Decision Analytics Journal最新文献

{"title":"A bi-objective stochastic model for operating room scheduling considering surgeons’ preferences and collaborative surgeries","authors":"Rana Azab ,&nbsp;Amr Eltawil ,&nbsp;Mohamed Gheith","doi":"10.1016/j.dajour.2024.100544","DOIUrl":"10.1016/j.dajour.2024.100544","url":null,"abstract":"<div><div>Operating Rooms (ORs) are pivotal hospital resources, significantly impacting expenses and revenue. This paper introduces a stochastic bi-objective model for OR allocation and scheduling of elective surgeries, considering surgeons’ preferences for specific ORs and preferred start times, as well as the integration of collaborative surgeries (CSs)—where multiple surgeons collaborate to perform a procedure. The proposed stochastic model, which accounts for the inherent uncertainty in surgery durations, seeks to minimize operating costs while maximizing surgeons’ preferences, thus offering a balanced solution for hospital management and medical staff. The model is formulated as a Mixed-Integer Linear Programming (MILP) problem and solved using the Sample Average Approximation (SAA) method. A comprehensive sensitivity analysis was conducted to precisely determine the optimal sample size, defined as the number of scenarios used to model the uncertain surgery durations, to ensure the robustness of the proposed approach. This is essential for approximating the probability distribution of surgery durations, for which a lognormal distribution was employed. This analysis enables stable results concerning the variability in surgery durations. Subsequently, the model was applied to a synthesized dataset, which mirrors real hospital operations. The results demonstrated that the model generates optimal OR and surgeon schedules robust enough to accommodate the inherent variability in surgery durations. Additionally, a Pareto-front analysis was employed to examine the trade-off between minimizing operating costs and maximizing surgeons’ preferences. Implementing a bi-objective optimization algorithm using the <span><math><mi>ɛ</mi></math></span>-constraint method identified a set of optimal schedules, offering valuable insights into balancing cost efficiency and surgeon satisfaction, thereby enabling hospital administrators to make informed scheduling decisions. Extensive numerical experiments were conducted to test the model’s scalability and effectiveness in generating optimal schedules under various operational conditions. The results of these experiments suggest that future work could focus on leveraging heuristic techniques to enhance computational efficiency. In conclusion, the proposed stochastic bi-objective model represents a comprehensive and flexible strategy for enhancing operational efficiency and improving surgeon satisfaction in the allocation and scheduling of ORs.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100544"},"PeriodicalIF":0.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136767","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 integrated mathematical epidemiology and inventory model for high demand and limited supplies under uncertainty","authors":"Yofre H. Garcia ,&nbsp;Saul Diaz-Infante ,&nbsp;Jesus A. Minjarez-Sosa","doi":"10.1016/j.dajour.2024.100543","DOIUrl":"10.1016/j.dajour.2024.100543","url":null,"abstract":"<div><div>At the start of the Coronavirus Disease (COVID-19) vaccination campaign in Mexico, the vaccine was the world’s most essential and scarce asset. Managing its administration to optimize its use was, and still is, of paramount importance. However, when the first vaccine was developed at the end of 2020, due to unprecedented demands and early manufacturing of vaccines, decision-makers had to consider the management of this asset with high uncertainty. We aim to analyze how random fluctuations in reorder points and delivery quantity impact the mitigation of a given outbreak. Because decision-makers would need to understand the implications of planning with a volatile vaccine supply, we have focused our effort on developing numerical tools to evaluate vaccination policies. One of our main objectives is to determine how many vaccines to administer per day so that a hypothetical vaccine inventory keeps its integrity while optimizing the mitigation of the outbreak. Our research uses classic models from inventory management and mathematical epidemiology to quantify uncertainty in a hypothetical vaccine inventory. By plugging a classic inventory model into an epidemic compartmental structure, we formulate a problem of sequential decisions. Then, we investigate how the random fluctuations in the reorder time and number of doses in each delivery impact a hypothetical ongoing vaccine campaign. Our simulations suggest that sometimes, it is better to delay vaccination until the vaccine supply is large enough to achieve a significant response.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100543"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136768","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 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}