Decision Analytics Journal最新文献

{"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}

{"title":"A comprehensive evaluation of machine learning and deep learning algorithms for wind speed and power prediction","authors":"Haytham Elmousalami ,&nbsp;Hadi Hesham Elmesalami ,&nbsp;Mina Maxi ,&nbsp;Ahmed Abdel Kader Mohamed Farid ,&nbsp;Nehal Elshaboury","doi":"10.1016/j.dajour.2024.100527","DOIUrl":"10.1016/j.dajour.2024.100527","url":null,"abstract":"<div><div>Accurate wind speed and power predictions are crucial for renewable wind energy applications. This study compares and evaluates twelve machine learning (ML) and deep learning (DL) algorithms, including single and ensemble models across various time scales from 10 min to a day and a half ahead with a particular focus on ensemble prediction algorithms. Moreover, the study proposes a wind speed and power prediction system where the outcome of the wind speed prediction (WSP) model serves as input for the wind power prediction (WPP) model. Several evaluation metrics, such as mean absolute percentage error (MAPE) and mean square error (MSE) were calculated to benchmark different model accuracies. For WSP, the extremely randomized trees, decision tree, and bagging ensemble algorithms demonstrated high accuracy across different time scales where the MAPE ranged from 3.4% to 9.2%, the MSE ranged from 0.17 to 1.15, and the adjusted coefficient of determination ranged from 94% to 99%. For WPP, bagging ensemble algorithms and extremely randomized trees were also effective for predicting different time scales where the MAPE ranged from 4.12% to 11.7% and the MSE ranged from 10945 to 2.4. Ensemble ML algorithms provide better and more accurate results than single ML algorithms. The extreme gradient boosting model showed relatively small computational time and memory according to computational cost. Moreover, this study conducted a sensitivity analysis where air pressure, wind vane, and humidity were the key predictors for WSP and WPP.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100527"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168124","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 comprehensive prescriptive action plan development model for information systems through project prioritization and roadmap planning","authors":"Nicolas Ribeiro Pires de Sousa ,&nbsp;João Eduardo Quintela Alves de Sousa Varajão","doi":"10.1016/j.dajour.2024.100528","DOIUrl":"10.1016/j.dajour.2024.100528","url":null,"abstract":"<div><div>Information technology/information systems investments are critical for business success. However, managers often face the challenge of prioritizing and selecting projects that better satisfy their needs, mainly due to the high number of options available and limited resources. Since investment decisions can strongly impact the organizational path, well-defined processes are required to guide them. At present, there is a lack of clarity about how organizations should proceed in information systems planning since most of the research literature focuses on decision models and criteria that only produce a prioritized list of initiatives without addressing the process and the subsequent need to define a plan for project implementation. In this article, we propose a new method for supporting the definition of information systems action plans resulting from a design science research project carried out in a public organization. We contribute to the theory and practice by providing a method that outlines the process comprehensively and describes the definition and application of a straightforward decision model for project prioritization in a real case, aiming to assist managers in conducting an efficient decision and prioritization of information technology investments.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100528"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168126","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 artificial intelligence multitier system with lightweight classifier for automated helmetless biker detection","authors":"Nur Mohammad Fahad ,&nbsp;Sadman Sakib ,&nbsp;Md. Ibrahim Ratul ,&nbsp;Md. Jakarea ,&nbsp;Abdul Karim Ibne Mohon ,&nbsp;Shahinur Alam Bhuiyan ,&nbsp;Md. Reduan Sarker","doi":"10.1016/j.dajour.2024.100526","DOIUrl":"10.1016/j.dajour.2024.100526","url":null,"abstract":"<div><div>Bike accidents on roads have become a significant concern nowadays. People suffer due to the tragic consequences of road accidents due to the reluctance of bike riders to wear proper helmets, reflecting their lack of awareness, leading to fatalities. This study addresses classification and detection tasks by constructing three custom datasets to identify and categorize bikers riding with or without helmets and assess helmet quality. Photometric data augmentation is applied to balance the dataset images. The primary goal of this study is to develop a computationally efficient convolutional neural network (CNN)-based model named ‘BikeNet-12’ for accurate classification tasks. Various performance metrics are employed to evaluate the model’s overall effectiveness. The BikeNet-12 model achieves a test accuracy of 99.32% in dataset 1. The effectiveness of the proposed approach is validated by experiments on a Safety Helmet Classifier dataset Performance comparison with various transfer learning models demonstrates the applicability of the model in terms of performance metrics. Dataset 2 is utilized to assess helmet quality as safe, unsafe, or inappropriate, and the model achieves the highest accuracy of 98.93%, showcasing its efficacy. Additionally, dataset 3 is employed with the YOLOv8 model to detect non-helmet headwear, such as caps, hijabs, and turbans, among riders, yielding satisfactory results with a mean average precision of 93.7%. Integrating classification and detection tasks positions the model as a potential application to enhance biker safety, promote precaution, and contribute to increased sustainability.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100526"},"PeriodicalIF":0.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721451","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 multi-objective production scheduling model and dynamic dispatching rules for unrelated parallel machines with sequence-dependent set-up times","authors":"Pham Duc Tai,&nbsp;Papimol Kongsri,&nbsp;Prasal Soeurn,&nbsp;Jirachai Buddhakulsomsiri","doi":"10.1016/j.dajour.2024.100525","DOIUrl":"10.1016/j.dajour.2024.100525","url":null,"abstract":"<div><div>This study presents a production scheduling for unrelated parallel machines with machine and job sequence-dependent setup times. The system performance measures to minimize include makespan, total tardiness, and number of tardy jobs. The aim of the study is to develop a solution methodology that can solve the problem for the large scale. First, the problem is formulated as a mixed-integer linear programming model. The augmented <span><math><mi>ɛ</mi></math></span>-constraint method is applied to find Pareto solutions for small problem instances. The purpose is to demonstrate that Pareto solutions, which balance the trade-offs among three measures of performance, can be found for these instances. Dispatching rule-based heuristics is developed to solve large problem instances. The heuristics feature three dispatching rules that are designed to handle the dependent setup time. In addition, these rules are combined into six variants using a time-based rule-switching mechanism. The heuristics are tested with 18 problem instances, containing 244 to 298 jobs, in two demand scenarios derived from the monthly demand data from an industrial user. Under each demand scenario, a set of heuristics that provides the best performance with respect to the three measures is identified. The heuristics include combinations of the shortest completion time and due date-based rules. Finally, a multi-criteria decision-making analysis is performed to determine the conditions specified by the weight given to each measure, with which one heuristic is preferred over the others.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100525"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662863","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}