Decision Analytics Journal最新文献

{"title":"An efficiency score unification method in data envelopment analysis using slack-based models with application in banking","authors":"Ali Hadi ,&nbsp;Alireza Amirteimoori ,&nbsp;Sohrab Kordrostami ,&nbsp;Saeid Mehrabian","doi":"10.1016/j.dajour.2024.100541","DOIUrl":"10.1016/j.dajour.2024.100541","url":null,"abstract":"<div><div>Providing a unique estimation model of the efficiency score for a bank branch has long been a primary concern for bank managers, who frequently reject efficiency studies because they contend that a single perspective evaluation cannot adequately display the multifunctional nature of decision-making units (DMUs). This paper presents a unification model for efficiency scores in the banking industry. The proposed model evaluates the efficiency of decision-making units through a two-stage method from different perspectives. This study calculates efficiency scores from several aspects and various inputs/outputs in the first stage, so an expanded space is used in the second stage. All viewpoints are transferred into a new space, creating a new efficient frontier in the expanded space. This model presents the unified efficiency score from DMUs by applying the slack-based model (SBM), which proposes enhancement guidelines. A unified efficiency score is created by considering three perspectives: production, profitability, and intermediary. Unlike the average score, the results show that the unified efficiency score can reflect the performance difference between the three scores achieved from the three perspectives. Additionally, this method demonstrates that DMUs cannot achieve overall efficiency if they are inefficient in at least one of the three aspects.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100541"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136014","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}

{"title":"An investigation of ensemble learning techniques for obesity risk prediction using lifestyle data","authors":"Shahid Mohammad Ganie ,&nbsp;Bobba Bharath Reddy ,&nbsp;Hemachandran K ,&nbsp;Manjeet Rege","doi":"10.1016/j.dajour.2024.100539","DOIUrl":"10.1016/j.dajour.2024.100539","url":null,"abstract":"<div><div>Obesity disease is a significant health issue and has affected millions of people worldwide. Identifying underlying reasons for the onset of obesity risk in its early stage has become challenging for medical practitioners. The growing volume of lifestyle data related to obesity has made it imperative to employ effective machine-learning algorithms that can gather insights from the underlying data trends and identify critical patient conditions. In this study, an ensemble learning approach including boosting, bagging, and voting techniques was used for obesity risk prediction based on lifestyle dataset. Specifically, XGBoost, Gradient Boosting, and CatBoost models are used for boosting, Bagged Decision Tree, Random Forest, and Extra Tree models are used for bagging, and Logistic Regression, Decision Tree, and Support Vector Machine models are used for voting. Different preprocessing steps were employed to improve the quality assessment of the data. Hyperparameter tuning and feature selection and ranking are also used to achieve better prediction results. The considered models are extensively evaluated and compared using various metrics. Among all the models, XGBoost performed better with an accuracy of 98.10%, precision and recall of 97.50%, f1-score of 96.50%, and AUC-ROC of 100%, respectively. Additionally, weight, height, and age features were identified and ranked as the most significant predictors using the recursive feature elimination method for obesity risk prediction. Our proposed model can be used in the healthcare industry to support healthcare providers in better predicting and detecting multiple stages of obesity diseases.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100539"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136015","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}

{"title":"A graph convolutional network for optimal intelligent predictive maintenance of railway tracks","authors":"Saeed MajidiParast ,&nbsp;Rahimeh Neamatian Monemi ,&nbsp;Shahin Gelareh","doi":"10.1016/j.dajour.2024.100542","DOIUrl":"10.1016/j.dajour.2024.100542","url":null,"abstract":"<div><div>This study presents a prescriptive analytics framework for optimal intelligent predictive maintenance of railway tracks. We use machine learning and Graph Convolutional Networks (GCNs) to optimize the maintenance schedules for railway infrastructure and enhance operational efficiency and safety. The model leverages vast data, including geometric measurements and historical maintenance records, to predict potential track failures before occurrence. This proactive maintenance strategy promises to reduce downtime and extend the lifespan of railway assets. Through detailed computational experiments, the effectiveness of the proposed model is demonstrated, providing a significant step forward in applying advanced machine learning techniques to the maintenance of critical transportation infrastructures.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100542"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136770","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}

{"title":"An Optimization of multi-level multi-objective cloud production systems with meta-heuristic algorithms","authors":"Mohammadreza Razdar ,&nbsp;Mohammad Amin Adibi ,&nbsp;Hassan Haleh","doi":"10.1016/j.dajour.2024.100540","DOIUrl":"10.1016/j.dajour.2024.100540","url":null,"abstract":"<div><div>The inability of small companies to compete with large production systems has led to the sharing resources in cloud production systems among smaller companies to compensate for their production deficiencies. This study proposes a multi-objective, multi-level cloud production system by minimizing the maximum completion time of activities, the costs of the entire cloud production system, and the maximum risk of information disclosure. We use a support vector machine (SVM) to train the input data, including activities, micro activities, and services of production units. The correlation between the trained and predicted data from the machine learning model equals 0.9977, indicating this method’s high accuracy and efficiency. The Lp-Metric, multi-objective grey wolf optimizer (MOGWO), and non-dominated sorting genetic algorithm-II (NSGA-II) algorithms were used to solve the problem using trained input data. The Lp-Metric results show that reducing the completion time of all activities and the maximum risk of information disclosure increases the costs of the entire cloud production system. We also examine the efficiency of the solution methods and demonstrate that MOGWO is more efficient in solving the cloud production system problem.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100540"},"PeriodicalIF":0.0,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136021","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}

{"title":"A Dynamic Optimization Model Considering Cost and Speed in the Cobb-Douglas Production Function","authors":"Yuta Motoyama","doi":"10.1016/j.dajour.2024.100535","DOIUrl":"10.1016/j.dajour.2024.100535","url":null,"abstract":"<div><div>This paper addresses the problem of cost minimization in a dynamic production environment where total costs depend not only on the production amount but also on production speed—a factor often overlooked in traditional production models. Unlike previous studies that focus solely on production volume, we incorporate production speed as a critical component, reflecting real-world scenarios where faster operations lead to additional costs such as machinery wear, energy consumption and training cost for workers. Utilizing the Cobb–Douglas production function, we derive a comprehensive total cost function and introduce an innovative application of the Euler–Lagrange equation to minimize these costs. This approach, rarely applied in the context of optimal production planning, enables us to determine the time-dependent optimal production quantity. Our analysis reveals that the optimal production amount evolves in an increasing and concave pattern over time, offering a novel and practical framework for companies seeking to balance production amount and speed for cost-efficient management.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100535"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136010","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}

{"title":"A predictive analytics framework for forecasting soccer match outcomes using machine learning models","authors":"Albert Wong ,&nbsp;Eugene Li ,&nbsp;Huan Le ,&nbsp;Gurbir Bhangu ,&nbsp;Suveer Bhatia","doi":"10.1016/j.dajour.2024.100537","DOIUrl":"10.1016/j.dajour.2024.100537","url":null,"abstract":"<div><div>Predicting the outcome of a sports game is a favourite pastime for sports fans and researchers. The interest has intensified in recent years due to data availability, the development and successful implementation of machine learning algorithms, and the proliferation of internet gaming. This research focuses on developing a predictive analytics framework using machine learning or artificial intelligence models, as well as publicly available game results and weather data, to accurately predict outcomes of games in the English Premier League. Development efforts include experimentation using weather data and constructs such as fatigue and momentum. Ensemble techniques such as stacking or voting are also explored to improve the accuracy of basic machine learning models. The results are compared with those derived from the odds given by the major bookmakers to gauge the usefulness and potential applications in sports betting.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100537"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136013","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}

{"title":"A novel predictive analytics model for forecasting short-term trends in equity assets prices","authors":"Fabián Achury-Calderón ,&nbsp;John A. Arredondo ,&nbsp;Leidy Catherinne Sánchez Ascanio","doi":"10.1016/j.dajour.2024.100534","DOIUrl":"10.1016/j.dajour.2024.100534","url":null,"abstract":"<div><div>This paper introduces a new predictive analytics model for forecasting stock price trends in financial assets traded on major stock exchanges worldwide and the Colombian Stock Exchange. The model is built on a probability space definition that consists of a measurable space derived from filtration. In this paper, the filtration is used to index two distinct <span><math><mi>σ</mi></math></span>-algebras: one from the probability space generated by the Autoregressive Integrated Moving Average model (<em>ARIMA</em>) applied to the price of the asset and another from a probability space created by a random walk with parameters for step size and probability terms, reflecting the asset’s historical behavior. However, in other applications, different probability distribution functions can be utilized. We propose a hypothesis about the trend and assess it using the assets mentioned above.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100534"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136012","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}

{"title":"Expanding strategic vision: The role of Non-Utopian Unreal Scenarios in decision-making","authors":"Félix O. Socorro Márquez ,&nbsp;Giovanni E. Reyes Ortiz ,&nbsp;Delys Y. Palacios Landaeta","doi":"10.1016/j.dajour.2024.100536","DOIUrl":"10.1016/j.dajour.2024.100536","url":null,"abstract":"<div><div>The main goal of this study is to document the Non-Utopian Unreal Scenarios —or NUUS— and to propose a mathematical approach, presenting them as a strategic tool that seeks to contribute to decision-making processes in various fields, in addition to those already existing, such as, for example, the Hurwicz, Wald, Savage and Laplace criteria. Traditional scenario planning often focuses on probable outcomes, which can limit strategic vision and the preparation of strategies that go beyond the limits set by the six existing scenarios. However, NUUS encourages the consideration of improbable but possible high-impact scenarios, thus broadening the scope of strategic planning. We employ a qualitative methodology, integrating insights from the scenario planning and risk management literature. We further highlight the limitations of conventional approaches prioritizing the Maximax and Maximin criteria, proposing a seventh scenario incorporating a broader range of possibilities. The study demonstrates how NUUS can facilitate more robust strategic foresight by analysing historical case studies and theoretical frameworks. Mathematical reasoning is used to develop a framework for calculating the implications of these scenarios, emphasizing the importance of probabilistic thinking in risk assessment. We show organizations can benefit from adopting NUUS to navigate uncertainty and reduce risk. We also discuss the practical applications of NUUS, illustrating its potential to inform strategic decisions in complex environments. This study contributes to strategic management by integrating NUUS into decision-making frameworks. Although further empirical studies will be required to validate the effectiveness of NUUS in real-world applications, with the ultimate goal of equipping organizations with the tools necessary to thrive in an increasingly unpredictable world, the conceptualization of NUUS could serve as an impetus for this. The implications of this study extend beyond theoretical discourse and offer practical insights for practitioners seeking to enhance their strategic planning and scenario exploration capabilities.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100536"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136766","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}

{"title":"Asymmetric impacts of the World Uncertainty Index and exchange rates on tourism using non-linear Autoregressive Distributed Lag models","authors":"Xuefeng Zhang ,&nbsp;Yueyi Chen ,&nbsp;Xiangqing Lu ,&nbsp;Woraphon Yamaka","doi":"10.1016/j.dajour.2024.100530","DOIUrl":"10.1016/j.dajour.2024.100530","url":null,"abstract":"<div><div>The tourism industry is highly susceptible to global economic fluctuations and uncertainties. Understanding the asymmetric effects of macroeconomic factors on tourism demand is crucial for developing effective policies and strategies in the sector. This study examines the asymmetric impacts of the World Uncertainty Index (WUI) and exchange rates on outbound tourism demand from Thailand to five Association of Southeast Asian Nations (ASEAN) destinations. We employ linear and non-linear Autoregressive Distributed Lag (ARDL) models using quarterly data from 2003Q1 to 2023Q4. The results demonstrate that the asymmetric ARDL model outperforms its linear counterpart, revealing significant non-linear dynamics in tourism demand. We find that Thai outbound tourism responds more strongly to currency depreciations in destination countries than to appreciations. This asymmetry aligns with prospect theory, suggesting that tourists are more sensitive to potential cost savings than equivalent increases. The study also shows that increases in global economic uncertainty, as measured by the WUI, have a more pronounced negative impact on tourism flows than decreases in uncertainty. These findings have important implications for policymakers and tourism stakeholders in ASEAN countries. They highlight the need for tailored strategies to capitalize on favorable exchange rate movements and mitigate the impacts of global economic uncertainty on regional tourism flows.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100530"},"PeriodicalIF":0.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136009","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}

{"title":"A machine learning predictive model for bushfire ignition and severity: The Study of Australian black summer bushfires","authors":"Kasinda Henderson ,&nbsp;Ripon K. Chakrabortty","doi":"10.1016/j.dajour.2024.100529","DOIUrl":"10.1016/j.dajour.2024.100529","url":null,"abstract":"<div><div>Australian bushfires are catastrophic, and their impacts span social, economic, and environmental factors. To reduce the damages experienced by bushfires, predicting Australian bushfire ignition allows for an early warning system to give first responders and disaster managers prompt and accurate information. Traditional methods of bushfire ignition prediction suffer from incorporating large-dimensional data and take extensive computational time. Applying machine learning (ML) models enhances accuracy and reduces the computational time required to predict bushfire ignition. This study proposes a predictive model that can take meteorological and topographical data and determine the probability of Australian bushfire ignition and severity using historical fire detection gathered from the Black Summer Bushfire Disaster. The Black Summer Bushfire Disaster occurred between December 2019 and February 2020. The fires affected numerous towns throughout Victoria, New South Wales, and the Australian Capital Territory; hence, the varying topographical and meteorological conditions allow fire ignition and severity influences to be explored. The proposed methodology incorporates Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Convolutional Neural Networks (CNN), and K-nearest Neighbour (kNN) Algorithms. The proposed method relies on five datasets. Meteorological data is sourced from the Bureau of Meteorology (BOM), Australia. Topographic data is sourced from Geoscience Australia and the National Aeronautics and Space Administration’s (NASA’s) Aqua and Terra satellites, which utilize a Moderate Resolution Imaging Spectroradiometer (MODIS). Active Fire Point Data is also sourced from NASA MODIS, which can detect fires. The proposed methodology aims to act as an early warning system by providing a fire occurrence and fire intensity warning map and the probability of fire occurrence and fire intensity depending on the current meteorological climate.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100529"},"PeriodicalIF":0.0,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136011","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}