Computers & Industrial Engineering最新文献

{"title":"Evaluating machine learning-driven intrusion detection systems in IoT: Performance and energy consumption","authors":"Saeid Jamshidi ,&nbsp;Kawser Wazed Nafi ,&nbsp;Amin Nikanjam ,&nbsp;Foutse Khomh","doi":"10.1016/j.cie.2025.111103","DOIUrl":"10.1016/j.cie.2025.111103","url":null,"abstract":"<div><div>In the landscape of network security, the integration of Machine Learning (ML)-based Intrusion Detection System (IDS) represents a significant leap forward, especially in the domain of the Internet of Things (IoT) and Software-Defined Networking (SDN). Such ML-based IDS are crucial for improving security infrastructures, and their importance is increasingly pronounced in IoT systems. However, despite the rapid advancement of ML-based IDS, there remains a gap in understanding their impact on critical performance metrics (e.g., CPU load, energy consumption, and CPU usage) in resource-constrained IoT devices. This becomes especially crucial in scenarios involving real-time cyber threats that challenge IoT devices in a public/private network.</div><div>To address this gap, this article presents an empirical study that evaluates the impact of state-of-the-art ML-based IDSs on performance metrics such as CPU usage, energy consumption, and CPU load in the absence and presence of real-time cyber threats, with a specific focus on their deployment at the edge of IoT infrastructures. We also incorporate SDN to evaluate the comparative performance of ML-based IDSs with and without SDN. To do so, we focus on the impact of both SDN’s centralized control and dynamic resource management on the performance metrics of an IoT system. Finally, we analyze our findings using statistical analysis using the Analysis of Variance (ANOVA) analysis. Our findings demonstrate that traditional ML-based IDS, when implemented at the edge gateway with and without SDN architecture, significantly affects performance metrics against cyber threats compared to DL-based ones. Also, we observed substantial increases in energy consumption, CPU usage, and CPU load during real-time cyber threat scenarios at the edge, underscoring the resource-intensive nature of these systems. This research fills the existing knowledge void and delivers essential insights into the operational dynamics of ML-based IDS at edge gateway in IoT systems.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111103"},"PeriodicalIF":6.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trust driven group decision making: Research progress and prospects from the perspective of consensus","authors":"Mingshuo Cao ,&nbsp;Qi Sun ,&nbsp;Francisco Chiclana ,&nbsp;Yujia Liu ,&nbsp;Tiantian Gai ,&nbsp;Yiling Yang ,&nbsp;Jian Wu","doi":"10.1016/j.cie.2025.111101","DOIUrl":"10.1016/j.cie.2025.111101","url":null,"abstract":"<div><div>Trust driven Group Decision Making (TGDM) is a new type of decision making process conducted through trust relationships and information exchange between individuals in the social network environment. By systematically organizing the research progress of TGDM and exploring its future research directions, the GDM research for consensus will be promoted. Firstly, this article combs the development status and research trends in recent years based on bibliometrics methods, and then summarizes and discusses the important literature related to TGDM. Secondly, it defines the scientific research category and basic framework of GDM and TGDM. Thirdly, the basic related concepts of TGDM problems are summarized, and then its characteristics and function are analyzed. Finally, it analyzes the problems and challenges faced by TGDM research and explores future research directions. It finds that many scholars have constructed multi-dimensional TGDM models from different perspectives, which have shown wonderful application performance in fields such as product design, failure mode and effects analysis, meta universe virtual communities, and Water–Energy–Food. In addition, it will be a very promising research direction to in-depth investigate TGDM driven by scene, behavior and decision maker’s personality characteristics.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111101"},"PeriodicalIF":6.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven modelling framework for predicting the quality of semiconductor devices to support burn-in decisions","authors":"Ibrahim Ahmed ,&nbsp;Piero Baraldi ,&nbsp;Enrico Zio ,&nbsp;Horst Lewitschnig","doi":"10.1016/j.cie.2025.111115","DOIUrl":"10.1016/j.cie.2025.111115","url":null,"abstract":"<div><div>Burn-in testing of semiconductor devices is performed to ensure product quality by identifying and removing early-life failures. Given the cost and time required by burn-in testing, this work proposes a framework to predict the quality of a production batch of semiconductor devices before burn-in. Unlike traditional methods for quality prediction that rely solely on statistical data, this framework incorporates production data to improve prediction accuracy. The framework combines statistical methods for feature extraction (Piecewise Aggregate Approximation and Principal Component Analysis) and quality estimation (Clopper-Pearson Estimator) with a modified Probabilistic Support Vector Regression (PSVR) to predict early-life failures. The PSVR hyperparameters are set by a Bayesian Optimization (BO) technique. The framework is validated on a synthetic case study designed to emulate the BI process of semiconductor devices and, then, applied to real data collected during semiconductor production. Results from a synthetic case study and real-world semiconductor production data demonstrate the accuracy of the proposed method in predicting the quality of production batches. The quality predictions can, then, be used to inform efficient burn-in test planning in terms of the number of devices to undergo burn-in and the type of burn-in tests to perform.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111115"},"PeriodicalIF":6.7,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solving many-objective reentrant hybrid flowshop scheduling problem considering uncertainty factors in thin-film transistor liquid crystal display","authors":"YongWei Wu ,&nbsp;XiuFang Lin ,&nbsp;GuangYu Zhu","doi":"10.1016/j.cie.2025.111117","DOIUrl":"10.1016/j.cie.2025.111117","url":null,"abstract":"<div><div>The thin-film transistor liquid crystal display (TFT-LCD) front-end array manufacturing process exhibits reentrant characteristics, with uncertainties in the transportation of glass substrates during shop scheduling, further impacting carbon emissions. This study develops a reentrant hybrid flow shop scheduling model considering carbon emissions under uncertain transportation time, where the uncertain transportation time is specifically defined by a triangular fuzzy number (TFN), and a crossing reentrant job handling mechanism is proposed. According to the characteristics of the problem, the shop scheduling process is optimized. In scheduling optimization, the Pythagorean fuzzy set (PFS) is used to solve the problem of uncertain transportation time, and the MYCIN uncertainty factor method, originating from the MYCIN expert system, is employed to evaluate the scheduling scheme and assist metaheuristic algorithm decision-making. A many-objective decision-making method based on PFS and MYCIN uncertainty factors theory is proposed. The golden section factor and the Levy flight are introduced into optimal foraging algorithm (OFA). An improved OFA based on the PFS and MYCIN uncertainty factors (PMYCIN-OFA) is then designed. Finally, three types of experiments are conducted: test cases testing, factory application case testing, and industrial software Flexsim simulations. The results demonstrate that the PMYCIN-OFA surpasses the performance of five classical multi-objective intelligent optimization algorithms and can provide practical solutions in the actual TFT-LCD front-end array manufacturing workshop.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111117"},"PeriodicalIF":6.7,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human control mode enables accurate real-time risk warning in human–machine systems","authors":"Chongfeng Li ,&nbsp;Xing Pan ,&nbsp;Linchao Yang ,&nbsp;Jun Wang ,&nbsp;Haobing Ma","doi":"10.1016/j.cie.2025.111110","DOIUrl":"10.1016/j.cie.2025.111110","url":null,"abstract":"<div><div>Data-driven risk analysis serves as an essential approach to risk mitigation in human–machine systems. Presently, risk management rooted in data often depends on labels extracted from risk outcomes, accentuating a causative risk management paradigm. However, these labels frequently fall short in capturing the dynamic evolution of risks in real-time, especially accounting for the impact of human intervention on risk dissemination. In striving for greater precision in real-time risk prediction within human–machine systems, human control is identified as a pivotal factor in shaping risk progression. A precise warning model is devised based on human control patterns, discerned through clustering control data focusing on “timeliness,” “stability,” and “coordination.” This methodology facilitates the development of machine learning-driven warning models. The viability of the proposed approach is substantiated through a case study involving aircraft landing mishaps. This research furnishes a conceptual framework and procedural guidelines to propel risk analysis within human–machine systems, with an emphasis on human-centric risk warnings across diverse industrial contexts.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111110"},"PeriodicalIF":6.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodological design of a digital twin architecture in advanced surgery applied to the treatment of enteroatmospheric fistula","authors":"Andres Padillo ,&nbsp;Jesús Racero ,&nbsp;Jose Carlos Molina ,&nbsp;Ignacio Eguía ,&nbsp;Javier Padillo","doi":"10.1016/j.cie.2025.111105","DOIUrl":"10.1016/j.cie.2025.111105","url":null,"abstract":"<div><div>This research describes the design of an integrated model system to address the monitoring, treatment, and evolution of a pathology throughout its life cycle (LCP, Life Cycle Pathology) based on the Digital Twin (DT) concept, showing the capabilities of the system and the possibilities that it offers in the treatment of the pathology in an integrated way. The concept of DT in the field of medicine is a relatively recent concept. Its application is mainly focused on very reduced areas, such as prosthesis development and simulation of the cardiovascular system mainly. The DT con-cept allows the integration of simulation tools, diagnosis, &amp; treatment, and follow up of pathologies, adapting all of them to the disease and patient. Therefore, so its inclusion in the medical field permits a personalization and creates a source of knowledge in the treatment of diseases. This research, as an application, will address the management of enteroatmospheric fistula (EAF), an uncommon pathology framed within advanced abdominal wall surgery, with a mortality rate close to 40%. To achieve this purpose, the direct and/or indirect variables associated with each patient must be considered in order to control, simulate, and evaluate the pathology Through the combination and collection of the physical information provided by the patient combined with the virtual information offered by the technology (DT); with the aim of being able to anticipate the real changes suffered throughout the LCP; predicting its behaviors and facilitates the surgeons decision-making on the treatment and management of the fistula.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111105"},"PeriodicalIF":6.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonparametric monitoring of high-dimensional processes via EWMA control charts based on random forest learning","authors":"Cang Wu ,&nbsp;Dong Wang ,&nbsp;Min Luo ,&nbsp;Wenpo Huang ,&nbsp;Zexin Si","doi":"10.1016/j.cie.2025.111111","DOIUrl":"10.1016/j.cie.2025.111111","url":null,"abstract":"<div><div>Multi-sensor systems have been widely used in manufacturing processes to perceive environmental or operating conditions and quality information, collecting large amounts of high-dimensional quality data. Because these systems are so common in practice, the important question has emerged as to how to leverage the full potential of rich data to monitor the various processes. Most traditional multivariate control charts in the field of high-dimensional data monitoring are no longer relevant because of their high dimensionality and typically unknown prior distribution of variables. In response to this need, this paper proposes a novel nonparametric Exponentially Weighted Moving Average (EWMA) control scheme by employing the random forest (RF) algorithm and log-likelihood method to transform high-dimensional data into one-dimensional data serving as the input of monitoring statistics. The simulation results indicate that the proposed control scheme outperforms its competitors in terms of various distributions and data types, especially for only one out-of-control (OC) cluster in the data stream. We also extend our scheme to the additional cases of a disk monitoring study and a breast cancer monitoring study prove the robustness and effectiveness of the proposed scheme.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111111"},"PeriodicalIF":6.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Policy optimization and practical applications in blockchain-enabled electric vehicle battery recycling system","authors":"Zhuoqun Li ,&nbsp;Jinsong Wei","doi":"10.1016/j.cie.2025.111119","DOIUrl":"10.1016/j.cie.2025.111119","url":null,"abstract":"<div><div>The rapid growth of the electric vehicle (EV) market underscores the urgent need for efficient battery recycling systems. Existing systems face challenges such as information asymmetry, operational inefficiencies, and environmental risks from improper disposal. This study first optimizes policy strategies using an evolutionary game-theoretic model to analyze key stakeholder interactions, then proposes a blockchain-based traceability system that enhances transparency, security, and efficiency throughout the EV battery lifecycle. Simulation results demonstrate that effective incentives and regulatory frameworks significantly improve system performance, providing a solid foundation for policy development. This research highlights blockchain’s potential to advance sustainability, support net-zero goals, and promote a circular economy in the EV industry.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111119"},"PeriodicalIF":6.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remaining useful life prediction of equipment using a multiobjective optimization reinforced prognostic approach","authors":"Jichao Zhuang ,&nbsp;Xiaotong Ding ,&nbsp;Zilin Zhang ,&nbsp;Xiaoli Zhao ,&nbsp;Weigang Li ,&nbsp;Ke Feng","doi":"10.1016/j.cie.2025.111116","DOIUrl":"10.1016/j.cie.2025.111116","url":null,"abstract":"<div><div>Data-driven methods have rapidly advanced equipment degradation monitoring and prognosis. However, traditional deep models rely on weak prior degradation knowledge and may not effectively incorporate degradation damage information. To address this limitation, a Deep Multiobjective Optimization Reinforced Prognostic (MORP) framework is proposed in this paper for equipment health prognosis. Specifically, a priori degradation knowledge and multi-source deep features are combined at both the feature and health indicator (HI) levels. They are then quantified into an unsupervised multi-objective optimization decision. Preceding this step, a multi-degradation criterion and HI generalizability are formulated as a multi-objective function, with the aim of enhancing the generalizability, monotonicity, tendency, and robustness of HIs. Comprehensive Health Indicators (CHIs) are then constructed while retaining the advantages of the Pareto frontier, using a reinforcement learning-guided swarm intelligence optimization method. To address anomalies within CHIs, a HI burr correction method featuring an interpolation-extrapolation term is introduced. Additionally, the prediction of remaining useful life is accomplished through a supervised prognostic scheme. Finally, the proposed methodology is applied to equipment datasets to validate its performance.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111116"},"PeriodicalIF":6.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient tugboat scheduling: A hybrid transformer-attention mechanism and artificial multiple intelligence system","authors":"Rapeepan Pitakaso ,&nbsp;Kanchana Sethanan ,&nbsp;Sarayut Gonwirat ,&nbsp;Chen-Fu Chien ,&nbsp;Ming K. Lim ,&nbsp;Ming-Lang Tseng","doi":"10.1016/j.cie.2025.111112","DOIUrl":"10.1016/j.cie.2025.111112","url":null,"abstract":"<div><div>Tugboats play a crucial role in connecting maritime and inland logistics by transferring goods from large vessels. However, managing their energy consumption is a major challenge due to factors such as barge capacity, cargo weight, tidal schedules, navigational complexities, and regulatory constraints. Efficient scheduling is essential to minimizing costs and enhancing sustainability. To address this challenge, this study introduces a mixed-integer programming model to optimize tugboat scheduling, incorporating real-world constraints to reduce energy consumption and operational inefficiencies. To address industrial scale problems, we propose a Hybrid Transformer-Attention Mechanism and Artificial Multiple Intelligence System (HT-AMIS), combined with metaheuristic-inspired intelligence boxes (IBs), to enhance adaptability and efficiency. The results show that HT-AMIS reduces tugboat operating costs by 11.75%, with energy costs reduced by 10.73% and penalty costs reduced by 21.96%. The model demonstrated robustness, effectively handling challenging scenarios such as tugboat breakdowns and severe weather conditions.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111112"},"PeriodicalIF":6.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}