Decision Analytics Journal最新文献

{"title":"An analytical framework for prioritizing barriers and public–private partnership solutions in civil helicopter emergency medical services","authors":"Detcharat Sumrit,&nbsp;Sawinee Maneelok","doi":"10.1016/j.dajour.2025.100617","DOIUrl":"10.1016/j.dajour.2025.100617","url":null,"abstract":"<div><div>Civil Helicopter Emergency Medical Services (CHEMS) are vital for improving emergency healthcare access, particularly in remote or congested areas. While widely implemented in high-income countries, CHEMS remain underutilized in low- and middle-income countries (LMICs) due to financial, regulatory, and logistical barriers. In response, public–private partnership (PPP) models have gained attention as a viable mechanism to overcome these barriers. However, the absence of context-specific decision-support frameworks hampers effective implementation. This study introduces a comprehensive multi-criteria decision-making (MCDM) framework to facilitate the deployment of CHEMS through PPPs in LMICs, with Thailand serving as a case study. The proposed framework integrates several methodologies: the Delphi method to identify critical factors; Level-Based Weight Assessment (LBWA) for eliciting subjective expert weights; Integrated Determination of Objective Criteria Weights (IDOCRIW) for objective weighting; and the Compromise Ranking of Alternatives based on Distance to Ideal Solution (CRADIS) for prioritizing strategic options. To accommodate uncertainty and vagueness in expert judgment, the framework employs the interval-valued <em>q</em>-rung orthopair fuzzy set (IV<em>q</em>-ROFS) approach. The analysis identifies eight major barriers and seven potential PPP collaboration models. The most critical obstacles include flight safety concerns, lack of regulatory support, and inadequate coordination with ground-based emergency services. The top-ranked PPP strategies emphasize regulatory streamlining, integrated emergency response systems, and co-financing mechanisms. These findings provide actionable insights for policymakers and EMS stakeholders, enabling more informed and context-sensitive decision-making. Furthermore, the proposed framework is adaptable to other EMS contexts in LMICs, contributing to the broader discourse on emergency healthcare delivery and public–private collaboration.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100617"},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PharmChain: A data-driven scenario-based drug traceability and regulation blockchain framework","authors":"Amrendra Singh Yadav ,&nbsp;Vincent Charles ,&nbsp;Tatiana Gherman ,&nbsp;Vinay Kumar ,&nbsp;Vijayant Pawar ,&nbsp;Nishchay Chaudhary","doi":"10.1016/j.dajour.2025.100620","DOIUrl":"10.1016/j.dajour.2025.100620","url":null,"abstract":"<div><div>The manufacturing and distribution of counterfeit tablets, especially in developing countries, is an urgent and increasingly critical global problem. Falsified medicinal products may contain incorrect ingredients and doses. One of the reasons for drug counterfeiting is the imperfect supply chain system in the pharmaceutical industry. Medicinal products are moved between manufacturers, suppliers, wholesalers, retailers, and pharmaceutical firms before meeting consumers. This study proposes PharmChain, a scenario-oriented drug traceability and regulation blockchain framework that reconstructs the entire service infrastructure by splitting the service provider into three separate service components and ensuring the authenticity and privacy of traceability details. PharmChain can track medication development via patient supply in the pharmaceutical industry. An Ethereum-based blockchain stores the transactions, and only trusted parties can access the data through the chain. We create and test our smart contract code in the Remix environment. We present detailed cost and security analyses incurred by supply chain stakeholders. We also use cost analysis to assess the performance of the proposed solution and demonstrate its affordability.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"17 ","pages":"Article 100620"},"PeriodicalIF":0.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A golden eagle-based hybrid deep learning model for automobile insurance fraud detection","authors":"Kavikumar Jacob ,&nbsp;Shubanath Thejani binti Mohammed Sayeed Shafaraz ,&nbsp;D. Nagarajan","doi":"10.1016/j.dajour.2025.100619","DOIUrl":"10.1016/j.dajour.2025.100619","url":null,"abstract":"<div><div>Insurance fraud detection is a significant problem in the insurance industry, producing immeasurable losses. Conventional insurance fraud detection models depend heavily on experts’ knowledge, and accurately estimating fraud when the data and the claim data are enormous is a complex and difficult task. This study proposes an efficient and effective Automobile Insurance Claim Fraud Detection (AICFD) approach. The feature selection process in the proposed approach uses Golden Eagle-Assisted Optimisation (GEAO) to efficiently select the subset of features. The obtained features are utilised for fraud detection using the deep learning model of hybrid Bidirectional Encoder Representation-Long Short-Term Memory (BERT-LSTM). The experimental analysis using the carclaim.txt dataset achieved better accuracy and recall of 99.02% and 99.1%.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100619"},"PeriodicalIF":0.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A stability and robustness analysis of multi-criteria decision methods in logistics","authors":"Parul Jangid ,&nbsp;Tarun Kumar ,&nbsp;Jahnvi ,&nbsp;Kailash Dhanuk ,&nbsp;M.K. Sharma","doi":"10.1016/j.dajour.2025.100618","DOIUrl":"10.1016/j.dajour.2025.100618","url":null,"abstract":"<div><div>Multi-Criteria Decision-Making (MCDM) methods, particularly TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) (Multicriteria Optimization and Compromise Solution), are extensively used in logistics for selecting optimal transportation routes involving multiple conflicting criteria. However, their ranking stability under uncertain input conditions remains a significant challenge. In this study, Monte Carlo Simulation (MCS) is applied to evaluate the ranking stability of TOPSIS and VIKOR across varying uncertainty levels and simulation runs. Probabilistic decision matrices based on normal distributions are generated to assess the effectiveness of Particle Swarm Optimization (PSO)-based weight optimization versus equal weighting. Results indicate that TOPSIS consistently provides more stable rankings compared to VIKOR, particularly when integrated with PSO-optimized weights, highlighting VIKOR’s greater sensitivity to input variability. Consequently, TOPSIS is recommended for logistics decision-making scenarios characterized by uncertain data. This research contributes a robust methodological framework to evaluate and enhance the reliability of MCDM methods under real-world uncertainties.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100618"},"PeriodicalIF":0.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning framework for optimizing personalized online course recommendation and selection","authors":"Khaoula Mrhar,&nbsp;Mounia Abik","doi":"10.1016/j.dajour.2025.100616","DOIUrl":"10.1016/j.dajour.2025.100616","url":null,"abstract":"<div><div>Massive Open Online Courses (MOOCs) and the broad adoption of distance learning over the past few years have caused a remarkable shift in the educational landscape. However, the vast majority of available MOOCs often challenge learners in selecting courses that align with their academic goals, leading to high dropout rates. This paper presents an unexploited opportunity for formal educational institutions to integrate MOOCs into their curricula by guiding and supporting learners on these platforms. It introduces a Deep Semantic MOOC Recommender System (DSMRS) designed to help learners choose MOOCs aligned with their formal curriculum. The system utilizes advanced Natural Language Processing (NLP) techniques to deliver personalized recommendations to learners. It employs a top-N recommender algorithm and leverages three key strategies: (a) an optimized Explicit Semantic Analysis (ESA) to measure semantic similarity between course descriptions and learning objectives; (b) Sentiment Analysis, using a Bayesian Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to analyze learner reviews of MOOCs; and (c) Classification of Recommended MOOCs based on Bloom’s Taxonomy, categorizing MOOCs according to cognitive complexity. The results highlight that experimentation conducted with the system demonstrates promising performance.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100616"},"PeriodicalIF":0.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Intuitionistic fuzzy cognitive mapping approach for blockchain-driven decision support in sustainable construction supply chains","authors":"Seyed Pendar Toufighi ,&nbsp;Amir Mohammad Norouzzadeh ,&nbsp;Jan Vang ,&nbsp;Esmaeil Sabzikaran","doi":"10.1016/j.dajour.2025.100615","DOIUrl":"10.1016/j.dajour.2025.100615","url":null,"abstract":"<div><div>This study examines how blockchain technology (BT) enhances sustainability and resilience in construction supply chains (CSCs). Recognizing the sector’s vulnerability to disruptions and inefficiencies, the research aims to identify key blockchain facilitators (BTFs) that enhance environmental, economic, and risk-related performance metrics. Using an integrated multi-criteria decision-making methodology based on intuitionistic fuzzy cognitive maps and an active Hebbian learning algorithm, causal relationships between BTFs and sustainability indicators are modeled and analyzed. Data is collected from ten experts through structured interviews and surveys, ensuring methodological rigor via sensitivity and consistency analyses. Results reveal that transparency, traceability, and smart contracts are the most influential facilitators, significantly improving environmental impact, stakeholder collaboration, and economic efficiency within CSCs. Decentralized and secure databases also emerged as critical enablers of resilience and cost reduction. Simulation results demonstrate that full-scale adoption of BT can improve sustainability performance by up to 35%, whereas partial adoption yields limited benefits. The study presents a novel decision-support framework for prioritizing BT facilitators, offering actionable insights for managers, policymakers, and construction firms seeking digital transformation and sustainable development. Overall, this research advances the understanding of BT’s strategic role in construction supply chains and offers a practical roadmap for its effective implementation.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100615"},"PeriodicalIF":0.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated oversampling and noise reduction method for robust predictive analytics","authors":"Jeong-Wook Lee ,&nbsp;Young Eun Jeon ,&nbsp;Jung-In Seo","doi":"10.1016/j.dajour.2025.100612","DOIUrl":"10.1016/j.dajour.2025.100612","url":null,"abstract":"<div><div>Imbalanced data is often encountered in scenarios where rare but critical events occur much less frequently than others, and it is particularly prominent in fields such as disease diagnosis, fraud detection, and risk management. The main problem with imbalanced data is that predictive models using machine learning algorithms are likely to become biased toward the majority class. For example, the models may have high overall accuracy but perform poorly in correctly identifying the minority class data points. In this situation, if our interest is the minority class, the models may lead to serious misclassifications, which impairs the reliability and validity of the predictions. In response to this issue, this study develops a resampling strategy integrated with random over-sampling examples and Tomek link. The developed resampling strategy increases data diversity by generating synthetic data points based on a probability distribution while eliminating noisy and overlapping data points, resulting in a higher-quality dataset. For illustrative purposes, a stroke dataset with a serious imbalance ratio of 98:2 is employed. To evaluate the performance of our resampling strategy and demonstrate its applicability, we apply a wide range of machine learning and deep learning models, including support vector machine, elastic net, random forest, extreme gradient boosting, and deep and convolutional neural networks. The outcomes of this study suggest that the developed resampling strategy can be effectively applied to other medical datasets with severe class imbalances and can enable more reliable and efficient predictive modeling in critical healthcare applications.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100612"},"PeriodicalIF":0.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable machine learning framework for explaining company valuation","authors":"Luís Baltazar Blanquet ,&nbsp;Miguel Alves Pereira ,&nbsp;Stefan Petrov","doi":"10.1016/j.dajour.2025.100611","DOIUrl":"10.1016/j.dajour.2025.100611","url":null,"abstract":"<div><div>Valuing private enterprises — particularly early-stage firms — remains a significant challenge due to high variability and frequent overvaluation, often exceeding 50%. These inaccuracies undermine trust in the venture capital (VC) market and complicate investment decision-making. To address these issues, we present machine learning (ML) frameworks that enhance valuation accuracy. Our study introduces a novel ML approach that outperforms traditional models, achieving a Mean Absolute Percentage Error of 34.90%, significantly improving upon industry benchmarks. The Total Cost of Ownership is identified as the most critical valuation methodology, with financial metrics becoming increasingly influential as firm value rises. Our findings highlight the potential of advanced data mining techniques to deliver more reliable, comprehensive valuations within the VC landscape. Future research should explore expanding this analysis to emerging markets and incorporating primary data for deeper insights.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100611"},"PeriodicalIF":0.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-analytics framework for optimizing user-centered virtual reality training","authors":"Abdallah Al-Hamad ,&nbsp;Attila Gilányi","doi":"10.1016/j.dajour.2025.100610","DOIUrl":"10.1016/j.dajour.2025.100610","url":null,"abstract":"<div><div>Safety training in high-risk industries often lacks user-centric design, leading to ineffective learning outcomes. This study presents a novel framework to optimize Virtual Reality (VR) safety training by integrating two decision-making methods to align user needs with technical design. The research addresses the problem of inadequate training efficacy by prioritizing user requirements and mapping them to technical solutions. A four-phase methodology identifies user requirements through expert consensus, prioritizes them using the Analytic Hierarchy Process (AHP), determines technical measures, and aligns them with user needs via Quality Function Deployment (QFD). SMART-FAST-CLEAR framework and consistency check used to validate expert agreement, though empirical user testing is recommended for future work. Results highlight VR’s superiority over augmented reality and computer-based training, emphasizing enhanced learning effectiveness and immersion without relying on complex numerical metrics. This framework offers a replicable model for designing effective, user-focused VR safety training systems, contributing to improved safety practices in high-risk environments.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100610"},"PeriodicalIF":0.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust optimization method for hybrid flow shop scheduling with uncertain setup times","authors":"Shiva Rahmativala ,&nbsp;Javid Ghahremani","doi":"10.1016/j.dajour.2025.100609","DOIUrl":"10.1016/j.dajour.2025.100609","url":null,"abstract":"<div><div>This research focuses on modeling and solving a bi-objective hybrid flow shop scheduling problem, considering uncertain job-dependent setup times and worker constraints. The main objective of the mathematical model is to simultaneously minimize the Maximum Completion Time (Cmax) and minimize the total tardiness. To achieve both objective functions simultaneously, various decisions are made, including scheduling the processing of jobs in each stage, assigning jobs to machines, and assigning workers in each stage. Uncertainty in the job-dependent setup time leads to the use of a robust-box optimization method to control this parameter. In addition, this paper proposes four algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi Objective Particle Swarm Optimization (MOPSO), Multi-Objective Grey Wolf Optimizer (MOGWO), and Multi-Objective Flow Direction Algorithm (MOFDA), to solve the problem. The results of solving the problem on 15 sample problems show that decreasing the total tardiness value increases the Cmax value. This is due to changes in the scheduling of job processing by machines and workers in different stages. Also, by comparing the efficient solutions obtained from different algorithms and examining the indices, it was observed that the MOFDA has higher efficiency in obtaining the Number of Pareto Front (NPF) and Maximum spread Index (MSI) indices. However, the Computational time (CPT) and Space Metric (SM) indices in this algorithm was higher compared to other algorithms. Also, the quality of the solutions obtained from this algorithm was higher compared to other algorithms and was selected as the more efficient algorithm. By examining the uncertainty rate in the job-dependent setup time analysis, it was observed that increasing this rate increases the Cmax and total tardiness values. So that, increasing the uncertainty rate from 0.5 to 0.9 leads to an 8.36% increase in the Cmax value and a 15.81% increase in the total tardiness value. The results of this model can help managers in appropriate scheduling of job processing in production units.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100609"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}