Decision Analytics Journal最新文献

A resource-constrained optimization model for parallel machine scheduling with constraint programming 基于约束规划的并行机器调度资源约束优化模型

Decision Analytics Journal Pub Date : 2025-05-22 DOI: 10.1016/j.dajour.2025.100585

Mohamed Amine Abdeljaouad , Zied Bahroun , Nour El Houda Saadani , Rahaf Sheiko , Karam Al-Assaf

{"title":"A resource-constrained optimization model for parallel machine scheduling with constraint programming","authors":"Mohamed Amine Abdeljaouad , Zied Bahroun , Nour El Houda Saadani , Rahaf Sheiko , Karam Al-Assaf","doi":"10.1016/j.dajour.2025.100585","DOIUrl":"10.1016/j.dajour.2025.100585","url":null,"abstract":"<div><div>This study investigates an NP-hard parallel machine scheduling problem, a critical challenge in manufacturing, healthcare, and logistics industries where efficient resource allocation is essential. The issue involves scheduling operations where each task requires an additional resource, with multiple resource types available, each limited to a single copy. The objective is to minimize the makespan, which is defined as the total completion time of all tasks. A novel constraint programming model is designed to solve the problem to optimality. The proposed model is benchmarked against two existing linear mathematical formulations, achieving up to 95% faster computational times while solving instances with up to 20 machines, 40 resources, and 90 operations per resource—scenarios the linear models failed to handle within reasonable computational limits. Furthermore, the model exhibits excellent scalability, effectively solving more extensive and complex instances. These findings underscore the potential of constraint programming as a powerful tool for tackling complex scheduling problems in resource-constrained environments, with applications in industries where resource-sharing is critical.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100585"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138725","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 optimization-based approach to fleet reliability and allocation in open-pit mining 基于优化的露天矿机队可靠性与配置方法

Decision Analytics Journal Pub Date : 2025-05-08 DOI: 10.1016/j.dajour.2025.100583

Sena Senses, Mustafa Kumral

{"title":"An optimization-based approach to fleet reliability and allocation in open-pit mining","authors":"Sena Senses, Mustafa Kumral","doi":"10.1016/j.dajour.2025.100583","DOIUrl":"10.1016/j.dajour.2025.100583","url":null,"abstract":"<div><div>Open-pit mining operations depend heavily on the availability and reliability of complex equipment fleets, where the failure of one component can disrupt overall productivity. This study proposes two complementary optimization models to enhance fleet allocation and reliability in the mining industry. The first model — a Mixed-Integer Nonlinear Programming (MINLP) formulation — supports short-term planning by maximizing the minimum reliability of heterogeneous truck–shovel sub-systems under production and utilization constraints. The second model focuses on medium-term reliability enhancement, allocating targeted reliability improvements to critical components based on equipment degradation and operational history. Both models are validated using real operational data from an open pit mine, which includes failure and repair time datasets from 17 trucks and 2 hydraulic shovels. Reliability curves are estimated using the power law model under a Non-Homogeneous Poisson Process (NHPP) assumption. Results show that optimal allocation can achieve production targets of 4,489 tons per hour with a minimum sub-system reliability of 0.7. Furthermore, reliability improvements tailored to engine-hour-based cost functions can effectively restore operational performance over a one-week horizon. This research bridges the gap between fleet allocation and reliability allocation and introduces a novel framework for integrating reliability into equipment planning. The models provide actionable insights for mining operations to optimize equipment deployment, reduce failure risk, and support more resilient and cost-effective planning.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100583"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928966","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 systematic review of sentiment analytics in banking headlines 对银行业头条新闻中情绪分析的系统回顾

Decision Analytics Journal Pub Date : 2025-05-07 DOI: 10.1016/j.dajour.2025.100584

Muhunthan Jayanthakumaran , Nagesh Shukla , Biswajeet Pradhan , Ghassan Beydoun

{"title":"A systematic review of sentiment analytics in banking headlines","authors":"Muhunthan Jayanthakumaran , Nagesh Shukla , Biswajeet Pradhan , Ghassan Beydoun","doi":"10.1016/j.dajour.2025.100584","DOIUrl":"10.1016/j.dajour.2025.100584","url":null,"abstract":"<div><div>This systematic review investigates sentiment analysis of news headlines in the banking sector, a field susceptible to public sentiment, as demonstrated by phenomena like bank runs leading to rapid deposit withdrawals. We trace the evolution of analytic methods from traditional machine learning to advanced deep learning models, notably Bidirectional Encoder Representations from Transformer (BERT) and Generative Pre-trained Transformer (GPT). Our study highlights their applications including headline generation, sentiment measurement, fake news detection, and analysis of political bias. Despite significant advancements, we uncover research gaps, such as the ineffective use of these methodologies in banking analysis, the underuse of GPT, and a focus on performance rather than practical application. Looking ahead, we note the increasing significance of Large Language Model (LLM), the untapped potential of headline analysis in banking, and the growing interest in this area spurred by rapid technological advancements. Our findings emphasise the pivotal role of sentiment analysis in deciphering market trends and improving decision making in finance, underscoring its strategic importance in the banking industry.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100584"},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936171","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 data-driven risk assessment of cybersecurity challenges posed by generative AI 生成式人工智能带来的网络安全挑战的数据驱动风险评估

Decision Analytics Journal Pub Date : 2025-05-02 DOI: 10.1016/j.dajour.2025.100580

Rami Mohawesh , Mohammad Ashraf Ottom , Haythem Bany Salameh

引用次数: 0

A dual-phase framework for detecting authentic and computer-generated customer reviews using large language models 使用大型语言模型检测真实的和计算机生成的客户评论的双阶段框架

Decision Analytics Journal Pub Date : 2025-05-02 DOI: 10.1016/j.dajour.2025.100581

Dina Nawara , Rasha Kashef

{"title":"A dual-phase framework for detecting authentic and computer-generated customer reviews using large language models","authors":"Dina Nawara , Rasha Kashef","doi":"10.1016/j.dajour.2025.100581","DOIUrl":"10.1016/j.dajour.2025.100581","url":null,"abstract":"<div><div>Customer reviews are crucial in potential buyers’ decision-making process. However, on online platforms, the credibility of these reviews is often undermined by fake reviews, which can mislead users. With advancements in large language models (LLMs), the review landscape has transformed, making it more common to encounter computer-generated reviews created using state-of-the-art language models rather than genuine user feedback. This evolution poses significant challenges in distinguishing authentic reviews from artificially generated ones. To address these challenges, we propose a novel dual-phase framework that first generates high-diversity synthetic reviews using advanced LLMs to learn their patterns, and then it leverages this knowledge to enhance fake reviews detection. Our methodology involves two key phases. In the first phase, we generate computer-generated reviews by leveraging advanced methods, including generative transformers, trained on genuine user reviews. In the second phase; traditional and deep learning based classifiers, are incorporated as detection models which classify reviews as either authentic or computer-generated. Evaluated on a benchmark Amazon review dataset, our framework demonstrate (1) the efficacy of our approach in generating diverse and contextually relevant human-based and computerized-based reviews and (2) the robustness of our system in classifying and verifying the authenticity of reviews.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100581"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912894","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 lift adjustment methodology for improving association rule interpretation 一种改进关联规则解释的升力调整方法

Decision Analytics Journal Pub Date : 2025-04-30 DOI: 10.1016/j.dajour.2025.100582

Bilal Sowan , Li Zhang , Nasim Matar , J. Zraqou , Firas Omar , Athari Alnatsheh

{"title":"A novel lift adjustment methodology for improving association rule interpretation","authors":"Bilal Sowan , Li Zhang , Nasim Matar , J. Zraqou , Firas Omar , Athari Alnatsheh","doi":"10.1016/j.dajour.2025.100582","DOIUrl":"10.1016/j.dajour.2025.100582","url":null,"abstract":"<div><div>Association rules can offer a human-interpretable insight extracted from data. The lift measures used for evaluating association rules in classical Association Rule Mining (ARM) contexts are mainly based on traditional and well-known ones but suffer from interpretation inadequacy when dealing with skewed distributions or low support. This study introduces a new lift adjustment approach with four methods to overcome traditional lift measures and identify the best rules in association rule mining. More concretely, our main objective is to improve the interpretability of association rules to make them more practically relevant for decision-making. We propose an approach incorporating four novel lift adjustment methods (smoothed, weighted, log, and threshold-adjusted lift) to achieve this. We introduce a flexible, dynamic approach combined with four new lift adjustment methods: smoothed, weighted, logarithm, and threshold-adjusted lift. Each technique addresses specific limitations of the traditional lift measure and better captures the reliable representation of item associations by exaggerating stronger relationships or smoothing weaker ones. The proposed methods applied context-aware rule evaluation and adjustment based on measures of relative significance (e.g., Jaccard similarity). The experimental results involving real-world data and synthetic datasets reveal new methods’ effectiveness and robustness in understanding the strengths of association rules and provide a comprehensive view that considers item importance. We evaluate the performance stability of our proposed methods using statistical analysis, including ANOVA, chi-squared, t-tests, and effect size metrics.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100582"},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924103","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 multiple factor analysis and hierarchical clustering of global logistics governance and development 全球物流治理与发展的多因素分析与层次聚类

Decision Analytics Journal Pub Date : 2025-04-28 DOI: 10.1016/j.dajour.2025.100579

Delimiro Visbal-Cadavid , Enrique Delahoz-Domínguez , Adel Mendoza-Mendoza

{"title":"A multiple factor analysis and hierarchical clustering of global logistics governance and development","authors":"Delimiro Visbal-Cadavid , Enrique Delahoz-Domínguez , Adel Mendoza-Mendoza","doi":"10.1016/j.dajour.2025.100579","DOIUrl":"10.1016/j.dajour.2025.100579","url":null,"abstract":"<div><div>This study integrates the Logistics Performance Index (LPI), Worldwide Governance Indicators (WGI), and Human Development Index (HDI) through Multiple Factor Analysis (MFA) and hierarchical clustering to create a comprehensive perspective on global development. By clustering countries based on these indicators, the analysis reveals distinct profiles highlighting variations in logistics performance, governance quality, and socio-economic development, yielding insights essential for addressing global development challenges. Three primary clusters emerged, representing countries with socio-economic vulnerabilities, emerging economies with moderate governance, and highly developed nations with advanced infrastructure. Key results demonstrate that Cluster 1 countries require substantial support in governance and infrastructure, while Cluster 2 nations benefit from institutional and logistical investment. Cluster 3 exemplifies governance and socio-economic standards benchmarks, offering sustainable development models. MFA and hierarchical clustering have proven effective in categorising countries with complex data, allowing policymakers to tailor development strategies. The study underscores the need for ongoing research to capture shifts in country profiles and assess intervention impacts over time.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100579"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898990","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 Stackelberg game analysis of livestreaming sales and product returns in e-commerce 电子商务中直播销售与产品退货的Stackelberg博弈分析

Decision Analytics Journal Pub Date : 2025-04-24 DOI: 10.1016/j.dajour.2025.100578

Fangfang Wei , Hao Hao , Pourya Pourhejazy , Zhaoran Xu

{"title":"A Stackelberg game analysis of livestreaming sales and product returns in e-commerce","authors":"Fangfang Wei , Hao Hao , Pourya Pourhejazy , Zhaoran Xu","doi":"10.1016/j.dajour.2025.100578","DOIUrl":"10.1016/j.dajour.2025.100578","url":null,"abstract":"<div><div>Many enterprises selling products on e-commerce platforms have adopted livestreaming to increase sales volume. A high return rate, caused by an exaggerated presentation of the products, can overwhelm the supply chain. Livestreaming considering the product return issue has received little attention in the academic literature. Building on the existing knowledge of the traditional e-commerce sales model, a Stackelberg game model led by an apparel enterprise is established to study livestreaming as a new sales strategy, considering the return rates, the livestreaming anchors’ commission, and the products’ diminishing time-value. A comparative analysis investigates whether the livestreaming sales model increases profitability, considering that some products may be returned. The results show that the return rate has a meaningfully different impact on the optimal price and sales volume of both the apparel enterprise and the third-party liquidation seller. In livestreaming sales, if the commission charged by the anchor passes a certain threshold, the apparel enterprise’s profit will be less than its traditional e-commerce profit, even with a low return rate. It is also found that a higher diminishing time-value coefficient of apparel may correspond to lower pricing by the third-party liquidation seller.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100578"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898989","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

Multi-criteria decision making to explore the relationship between supply chain mapping and performance 多准则决策，探讨供应链映射与绩效之间的关系

Decision Analytics Journal Pub Date : 2025-04-21 DOI: 10.1016/j.dajour.2025.100577

Md Ainul Kabir , Sharfuddin Ahmed Khan , Angappa Gunasekaran , Muhammad Shujaat Mubarik

{"title":"Multi-criteria decision making to explore the relationship between supply chain mapping and performance","authors":"Md Ainul Kabir , Sharfuddin Ahmed Khan , Angappa Gunasekaran , Muhammad Shujaat Mubarik","doi":"10.1016/j.dajour.2025.100577","DOIUrl":"10.1016/j.dajour.2025.100577","url":null,"abstract":"<div><div>In today’s highly dynamic and volatile business environment, the performance of a supply chain significantly depends on its structure, technological capabilities, and the adaptability of its constituent stages. Supply chain mapping, an approach to represent complex supply chain networks, is crucial for enhancing supply chain performance by identifying critical linkages, flows, and relationships. Despite its strategic importance, the specific impacts of supply chain mapping attributes on various performance indicators remain underexplored. Addressing this research gap, this study investigates the relationships between key supply chain mapping attributes (<em>e.g</em>., information flow, lead-time, mode of transportation) and supply chain performance indicators (<em>e.g</em>., reliability, responsiveness, agility). To achieve this, the study employs a multi-step analytical approach: first, relevant attributes are identified through a systematic literature review; second, these attributes are validated using the Delphi method involving international supply chain experts; finally, the Grey Decision-Making Trial and Evaluation Laboratory (Grey-DEMATEL) technique is applied to establish interrelationships among the attributes. Findings reveal that information flow is the most influential supply chain mapping attribute, significantly impacting multiple performance indicators, especially supply chain responsiveness. The novelty of this research lies in its integrative use of Delphi and Grey-DEMATEL methods, providing practitioners with actionable insights into effectively leveraging supply chain mapping to achieve strategic performance improvements.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100577"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886652","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 systematic review of machine learning for hybrid intelligence in production management 生产管理中混合智能机器学习的系统综述

Decision Analytics Journal Pub Date : 2025-04-17 DOI: 10.1016/j.dajour.2025.100574

Carl René Sauer, Peter Burggräf, Fabian Steinberg

{"title":"A systematic review of machine learning for hybrid intelligence in production management","authors":"Carl René Sauer, Peter Burggräf, Fabian Steinberg","doi":"10.1016/j.dajour.2025.100574","DOIUrl":"10.1016/j.dajour.2025.100574","url":null,"abstract":"<div><div>The increasing use of intelligent data processing and its capacity to handle vast data sets enhance efficiency and effectiveness in production management. Consequently, machine learning models have become essential for decision-making in this domain. Previous literature reviews have not considered the perspective of real business requirements from the domain environment, including a knowledge base of theoretical foundations and available methods within the domain. To provide a scientific overview of the current state of the art and to establish a starting point for developing new approaches, this paper presents the results of a systematic literature review. 217 publications were analyzed and synthesized. The publications are classified based on a developed framework that considers the decision type, the production management application, the underlying objective, type, technique, concrete algorithm of the ML model, and decision support for production management issues. A descriptive analysis reveals that there are approaches for all decision types, including unstructured decisions. Surprisingly, some of these approaches are not solely based on simulations to find an optimum. Remarkably, the number of publications related to the type of decision support does not decrease with increasing complexity. Although this paper provides practical guidance to practitioners in selecting applications and ML models to assist their decisions in their production environment, there is a significant need for further research to assist production managers. This can be achieved by developing hybrid models involving interaction between machine and human agents.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100574"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848670","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