Decision Analytics Journal最新文献_第2页

A data-driven approach to customer lifetime value prediction using probability and machine learning models 使用概率和机器学习模型进行客户终身价值预测的数据驱动方法

Decision Analytics Journal Pub Date : 2025-07-01 DOI: 10.1016/j.dajour.2025.100601

Albert Wong, Andres Viloria Garcia, Yew-Wei Lim

{"title":"A data-driven approach to customer lifetime value prediction using probability and machine learning models","authors":"Albert Wong, Andres Viloria Garcia, Yew-Wei Lim","doi":"10.1016/j.dajour.2025.100601","DOIUrl":"10.1016/j.dajour.2025.100601","url":null,"abstract":"<div><div>Customer lifetime value is an important marketing metric and has applications in market segmentation, strategy development, and direct marketing programs, especially when customers are not under contract. In this research, we demonstrate the prediction of the lifetime value of patients in a health service portfolio in two separate ways. The probability of a patient being alive and their value in the coming evaluation period are first predicted using a probability model that has been well-established in the marketing community. We then use several machine learning algorithms to perform the same task. The results of these two approaches are compared in terms of accuracy to gain insight into their respective strengths and weaknesses. We believe that the work is one of the first attempts to gain an understanding of the use of machine learning algorithms in this important marketing issue. The results showed that the probability model performs better than the machine learning models, probably due to the assumption required in the probability calculations. It is therefore recommended that an essential step in applying these software approaches is to verify the validity of the key assumption of regularity. In addition, in future studies, consideration should be given to a larger dataset with demographic variables beyond age and gender that were used in this study. Developing specific ML models for dealing with zero-inflated data, which is an inherent feature of customer lifetime data, will also be helpful.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100601"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570849","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}

引用次数: 0

An integrated deep learning approach for predictive vehicle maintenance 预测性车辆维修的集成深度学习方法

Decision Analytics Journal Pub Date : 2025-06-25 DOI: 10.1016/j.dajour.2025.100597

Abderrachid Errezgouny , Youness Chater , Carlos D. Barranco González , Abdeljabbar Cherkaoui

{"title":"An integrated deep learning approach for predictive vehicle maintenance","authors":"Abderrachid Errezgouny , Youness Chater , Carlos D. Barranco González , Abdeljabbar Cherkaoui","doi":"10.1016/j.dajour.2025.100597","DOIUrl":"10.1016/j.dajour.2025.100597","url":null,"abstract":"<div><div>In the automotive sector, vehicle data gathered through On-board Diagnostics (OBD) systems offers continuous insights into vehicle health status and performance. Leveraging this data for predictive maintenance can significantly reduce unplanned failures, enhance safety, and extend vehicle lifespan. This paper proposes a novel hybrid model for Predictive Maintenance (PdM), that integrates Long Short-Term Memory (LSTM) neural networks with K-means clustering to analyze unlabeled time-series data from OBD systems. Our main contribution is to integrate an unsupervised deep learning approach that effectively captures temporal dependencies and clusters operational patterns to predict engine condition with high accuracy, addressing the common challenge of unlabeled vehicle datasets. The model achieves state-of-the-art prediction performance with a 97.5% R<sup>2</sup> score of the selected feature, demonstrating its strong generalization and reliability in different domain applications. Compared to standalone LSTM, Gated Recurrent Units (GRUs) and Recurrent Neural Networks (RNNs) models, our hybrid approach outperforms traditional methods across all tested metrics, marking a significant advancement in predictive maintenance for vehicular systems. This work paves the way for smarter, real-time diagnostics in next-generation vehicles.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100597"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518799","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}

引用次数: 0

An integrated analytics model for supplier selection and order allocation with machine learning and multi-criteria optimization 基于机器学习和多准则优化的供应商选择和订单分配集成分析模型

Decision Analytics Journal Pub Date : 2025-06-25 DOI: 10.1016/j.dajour.2025.100599

Enty Nur Hayati, Wakhid Ahmad Jauhari, Retno Wulan Damayanti, Cucuk Nur Rosyidi

{"title":"An integrated analytics model for supplier selection and order allocation with machine learning and multi-criteria optimization","authors":"Enty Nur Hayati, Wakhid Ahmad Jauhari, Retno Wulan Damayanti, Cucuk Nur Rosyidi","doi":"10.1016/j.dajour.2025.100599","DOIUrl":"10.1016/j.dajour.2025.100599","url":null,"abstract":"<div><div>Sustainable Supplier Selection and Order Allocation (SSSOA) are critical strategic decisions in supply chain management. The decision-making process becomes complex under uncertainty, especially in a multi-supplier, multi-item, and multi-period environment. This study proposes a four-stage framework to address the SSSOA planning problem. In the first stage, machine learning techniques with the Autoregressive Integrated Moving Average (ARIMA) method are used to determine future product demand. In the second stage, Life Cycle Analysis (LCA) is used to determine the environmental impact of purchased drugs. In the third stage, a fuzzy supplier evaluation model based on the Best-Worst Method (BWM)-Additive Ratio Assessment (ARAS) method is used to determine supplier scores. Finally, a fuzzy probabilistic multi-objective mixed integer linear programming model is developed to determine the optimal drug order. This model aims to minimize the total purchase cost, probabilistic defects, and environmental impacts and maximize the total purchase value of the order allocation. The weighted sum method is used to solve the model. The application of the proposed framework is tested using a real dataset from a teaching hospital in Surakarta, Indonesia. The results show that this model can minimize the purchasing cost by 168.11 million Indonesian Rupiah (IDR), optimizing the total allocation value by 6,519.731 units. Sensitivity analysis of parameters such as holding cost, <span><math><mi>α</mi></math></span> value, and supplier capacity reveals that significant changes in these parameters substantially affect the total purchasing cost and order allocation. The implications of this study include improving planning accuracy, reducing environmental impacts, and optimizing supplier selection amid uncertainty, with potential applications in various other industrial sectors.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100599"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548975","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}

引用次数: 0

An analytical review of the grasshopper optimization method for multi-objective decision-making 多目标决策中蚱蜢优化方法的分析综述

Decision Analytics Journal Pub Date : 2025-06-25 DOI: 10.1016/j.dajour.2025.100598

Mohammed A. El-Shorbagy , Anas Bouaouda , Laith Abualigah , Fatma A. Hashim

{"title":"An analytical review of the grasshopper optimization method for multi-objective decision-making","authors":"Mohammed A. El-Shorbagy , Anas Bouaouda , Laith Abualigah , Fatma A. Hashim","doi":"10.1016/j.dajour.2025.100598","DOIUrl":"10.1016/j.dajour.2025.100598","url":null,"abstract":"<div><div>Multi-objective optimization problems (MOPs) are common in real-world applications, including scheduling, vehicle routing, and engineering design. A key challenge in solving MOPs is balancing convergence and diversity, as these problems often involve conflicting objectives and complex constraints. To address this, researchers have developed numerous multi-objective optimization algorithms, among them the Multi-Objective Grasshopper Optimization Algorithm (MOGOA). MOGOA utilizes an external archive to store Pareto-optimal solutions and employs a roulette wheel selection mechanism to guide global optimization, effectively directing the evolution of the grasshopper population toward diverse and high-quality solutions. Since its introduction by Mirjalili et al. in 2018, MOGOA has attracted significant attention from researchers and has been widely applied to address various MOPs across diverse domains. This review paper examines key research publications utilizing MOGOA. First, an overview of MOGOA is provided, detailing its bio-inspired foundation and optimization framework. The core operations of MOGOA are explained step-by-step, and its theoretical basis is outlined. Reviewed studies are categorized into three groups based on their adaptation approach: standard, modified, and hybridized implementations. The primary applications of MOGOA are comprehensively explored. Next, a critical evaluation of MOGOA’s performance is presented, comparing its effectiveness against recent multi-objective algorithms using the CEC2009 benchmark test suite. Additionally, an in-depth analysis of MOGOA’s strengths, weaknesses, and key research gaps is provided. Finally, the paper concludes with insights and potential future research directions for MOGOA. This review offers a comprehensive analysis of MOGOA’s performance and applications, contributing to the broader field of MOPs.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100598"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513998","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}

引用次数: 0

An analytics-based framework for optimizing resource allocation and preemptive scheduling in manufacturing 基于分析的制造业资源分配和优先调度优化框架

Decision Analytics Journal Pub Date : 2025-06-18 DOI: 10.1016/j.dajour.2025.100596

Urtzi Otamendi , Iñigo Martinez , Xabier Belaunzaran , Arkaitz Artetxe , Javier Franco , Alejandro Uribe , Igor G. Olaizola , Basilio Sierra

{"title":"An analytics-based framework for optimizing resource allocation and preemptive scheduling in manufacturing","authors":"Urtzi Otamendi , Iñigo Martinez , Xabier Belaunzaran , Arkaitz Artetxe , Javier Franco , Alejandro Uribe , Igor G. Olaizola , Basilio Sierra","doi":"10.1016/j.dajour.2025.100596","DOIUrl":"10.1016/j.dajour.2025.100596","url":null,"abstract":"<div><div>Production scheduling is critical in manufacturing operations, requiring the optimal assignment of limited resources. This paper introduces a novel generalization of the Unrelated Parallel Machine (UPM) problem, addressing key real-world complexities: sequence- and machine-dependent setup times, resource assignment constraints, and preemptive scheduling. These extensions, particularly workforce assignments where specific qualifications and availability schedules determine employee eligibility, represent a significant step forward in industrial scheduling research. A Mixed Integer Linear Programming (MILP) model and three constraint-specific variations were developed to evaluate performance and scalability and isolate preemption and resource constraints. Extensive computational experiments demonstrated a trade-off between model applicability and computational efficiency. The proposed model achieved realistic job distribution across machines but encountered scalability challenges due to the combinatorial complexity introduced by what we term dense eligibility matrices, representing a high proportion of potential employee-machine assignments. The preemption-only model optimized makespan effectively, while the resource-focused model provided more practical solutions at the cost of higher processing times. The baseline UPM with sequence-dependent setup times (UPMS) model exhibited computational efficiency but lacked applicability to dynamic industrial environments. This study highlights the impact of preemption and resource assignment on scheduling optimization and underscores the importance of sparsity reduction techniques to enhance scalability. By bridging gaps in workforce management and operational flexibility, the proposed framework provides a robust foundation for addressing complex industrial scheduling challenges.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100596"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322470","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}

引用次数: 0

A robust support vector machine approach for Raman data classification 拉曼数据分类的鲁棒支持向量机方法

Decision Analytics Journal Pub Date : 2025-06-18 DOI: 10.1016/j.dajour.2025.100595

Marco Piazza , Andrea Spinelli , Francesca Maggioni , Marzia Bedoni , Enza Messina

{"title":"A robust support vector machine approach for Raman data classification","authors":"Marco Piazza , Andrea Spinelli , Francesca Maggioni , Marzia Bedoni , Enza Messina","doi":"10.1016/j.dajour.2025.100595","DOIUrl":"10.1016/j.dajour.2025.100595","url":null,"abstract":"<div><div>Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, <em>Raman spectroscopy</em> analysis of biological samples has been successfully applied for early-stage diagnosis. However, spectra’s inherent complexity and variability make the manual analysis challenging, even for domain experts. For the same reason, the use of traditional <em>Statistical Learning</em> and <em>Machine Learning</em> techniques could not guarantee for accurate and reliable results. Machine learning models, combined with robust optimization techniques, offer the possibility to improve the classification accuracy and enhance the resilience of predictive models under data uncertainty. In this paper, we investigate the performance of a novel robust formulation for <em>Support Vector Machine</em> (SVM) in classifying COVID-19 samples obtained from Raman spectroscopy. Given the noisy and perturbed nature of biological samples, we protect the classification process against uncertainty through the application of robust optimization techniques. Specifically, we consider the robust counterparts of deterministic SVM formulations using bounded-by-norm uncertainty sets. We explore the cases of both linear and kernel-induced classifiers, addressing binary and multiclass classification tasks. The effectiveness of our approach is evaluated on real-world COVID-19 Raman saliva samples provided by Italian hospitals. We assess the performance of the proposed method by comparing the results of our numerical experiments with those of a state-of-the-art classifier, showing the potential of robust classifiers in handling uncertain Raman data.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100595"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335640","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}

引用次数: 0

A review of strategies, challenges, and ethical implications of machine learning in smart manufacturing 回顾智能制造中机器学习的策略、挑战和伦理影响

Decision Analytics Journal Pub Date : 2025-06-13 DOI: 10.1016/j.dajour.2025.100591

Yassmin Seid Ahmed , Abbas S. Milani

{"title":"A review of strategies, challenges, and ethical implications of machine learning in smart manufacturing","authors":"Yassmin Seid Ahmed , Abbas S. Milani","doi":"10.1016/j.dajour.2025.100591","DOIUrl":"10.1016/j.dajour.2025.100591","url":null,"abstract":"<div><div>Manufacturing organizations continuously need to innovative production strategies and advance their machinery to adapt to evolving business objectives. Machine learning and data mining are now essential techniques for solving various complex manufacturing problems promptly and intelligently. This article reviews recent research from multiple sectors that have employed machine learning to develop intelligent manufacturing processes, while highlighting key challenges and areas that have been partly overlooked. Over the last two decades, scholars have developed numerous AI-based algorithms and approaches to improve manufacturing processes outputs, with scheduling, monitoring, quality, and fault detection being among the main focus areas. The review categorizes smart manufacturing problems into clustering, classification, and regression tasks, and discusses the underlying performance metrics associated with each category. Additionally, the study tackles ethical issues by discussing such important considerations as data privacy, transparency, and fairness in industrial machine-learning implementations. Finally, it emphasizes that many users remain concerned about compliance with global data protection legislations and the need to build trust in autonomous decision-making systems.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100591"},"PeriodicalIF":0.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522522","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}

引用次数: 0

A hybrid approach using deep clustering and Lagrangian relaxation for sustainable waste logistics 基于深度聚类和拉格朗日松弛的可持续废物物流混合方法

Decision Analytics Journal Pub Date : 2025-06-10 DOI: 10.1016/j.dajour.2025.100590

Teena Thomas , Chandrasekharan Rajendran , Hans Ziegler , Sumit Saxena

{"title":"A hybrid approach using deep clustering and Lagrangian relaxation for sustainable waste logistics","authors":"Teena Thomas , Chandrasekharan Rajendran , Hans Ziegler , Sumit Saxena","doi":"10.1016/j.dajour.2025.100590","DOIUrl":"10.1016/j.dajour.2025.100590","url":null,"abstract":"<div><div>Optimizing solid waste management (SWM) is essential for ensuring a sustainable and healthy environment in a city. This study considers a two-echelon solid waste logistics system (2E-SWLS) in a metropolitan city with a fleet of capacitated heterogeneous vehicles. The problem consists of waste collection sites, transfer stations acting as intermediate facilities and dumping yards. The objective is to identify the best locations for transfer stations and optimize the logistics system by minimizing total cost. The problem is formulated as a Mixed Integer Linear Programming (MILP) model. To address large-scale city network complexities, we propose a Cluster-Fix-Optimize Matheuristic (C-F-OM), as the MILP model fails to provide a solution within the given CPU time. This method involves a deep learning-based clustering of sites, determining the transfer station location within each cluster and optimizing the associated operational and logistic decisions while serving as a benchmark solution to the problem. Additionally, we introduce a Lagrangian Relaxation-Fix-Optimize Matheuristic (LR-F-OM) to determine a lower bound for 2E-SWLS. The effectiveness of this lower bound is compared with that of the conventional subgradient method. The upper bound derived from LR-F-OM outperforms the C-F-OM solution and promises significant savings of approximately 50%, when compared to the existing solution approaches in a case study in India by providing insights on facility and logistical configurations for improving the operational efficiency. The study also provides managerial insights on factors such as vehicle fleet heterogeneity, transfer station capacity, demand variations at waste collection sites, and vehicle operational costs on total cost.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100590"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306879","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}

引用次数: 0

A structured review of large language models in metaheuristic optimisation 元启发式优化中大型语言模型的结构化回顾

Decision Analytics Journal Pub Date : 2025-06-01 DOI: 10.1016/j.dajour.2025.100587

Reza Ghanbarzadeh , Seyedali Mirjalili

{"title":"A structured review of large language models in metaheuristic optimisation","authors":"Reza Ghanbarzadeh , Seyedali Mirjalili","doi":"10.1016/j.dajour.2025.100587","DOIUrl":"10.1016/j.dajour.2025.100587","url":null,"abstract":"<div><div>Metaheuristics are widely used to address complex optimisation problems where traditional exact methods are computationally infeasible or insufficiently flexible. With the rapid advancement of artificial intelligence, large language models, such as ChatGPT, Claude, Gemini, and LLaMA, have emerged as powerful tools capable of enhancing, automating, and adapting various stages of the optimisation process. This systematic literature review investigates the evolving role of large language models in metaheuristic optimisation, with a focus on algorithm generation, parameter tuning, hybridisation, constraint handling, and multi-objective optimisation. Following PRISMA guidelines, 25 studies from nine major scientific databases were selected and analysed. Through thematic analysis, a novel role-based taxonomy was developed that categorises large language model contributions into four functional roles: Advisor, Refiner, Enhancer, and Innovator. The findings demonstrate that large language models support the automation of metaheuristic workflows, enable dynamic adaptation, and contribute to the creation of novel heuristic strategies. Despite these advantages, the review also identifies persistent limitations, including prompt sensitivity, computational overhead, and scalability challenges. These issues highlight the need for more robust evaluation frameworks and benchmarking practices. This review offers a comprehensive synthesis of the current landscape, highlights research gaps, and provides actionable insights for researchers and practitioners aiming to integrate large language models into advanced optimisation systems across domains such as engineering, logistics, and computational design.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100587"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195940","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}

引用次数: 0

A novel fractional order model for analyzing counterterrorism operations and mitigating extremism 一种分析反恐行动和减轻极端主义的新型分数阶模型

Decision Analytics Journal Pub Date : 2025-06-01 DOI: 10.1016/j.dajour.2025.100589

Mutaz Mohammad , Isa Abdullahi Baba , Evren Hincal , Fathalla A. Rihan

{"title":"A novel fractional order model for analyzing counterterrorism operations and mitigating extremism","authors":"Mutaz Mohammad , Isa Abdullahi Baba , Evren Hincal , Fathalla A. Rihan","doi":"10.1016/j.dajour.2025.100589","DOIUrl":"10.1016/j.dajour.2025.100589","url":null,"abstract":"<div><div>This study examines the profound impact of terrorism on individuals and society by developing a fractional-order mathematical model to analyze and enhance counterterrorism efforts. The model accounts for the persistent and complex nature of extremist behavior, particularly emphasizing the importance of preventing violent extremism before it escalates into terrorism. Real-world data on terrorist activities in Nigeria – specifically from the Boko Haram insurgency – was used to calibrate and validate the model, ensuring its relevance and accuracy. The model reveals that the basic reproduction number (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>) plays a decisive role in determining the long-term success of counterterrorism strategies. Numerical simulations show that terrorist activities decline when <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub><mo><</mo><mn>1</mn></mrow></math></span>, while they persist or escalate when <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>></mo><mn>1</mn></mrow></math></span>. A comprehensive sensitivity analysis further identifies the most influential parameters affecting <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, providing actionable insights into where interventions can be most effective. Parameters related to recruitment, ideological spread, and counter-radicalization efforts were found to have the highest impact. The study concludes by offering strategic recommendations informed by the simulation and sensitivity results, aiming to support the design of more targeted and sustainable counterterrorism policies.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100589"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254839","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}

引用次数: 0