Decision Analytics Journal最新文献_第2页

A systematic review of simulation models in medicine supply chain management: Current state and emerging trends.

Decision Analytics Journal Pub Date : 2025-02-21 DOI: 10.1016/j.dajour.2025.100555

Hellen Nabayiga, Robert Van Der Meer, Mouhamad Shaker Ali Agha

{"title":"A systematic review of simulation models in medicine supply chain management: Current state and emerging trends.","authors":"Hellen Nabayiga, Robert Van Der Meer, Mouhamad Shaker Ali Agha","doi":"10.1016/j.dajour.2025.100555","DOIUrl":"10.1016/j.dajour.2025.100555","url":null,"abstract":"<div><div>Simulation modelling has widely been applied in healthcare supply chain management, focusing on blood and vaccine supply chains with less attention on the medicine supply chains. This study presents a systematic review of studies applying simulation methods, namely agent-based modelling, discrete event simulation, and system dynamics, to address problems in the medicine supply chain. We adopt the Search, Appraisal, Synthesis, and Analysis (SALSA) approach to collect data from three databases (Scopus, Web of Science, and PubMed) from 2000 to 2023. 320 journal publications qualified for the initial screening and filtration and were extracted for further analysis. Only 31 studies met the inclusion criteria, with the first publication identified in 2010 and the last in 2023. The paper shows the usefulness of applying simulation in identifying medicine supply chain bottlenecks pertaining to stockouts (19%, n <span><math><mo>=</mo></math></span> 6), and falsified or counterfeit (16%, n <span><math><mo>=</mo></math></span> 5). System dynamics was the most applied approach with 42% (n <span><math><mo>=</mo></math></span> 13) and 6% (n <span><math><mo>=</mo></math></span> 2) employing a hybrid simulation approach. 32% (n <span><math><mo>=</mo></math></span> 10) of the studies reported verification and validation at either a conceptual or operational level with insufficient data from the real-world system reported as a challenge. The study suggests a gradually increasing interest in simulation applications in medicine supply chains informing decision-making. Combining multiple simulation approaches is recommended to address complex medicine supply chain issues, such as availability. In order to understand the usefulness of the model in decision-making, more effort is needed to validate developed models.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100555"},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474428","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}

引用次数: 0

An integrated Artificial Intelligence and optimization model for operational efficiency and risk reduction in Letter of Credit examination process

Decision Analytics Journal Pub Date : 2025-02-18 DOI: 10.1016/j.dajour.2025.100552

Mounaf Asaad Khalil, Majed Hadid, Regina Padmanabhan, Adel Elomri, Laoucine Kerbache

{"title":"An integrated Artificial Intelligence and optimization model for operational efficiency and risk reduction in Letter of Credit examination process","authors":"Mounaf Asaad Khalil, Majed Hadid, Regina Padmanabhan, Adel Elomri, Laoucine Kerbache","doi":"10.1016/j.dajour.2025.100552","DOIUrl":"10.1016/j.dajour.2025.100552","url":null,"abstract":"<div><div>Digital transformation in banking has significantly improved efficiency, including in the critical Letter of Credit (LC) examination area. Despite these advancements, LC examination remains complex, labor-intensive, and error-prone, leading to operational risks and inefficiencies. Integrating Artificial Intelligence (AI) offers a promising solution but requires human checkers to verify AI-generated decisions, ensuring accuracy and compliance. Assigning these verification tasks is essential to fully capitalize on AI’s potential, balancing time savings with risk reduction. This paper explores the underexamined challenge of optimizing the hybrid process of AI-assisted LC examination to enhance trade finance. The research aims to minimize examination risk and maximize checker capacity utilization by offering practical strategies for improvement. Through data-driven research collaboration with international banks and FinTech companies and benchmarking relevant literature, an Integer Linear Programming model was developed to assign review tasks for LC documents indexed by AI based on their criticality and discrepancies to human checkers. The model also considers monetary value, checker expertise, and availability factors. Real case studies evaluated improvements over baseline practices, objectives prioritization, trade-off analysis, and varying supply–demand scenarios. The model achieved a 68.3% reduction in operational risks, improving compliance and trustworthiness while minimizing errors and financial losses. Utilization rates ranged from 34% for risk-focused strategies to 73% for efficiency-driven approaches, providing flexibility for resource allocation aligned with organizational priorities. Using the proposed AI-optimization framework and results, the study offers actionable insights for managers and guidance for researchers.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100552"},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438221","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}

引用次数: 0

A comprehensive review of dwarf mongoose optimization algorithm with emerging trends and future research directions

Decision Analytics Journal Pub Date : 2025-02-11 DOI: 10.1016/j.dajour.2025.100551

Olanrewaju Lawrence Abraham , Md Asri Ngadi

引用次数: 0

A hybrid multi-objective optimization approach with NSGA-II for feature selection

Decision Analytics Journal Pub Date : 2025-01-31 DOI: 10.1016/j.dajour.2025.100550

Praveen Vijai, Bagavathi Sivakumar P.

{"title":"A hybrid multi-objective optimization approach with NSGA-II for feature selection","authors":"Praveen Vijai, 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}

引用次数: 0

A novel Full Multiplicative Data Envelopment Analysis Model for solving Multi-Attribute Decision-Making problems

Decision Analytics Journal Pub Date : 2025-01-27 DOI: 10.1016/j.dajour.2025.100549

Narong Wichapa , Atchara Choompol , Ronnachai Sangmuenmao

{"title":"A novel Full Multiplicative Data Envelopment Analysis Model for solving Multi-Attribute Decision-Making problems","authors":"Narong Wichapa , Atchara Choompol , 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}

引用次数: 0

An investigation of supervised machine learning models for predicting drivers’ ethical decisions in autonomous vehicles

Decision Analytics Journal Pub Date : 2025-01-22 DOI: 10.1016/j.dajour.2025.100548

Amandeep Singh, Yovela Murzello, Sushil Pokhrel, Siby Samuel

{"title":"An investigation of supervised machine learning models for predicting drivers’ ethical decisions in autonomous vehicles","authors":"Amandeep Singh, Yovela Murzello, Sushil Pokhrel, 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}

引用次数: 0

An outlier detection framework for Air Quality Index prediction using linear and ensemble models

Decision Analytics Journal Pub Date : 2025-01-16 DOI: 10.1016/j.dajour.2025.100546

Pradeep Kumar Dongre , Viral Patel , Upendra Bhoi , Nilesh N. Maltare

{"title":"An outlier detection framework for Air Quality Index prediction using linear and ensemble models","authors":"Pradeep Kumar Dongre , Viral Patel , Upendra Bhoi , 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}

引用次数: 0

A systematic review of machine learning applications in sustainable supplier selection

Decision Analytics Journal Pub Date : 2025-01-15 DOI: 10.1016/j.dajour.2025.100547

Joachim O. Gidiagba , Lagouge K. Tartibu , Modestus O. Okwu

{"title":"A systematic review of machine learning applications in sustainable supplier selection","authors":"Joachim O. Gidiagba , Lagouge K. Tartibu , 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}

引用次数: 0

A cognitive platform for collecting cyber threat intelligence and real-time detection using cloud computing

Decision Analytics Journal Pub Date : 2025-01-08 DOI: 10.1016/j.dajour.2025.100545

Prasasthy Balasubramanian, Sadaf Nazari, Danial Khosh Kholgh, Alireza Mahmoodi, Justin Seby, Panos Kostakos

{"title":"A cognitive platform for collecting cyber threat intelligence and real-time detection using cloud computing","authors":"Prasasthy Balasubramanian, Sadaf Nazari, Danial Khosh Kholgh, Alireza Mahmoodi, Justin Seby, 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}

引用次数: 0

An integrated multi-stage decision model for upstream supply chain disaster management readiness assessment

Decision Analytics Journal Pub Date : 2025-01-04 DOI: 10.1016/j.dajour.2024.100538

Detcharat Sumrit, Orawan Jongprasittiphol

{"title":"An integrated multi-stage decision model for upstream supply chain disaster management readiness assessment","authors":"Detcharat Sumrit, 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}

引用次数: 0