Journal of Management Analytics最新文献_第3页

MCDM technique using single-valued neutrosophic trigonometric weighted aggregation operators MCDM技术使用单值嗜中性三角加权聚合算子

2区管理学

Journal of Management Analytics Pub Date : 2023-10-08 DOI: 10.1080/23270012.2023.2264294

Jun Ye, Shigui Du, Rui Yong

{"title":"MCDM technique using single-valued neutrosophic trigonometric weighted aggregation operators","authors":"Jun Ye, Shigui Du, Rui Yong","doi":"10.1080/23270012.2023.2264294","DOIUrl":"https://doi.org/10.1080/23270012.2023.2264294","url":null,"abstract":"AbstractMotivated based on the trigonometric t-norm and t-conorm, the aims of this article are to present the trigonometric t-norm and t-conorm operational laws of SvNNs and then to propose the SvNN trigonometric weighted average and geometric aggregation operators for the modelling of a multiple criteria decision making (MCDM) technique in an inconsistent and indeterminate circumstance. To realize the aims, this paper first proposes the trigonometric t-norm and t-conorm operational laws of SvNNs, which contain the hybrid operations of the tangent and arctangent functions and the cotangent and inverse cotangent functions, and presents the SvNN trigonometric weighted average and geometric operators and their properties. Next, a MCDM technique is proposed in view of the presented two aggregation operators in the circumstance of SvNNs. In the end, an actual case of the choice issue of slope treatment schemes is provided to indicate the practicability and effectivity of the proposed MCDM technique.Keywords: Single-valued neutrosophic numbertrigonometric t-norm and t-conormtrigonometric weighted aggregation operatordecision making Data availabilityAll data are included in this study.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135198087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Commodity layout in supermarkets: using the integration of the comprehensive related value method and genetic algorithm 超市商品布局:综合相关值法与遗传算法的结合

2区管理学

Journal of Management Analytics Pub Date : 2023-09-21 DOI: 10.1080/23270012.2023.2258376

Chenxia Jin, Fachao Li, Yuqing Xia, Sohail S. Chaudhry

{"title":"Commodity layout in supermarkets: using the integration of the comprehensive related value method and genetic algorithm","authors":"Chenxia Jin, Fachao Li, Yuqing Xia, Sohail S. Chaudhry","doi":"10.1080/23270012.2023.2258376","DOIUrl":"https://doi.org/10.1080/23270012.2023.2258376","url":null,"abstract":"AbstractThe existing shelf layout methods do not explicitly consider the attention and relevancy of the commodity systematically and thus have failed to capture the invalid associations, resulting in poor sales impact and customer satisfaction. For such shortcomings, in this paper, we propose a mathematical programming approach for shelf layout problems based on comprehensive related value. First, we introduce the concepts of related value considering both attention and relevancy; second, we give the concept of adjacent utility value and the freedom of placement, and further analyze the impact of the same commodity on surrounding commodities due to different placement positions; third, we establish a new comprehensive related value-based commodity layout optimization model (CRV-CL) and provide the solution steps integrating with a genetic algorithm. Finally, we analyze the characteristics of CRV-CL through a specific case. The simulation results indicate the overall relevancy after applying the CRV-CL model.Keywords: Shelf layoutcomprehensive related valuefreedom of placementadjacent utility valuegenetic algorithm Disclosure statementNo potential conflict of interest was reported by the author(s).Ethical approvalThis article does not contain any studies with human participants or animals performed by any of the authors.Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Grant (72101082); the Natural Science Foundation of Hebei Province under Grant (F2021208011). The research of Sohail S. Chaudhry was partially supported through a research sabbatical leave from Villanova University.","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136154828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using a novel ensemble learning framework to detect financial reporting misconduct 使用一种新颖的集成学习框架来检测财务报告不当行为

2区管理学

Journal of Management Analytics Pub Date : 2023-09-14 DOI: 10.1080/23270012.2023.2258372

Siqi Pan, Qiang Ye, Wen Shi

{"title":"Using a novel ensemble learning framework to detect financial reporting misconduct","authors":"Siqi Pan, Qiang Ye, Wen Shi","doi":"10.1080/23270012.2023.2258372","DOIUrl":"https://doi.org/10.1080/23270012.2023.2258372","url":null,"abstract":"AbstractOur research focuses on detecting financial reporting misconduct and derives a comprehensive misconduct sample using AAERs and intentional restatements. We develop a novel ensemble learning method, Multi-LightGBM, for highly imbalanced classification learning. We adopt a human-machine cooperation feature selection method, which can mitigate the limitation of incomplete theories, enhance the model performance, and guide researchers to develop new theories. We propose a cost-based measure, expected benefits of classification, to evaluate the economic performance of a model. The out-of-sample tests show that Multi-LightGBM, coupled with the features we selected, outperforms other predictive models. The finding that introducing intentional material restatements into our predictive model does not reduce the effectiveness of capturing AAERs has important implications for research on AAERs detection. Moreover, we can identify more misconduct firms with fewer resources by the misconduct sample relative to the standalone AAERs sample, which is quite beneficial for most model users.Keywords: financial reporting misconductensemble learningfeature selectionLightGBM Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by National Natural Science Foundation of China under [grant numbers 72071038, 72121001].","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134970106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence applications in finance: a survey 人工智能在金融中的应用:一项调查

2区管理学

Journal of Management Analytics Pub Date : 2023-08-10 DOI: 10.1080/23270012.2023.2244503

Xuemei Li, Alexander Sigov, Leonid Ratkin, Leonid A. Ivanov, Ling Li

{"title":"Artificial intelligence applications in finance: a survey","authors":"Xuemei Li, Alexander Sigov, Leonid Ratkin, Leonid A. Ivanov, Ling Li","doi":"10.1080/23270012.2023.2244503","DOIUrl":"https://doi.org/10.1080/23270012.2023.2244503","url":null,"abstract":"AbstractFinance is in our daily life. We invest, borrow, lend, budget, and save money. Finance also provides guidelines for corporation and government spending and revenue collection. Traditional statistical solutions such as regression, PCA, and CFA have been widely used in financial forecasting and analysis. With the increasing interest in artificial intelligence in recent years, this paper reviews the Artificial Intelligence (AI) techniques in the finance domain systematically and attempts to identify the current AI technologies used, major applications, challenges, and trends in Finance. It explores AI-related articles in Finance in IEEE Xplore and EI compendex databases. Findings suggest AI has been engaged in Finance in financial forecasting, financial protection, and financial analysis and decision-making areas. Financial forecasting is one of the main sub-fields of Finance affected by AI technology. Major AI technology used is the supervised learning. Deep learning has gained popular in recent years. AI could be used to address some emerging topics.Keywords: machine learning; artificial intelligencefinance Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135553759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An inventory analysis in a multi-echelon supply chain system under asymmetry fuzzy demand: a fmincon optimization 不对称模糊需求下多级供应链系统库存分析:fmincon优化

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2023-07-31 DOI: 10.1080/23270012.2023.2239818

B. Karthick

引用次数: 0

Predicting song popularity based on spotify's audio features: insights from the Indonesian streaming users 基于spotify音频功能预测歌曲受欢迎程度:来自印尼流媒体用户的见解

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2023-07-27 DOI: 10.1080/23270012.2023.2239824

H. Saragih

引用次数: 0

Linguistic neutrosophic matrix energy and its application in multiple criteria group decision-making 语言嗜中性矩阵能量及其在多准则群决策中的应用

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2023-07-20 DOI: 10.1080/23270012.2023.2232804

Rui Yong, S. Du, Junting Ye

引用次数: 0

Cold supply chain inventory models for agricultural products with multi-stage stochastic deterioration rates 具有多阶段随机劣化率的农产品冷供应链库存模型

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2023-07-07 DOI: 10.1080/23270012.2023.2229312

R. Latpate, Maruti Bhosale, Sandesh S. Kurade

引用次数: 1

A multi-agent deep reinforcement learning approach for solving the multi-depot vehicle routing problem 一种求解多仓库车辆路径问题的多智能体深度强化学习方法

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2023-07-03 DOI: 10.1080/23270012.2023.2229842

Ali Arishi, K. Krishnan

引用次数: 0

Study on coopetition relationship simulation among M-commerce information service subjects based on Lotka-Volterra model 基于Lotka-Volterra模型的移动商务信息服务主体间合作关系仿真研究

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2023-06-19 DOI: 10.1080/23270012.2023.2219999

Xiaojun Xu, Linzhong Xu, Xiaoli Wang

引用次数: 0