Decision Analytics Journal最新文献

{"title":"A comprehensive review of dwarf mongoose optimization algorithm with emerging trends and future research directions","authors":"Olanrewaju Lawrence Abraham ,&nbsp;Md Asri Ngadi","doi":"10.1016/j.dajour.2025.100551","DOIUrl":"10.1016/j.dajour.2025.100551","url":null,"abstract":"<div><div>The Dwarf Mongoose Optimization (DMO) algorithm, inspired by the behaviors and foraging patterns of dwarf mongooses, is a recently formulated swarm-based metaheuristic method emulating the cooperative behavior of mongooses during food searches. The DMO algorithm effectively addresses various optimization challenges across multiple domains by balancing global and local searches, resulting in near-optimal solutions. Numerous DMO variants have been developed since its inception. A comprehensive survey of recent DMO research from 2022 to August 2024 is provided in this study, beginning with the natural inspiration and conceptual framework of the DMO. It then explores various modifications, hybridizations, and algorithm applications across different fields. Lastly, a meta-analysis of DMO advancements and potential directions for further research are provided.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100551"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387284","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 hybrid multi-objective optimization approach with NSGA-II for feature selection","authors":"Praveen Vijai,&nbsp;Bagavathi Sivakumar P.","doi":"10.1016/j.dajour.2025.100550","DOIUrl":"10.1016/j.dajour.2025.100550","url":null,"abstract":"<div><div>This study introduces a hybrid feature selection technique with a multi-objective algorithm incorporating Information Gain, Random Forest, and Relief F-based approach. We integrate the strengths of filter and wrapper methodologies to enhance the efficacy of addressing feature selection. The information gain, random forest, and relief F-based approach are used to evaluate the significance of features concerning the labels. Subsequently, the information derived from feature scoring is utilized to initialize the population. In addition, the work introduces a new operator for crossover and mutation that uses feature scores to guide these processes. This strategy improves the convergence efficiency and sharpens the search direction of the proposed model within the search space. As part of our empirical research, we compare the suggested model to three different multi-objective feature selection techniques on five different high-dimensional datasets. Our proposed model outperforms state-of-the-art algorithms, as shown by the empirical data. It achieves higher classification accuracy across a range of datasets and exhibits robustness in performance while substantially reducing the feature space.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100550"},"PeriodicalIF":0.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143305938","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 Full Multiplicative Data Envelopment Analysis Model for solving Multi-Attribute Decision-Making problems","authors":"Narong Wichapa ,&nbsp;Atchara Choompol ,&nbsp;Ronnachai Sangmuenmao","doi":"10.1016/j.dajour.2025.100549","DOIUrl":"10.1016/j.dajour.2025.100549","url":null,"abstract":"<div><div>This study presents a novel Full Multiplicative Data Envelopment Analysis (FMDEA) for solving Multi-Attribute Decision-Making (MADM) problems. The proposed model offers an innovative approach to solving MADM problems by integrating the principles of Data Envelopment Analysis (DEA) with Full Multiplicative Form (FMF). This approach effectively addresses the significant limitations of traditional MADM methods, particularly concerning data normalization and computational complexity. We demonstrate the robustness and reliability of the proposed FMDEA model through its application across various decision-making scenarios. We demonstrate the perfect alignment of FMDEA with various decision-making scenarios, such as Multi-Objective Optimization with Full Multiplicative Form (MOOFMF), Multi-Objective Optimization by Ratio Analysis (MOORA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Weighted Aggregated Sum Product Assessment (WASPAS), and Complex Proportional Assessment (COPRAS) in flexible manufacturing. The model exhibited high correlations with MOORA, MOOFMF, TOPSIS, WASPAS, and COPRAS. The FMDEA model consistently aligned with MOORA, MOOFMF, TOPSIS, WASPAS, and COPRAS in Computer Numerical Control (CNC) lathe selection. These results confirm the FMDEA model’s effectiveness in addressing MADM challenges by offering a simple, versatile, and user-friendly framework compatible with various optimization solvers, thus enhancing its practical applicability in complex decision-making contexts.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100549"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136769","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 supervised machine learning models for predicting drivers’ ethical decisions in autonomous vehicles","authors":"Amandeep Singh,&nbsp;Yovela Murzello,&nbsp;Sushil Pokhrel,&nbsp;Siby Samuel","doi":"10.1016/j.dajour.2025.100548","DOIUrl":"10.1016/j.dajour.2025.100548","url":null,"abstract":"<div><div>Vehicle-pedestrian interactions in autonomous vehicles (AVs) present complex challenges that require advanced decision-making algorithms. Understanding the factors influencing ethical decision-making (EDM) in critical situations is essential as AVs become more prevalent. This study addresses a gap in AV research by using predictive analytics methods to develop models that assess decision-making outcomes under varying time pressures. We recruited 204 participants from North America, aged 18-30 years and 65 years and above, for an online experiment. Participants viewed video clips from a driving simulator that simulated ethical dilemmas. They had to decide whether the AV should stay in its lane or change lanes by pressing the spacebar. The principal component analysis identified age, distraction, and trust in automation as the key factors influencing decision-making. Several machine learning models were optimized to predict decision outcomes, with the Gaussian Naive Bayes model demonstrating strong performance across different time pressures. Feature importance analysis highlighted the significant roles of age and trust in automation. Partial dependence plots illustrated the interaction between these factors and their influence on decision-making outcomes under time constraints. These findings contribute to the development of personalized decision-making algorithms for AVs. Predictive analytics provides valuable insights into improving AV systems’ safety, trust, and ethical behavior by accounting for individual differences in decision-making.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100548"},"PeriodicalIF":0.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136016","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 outlier detection framework for Air Quality Index prediction using linear and ensemble models","authors":"Pradeep Kumar Dongre ,&nbsp;Viral Patel ,&nbsp;Upendra Bhoi ,&nbsp;Nilesh N. Maltare","doi":"10.1016/j.dajour.2025.100546","DOIUrl":"10.1016/j.dajour.2025.100546","url":null,"abstract":"<div><div>The Air Quality Index (AQI) is a key indicator for assessing air quality and its associated health impacts. Accurate AQI calculations are crucial for reliable air quality assessments, but outliers in air quality data can distort these calculations, leading to inaccurate predictions. This paper presents a comprehensive framework for air quality prediction that integrates multiple outlier detection methods with machine learning models, focusing on enhancing the accuracy and robustness of predictions. The study investigates various outlier detection techniques, including the Interquartile Range (IQR), robust Z-score, and Mahalanobis distance, and evaluates their impact when integrated into machine learning models. Unlike traditional approaches that remove outliers without considering seasonal effects, this research proposes retaining extreme data points after seasonal validation to improve model generalization and prediction accuracy for unseen data. The framework is evaluated using a dataset from Jaipur city, testing multiple machine learning models, including linear regression, ensemble methods, and K-Nearest Neighbor (KNN) regression. Results show that the integrated framework significantly improves model performance, with the Extra Trees Regressor achieving the best results (MAE = 11.9161, RMSE = 16.1660, and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.8884) after refinement, compared to baseline performance (MAE = 12.6765, RMSE = 17.8452, and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.8737). This study demonstrates the empirical effectiveness of the proposed framework and provides practical guidelines for air quality prediction in real-world applications.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100546"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136018","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 systematic review of machine learning applications in sustainable supplier selection","authors":"Joachim O. Gidiagba ,&nbsp;Lagouge K. Tartibu ,&nbsp;Modestus O. Okwu","doi":"10.1016/j.dajour.2025.100547","DOIUrl":"10.1016/j.dajour.2025.100547","url":null,"abstract":"<div><div>Supplier quality evaluation in industrial sectors is well-recognized due to its direct impact on quality assurance and improvement. This task is challenging due to the need to process extensive qualitative and quantitative data, multi-dimensional attributes, and numerous suppliers. Traditional methods are increasingly inadequate to manage this large amount of data and facilitate effective decision-making. This research paper presents a systematic literature review on adopting machine learning techniques for sustainable supplier selection from 2010 to 2024. From an initial pool of 99 papers in the Scopus database, 20 papers from Web of Science, and 52 papers in Google Scholar, 25, 12 and 13 papers, respectively, were selected for in-depth analysis. The study elucidates the role of machine learning in enhancing supplier selection across various sectors, focusing on literature published in English. The findings indicate that machine learning significantly improves organizational performance by refining supplier selection processes, addressing inconsistencies in traditional methods, and leveraging vast data repositories. Integrating Artificial Intelligence (AI) into supply chain operations enables more rapid and reliable decision-making, especially when conventional approaches falter due to large data volumes. The developed framework identifies the significance of traditional and machine learning methods in different stages of supplier selection, including criteria definition, weighting, evaluation, and ranking.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100547"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136017","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 cognitive platform for collecting cyber threat intelligence and real-time detection using cloud computing","authors":"Prasasthy Balasubramanian,&nbsp;Sadaf Nazari,&nbsp;Danial Khosh Kholgh,&nbsp;Alireza Mahmoodi,&nbsp;Justin Seby,&nbsp;Panos Kostakos","doi":"10.1016/j.dajour.2025.100545","DOIUrl":"10.1016/j.dajour.2025.100545","url":null,"abstract":"<div><div>The extraction of cyber threat intelligence (CTI) from open sources is a rapidly expanding defensive strategy that enhances the resilience of both Information Technology (IT) and Operational Technology (OT) environments against large-scale cyber-attacks. However, for most organizations, collecting actionable CTI remains both a technical bottleneck and a black box. While previous research has focused on improving individual components of the extraction process, the community lacks open-source platforms for deploying streaming CTI data pipelines in the wild. This study proposes an efficient platform capable of processing compute-intensive data pipelines, based on cloud computing, for real-time detection, collection, and sharing of CTI from various online sources. We developed a prototype platform (TSTEM) with a containerized microservice architecture that uses Tweepy, Scrapy, Terraform, Elasticsearch, Logstash, and Kibana (ELK), Kafka, and Machine Learning Operations (MLOps) to autonomously search, extract, and index indicators of compromise (IOCs) in the wild. Moreover, the provisioning, monitoring, and management of the platform are achieved through infrastructure as code (IaC). Custom focus-crawlers collect web content, processed by a first-level classifier to identify potential IOCs. Relevant content advances to a second level for further examination. State-of-the-art natural language processing (NLP) models are used for classification and entity extraction, enhancing the IOC extraction methodology. Our results indicate these models exhibit high accuracy (exceeding 98%) in classification and extraction tasks, achieving this performance within less than a minute. The system’s effectiveness is due to a finely-tuned IOC extraction method that operates at multiple stages, ensuring precise identification with low false positives.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100545"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136019","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 multi-stage decision model for upstream supply chain disaster management readiness assessment","authors":"Detcharat Sumrit,&nbsp;Orawan Jongprasittiphol","doi":"10.1016/j.dajour.2024.100538","DOIUrl":"10.1016/j.dajour.2024.100538","url":null,"abstract":"<div><div>Supply Chain Disaster Management (SCDM) is essential for mitigating disruptions, ensuring business continuity, and maintaining a competitive advantage. This study introduces a comprehensive decision model to assess disaster management readiness within the upstream supply chain. Through an extensive literature review grounded in Contingency Resource-Based View (CRBV) theory, fifteen initial readiness factors (<em>RFs</em>) are identified. The Fuzzy Delphi Method (FDM) then refines these factors to twelve, enhancing their relevance and applicability. Next, the Fuzzy Linguistic Preference Relation (FLinPreRa) method assesses the relative importance of each <em>RF</em>, providing a profound understanding of their individual and collective impact. Finally, Weight-Variance Analysis (WVA) evaluates the strengths and weaknesses of each <em>RF</em>, enabling targeted strategies for enhancing disaster readiness. A case study involving five automotive manufacturers in Thailand showcases the practical application of this decision model. The results indicate that this approach is an effective tool for assessing SCDM readiness, pinpointing critical areas for improvement, and guiding strategic investment. Beyond enhancing disaster preparedness, the model also strengthens overall supply chain resilience and responsiveness. Moreover, the framework can be easily adapted to other industries aiming to improve their SCDM readiness by tailoring it to address sector-specific challenges.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100538"},"PeriodicalIF":0.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136020","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 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}