo, Subrata Chakraborty, Romany F. Mansour

引用次数: 0

A federated learning model for integrating sustainable routing with the Internet of Vehicular Things using genetic algorithm 利用遗传算法将可持续路由与车载物联网整合的联合学习模型

Decision Analytics Journal Pub Date : 2024-06-01 DOI: 10.1016/j.dajour.2024.100486

Sushovan Khatua , Debashis De , Somnath Maji , Samir Maity , Izabela Ewa Nielsen

{"title":"A federated learning model for integrating sustainable routing with the Internet of Vehicular Things using genetic algorithm","authors":"Sushovan Khatua , Debashis De , Somnath Maji , Samir Maity , Izabela Ewa Nielsen","doi":"10.1016/j.dajour.2024.100486","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100486","url":null,"abstract":"<div><p>A distributed machine learning technique called federated learning allows numerous Internet of Things (IoT) edge devices to work together to train a model without sharing their raw data. Internet of Vehicular Things (IoVT) are an important tool in smart cities for moving objects, such as knowing the traffic patterns, road conditions, vehicle behavior, etc. To enhance traffic management and optimize routes, federated learning, and IoT must work jointly, which may achieve sustainable development goals (SDG) in many ways. This research outlines a system for federated learning in vehicular networks in smart cities. The suggested architecture considers the difficulties presented by such situations’ restricted network connectivity, privacy issues, and security concerns. The framework employs a hybrid methodology integrating federated learning on a centralized server with local training on individual cars. The proposed framework is assessed based on a real-world dataset from a smart city through IoT devices. The findings demonstrate that the suggested method successfully increases model accuracy while preserving the confidentiality and security of the data. In this investigation, we incorporated the Federated Learning model, which can fetch all the information between arbitrary nodes and derive the Traffic, Fuel Cost, Safety, Parking Cost, and Transportation cost for a better routing approach. The suggested framework can be utilized to increase the effectiveness of the transportation system, decrease congestion in smart cities, and improve traffic management. We employ an improved genetic algorithm (iGA) with generation-dependent even mutation to tackle the emission in the smart environment.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100486"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000900/pdfft?md5=eeb6ddcfcdd52908ced09f3dcc2c8155&pid=1-s2.0-S2772662224000900-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251052","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 predictive analytics approach for forecasting bike rental demand 预测自行车租赁需求的预测分析方法

Decision Analytics Journal Pub Date : 2024-06-01 DOI: 10.1016/j.dajour.2024.100482

Meerah Karunanithi, Parin Chatasawapreeda, Talha Ali Khan

{"title":"A predictive analytics approach for forecasting bike rental demand","authors":"Meerah Karunanithi, Parin Chatasawapreeda, Talha Ali Khan","doi":"10.1016/j.dajour.2024.100482","DOIUrl":"10.1016/j.dajour.2024.100482","url":null,"abstract":"<div><p>The demand for rental bikes in urban areas fluctuates, leading to localized surpluses and shortages. To address this challenge, effective bike relocation strategies are essential for ensuring equitable distribution and maximizing customer satisfaction. This study aims to employ advanced machine learning techniques to forecast bike rental demand in urban areas, thereby enhancing the efficiency and accessibility of bike rental services and contributing to sustainable urban mobility. The study comprehensively analyzes various influencing factors using machine learning models, including Ordinary Least Squares regression, MLP Regression, Gradient Boosting Regression, Random Forest Regression, Polynomial Regression, and Decision Tree Regression. The primary objective is to identify the most accurate predictor by comparing key metrics such as <span><math><mi>R</mi></math></span>-squared (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pearson Correlation Coefficient. Insights gained from this analysis aid in identifying influential variables and ensure the development of resource-efficient and adaptable models, leading to more informed decision-making for rental bike businesses. Additionally, future research directions involve the implementation of artificial intelligence technology to predict overall bike demand based on urban cities’ criteria, including the number of national and international tourists. By addressing these objectives, this study seeks to provide valuable insights and tools for rental bike businesses to optimize operations, make strategic decisions, and enhance customer experience in competitive urban markets.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100482"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000869/pdfft?md5=9f633b17c816d861bc20e729e9695586&pid=1-s2.0-S2772662224000869-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141144749","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 integrated best–worst method and fuzzy TOPSIS for resilient-sustainable supplier selection 用于选择弹性-可持续供应商的最佳-最差法和模糊 TOPSIS 综合法

Decision Analytics Journal Pub Date : 2024-06-01 DOI: 10.1016/j.dajour.2024.100488

Sahar Varchandi , Ashkan Memari , Mohammad Reza Akbari Jokar

{"title":"An integrated best–worst method and fuzzy TOPSIS for resilient-sustainable supplier selection","authors":"Sahar Varchandi , Ashkan Memari , Mohammad Reza Akbari Jokar","doi":"10.1016/j.dajour.2024.100488","DOIUrl":"10.1016/j.dajour.2024.100488","url":null,"abstract":"<div><p>Achieving a balance between economic, environmental, and social factors in supplier selection while prioritizing business continuity poses a considerable challenge. It is imperative to guarantee that selected suppliers adhere to sustainability and resilience requirements while supporting the company’s economic advancement. This study addresses this challenge through a novel approach that combines the Best–Worst Method (BWM) with the Fuzzy Technique Order of Preference by Similarity to Ideal Solution (F-TOPSIS). Integrating these methodologies reduces the burden of pairwise comparisons, a common challenge in supplier selection using multi-criteria decision-making, thereby streamlining the evaluation process. To assess the effectiveness of the proposed model, we implemented our method on an actual case study of e-commerce and conducted a sensitivity analysis of the results. The findings suggest that the proposed method offers improved consistency in rankings across criteria compared to traditional BWM. It also makes a balance between simplicity and accuracy, ensuring that selected suppliers are equipped to handle disruptions and uncertainties. This aligns practical simplicity with theoretical rigor which makes the proposed method more accessible and manageable for practitioners in real-world settings.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100488"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000924/pdfft?md5=eebd793dba470c62dffccfd7c312e60d&pid=1-s2.0-S2772662224000924-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141280415","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 hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems 解决双目标灵活作业车间调度问题的混合模拟算法

Decision Analytics Journal Pub Date : 2024-06-01 DOI: 10.1016/j.dajour.2024.100485

Saman Nessari , Reza Tavakkoli-Moghaddam , Hessam Bakhshi-Khaniki , Ali Bozorgi-Amiri

{"title":"A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems","authors":"Saman Nessari , Reza Tavakkoli-Moghaddam , Hessam Bakhshi-Khaniki , Ali Bozorgi-Amiri","doi":"10.1016/j.dajour.2024.100485","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100485","url":null,"abstract":"<div><p>The flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays a crucial role in enhancing productivity and efficiency in modern manufacturing systems, aimed at optimizing the allocation of jobs to a variable set of machines. This paper introduces an algorithm to tackle the FJSSP by minimizing makespan and total weighted earliness and tardiness under uncertainty. This hybrid algorithm effectively addresses the complexities of stochastic multi-objective optimization by integrating the equilibrium optimizer (EO) as an initial solutions generator, Non-dominated sorting genetic algorithm II (NSGA-II), and simulation techniques. The algorithm’s effectiveness is validated by showcasing specific instances and delivering decision results for optimal scheduling across varying levels of uncertainty. Results reveal the algorithm’s consistent superiority in managing the complexities of stochastic parameters across various problem scales, achieving lower makespan and improved Pareto front quality compared to existing methods. Particularly notable is the algorithm’s faster convergence and robust performance, as validated by the statistical Wilcoxon test, which confirms its reliability and efficacy in handling dynamic scheduling situations. These findings underscore the algorithm’s potential in providing flexible, robust solutions. The proposed algorithm’s unique balance of exploitative and explorative capabilities within a simulation framework enables effective handling of uncertainty in the FJSSP, offering flexibility and customization that is adaptable to various scheduling environments.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100485"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000894/pdfft?md5=7212b786690a27bfbd5ce4f691eeda54&pid=1-s2.0-S2772662224000894-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251051","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 Mamdani fuzzy inference system with trapezoidal membership functions for investigating fishery production 用于调查渔业生产的梯形成员函数马姆达尼模糊推理系统

Decision Analytics Journal Pub Date : 2024-05-18 DOI: 10.1016/j.dajour.2024.100481

Kanisha Pujaru , Sayani Adak , T.K. Kar , Sova Patra , Soovoojeet Jana

{"title":"A Mamdani fuzzy inference system with trapezoidal membership functions for investigating fishery production","authors":"Kanisha Pujaru , Sayani Adak , T.K. Kar , Sova Patra , Soovoojeet Jana","doi":"10.1016/j.dajour.2024.100481","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100481","url":null,"abstract":"<div><p>Seas, marine ecosystems, and coastal regions are crucial components of our environment. Numerous scientific strategies have been adopted to boost fisheries and aquaculture productivity. This study proposes a fuzzy-logic-based model to produce fisheries in India, which ranks fourth worldwide for fisheries production. Five input variables, such as fish seed, export, post-harvesting, released fund, and temperature, are considered inputs, and the production of fisheries is taken as the output variable. A Mamdani-type fuzzy inference system with trapezoidal membership functions is prepared with 243 rules in the IF-THEN format. This mathematical model investigates the impacts of input parameters on the production of Indian fisheries. We fit the model with the real-world data and show that fish seed, export, released fund, and post-harvesting facilities positively impact fisheries production. However, a very high temperature is unsuitable for high production, even if all other parameters lie at their desired level.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100481"},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000857/pdfft?md5=f6fc9505e0ef1af7e8473e41b98f7d0a&pid=1-s2.0-S2772662224000857-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141097375","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