Computers & Industrial Engineering最新文献

Sustainable fault detection and process simulation in semiconductor manufacturing using machine learning and life cycle assessment 基于机器学习和生命周期评估的半导体制造可持续故障检测和过程仿真

IF 6.5 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-10-08 DOI: 10.1016/j.cie.2025.111584

Tsai-Chi Kuo , Tzu-Yen Hong , Liang-Wei Chen

{"title":"Sustainable fault detection and process simulation in semiconductor manufacturing using machine learning and life cycle assessment","authors":"Tsai-Chi Kuo , Tzu-Yen Hong , Liang-Wei Chen","doi":"10.1016/j.cie.2025.111584","DOIUrl":"10.1016/j.cie.2025.111584","url":null,"abstract":"<div><div>As digital transformation advances across various industries, the growing demand for semiconductor manufacturing calls for developing methodologies that enhance production efficiency while minimizing environmental impact. Although various methodologies have demonstrated significant potential in fault detection and process optimization, existing approaches mainly focus on improving defect reduction without systematically incorporating the impacts on sustainability considerations. This study proposes an integrated framework that synergistically combines fault detection, discrete event simulation (DES), and life cycle assessment (LCA) to address both operational efficiency and sustainability in the photolithography process. A machine learning (ML) model is developed for defect prediction. DES is then used by incorporating an inspection control mechanism informed by the defect prediction results, enabling the removal of defective wafers to reduce unnecessary processing and improve production efficiency, while LCA quantifies the corresponding environmental impact to enable sustainability evaluation aligned with industrial practices. Experiments are conducted to validate the effectiveness of the proposed framework, and the results demonstrate improvements in both operational efficiency and environmental footprint in semiconductor manufacturing.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"210 ","pages":"Article 111584"},"PeriodicalIF":6.5,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268483","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}

引用次数: 0

A simheuristic approach to optimize energy consumption in the single-machine scheduling problem with stochastic processing times 具有随机加工时间的单机调度问题能耗优化的近似启发式方法

IF 6.5 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-10-06 DOI: 10.1016/j.cie.2025.111580

Matheus Lopes Bittencourt , Clarissa Maria Rodrigues de Oliveira , Isis Didier Lins , Raphael Kramer

引用次数: 0

Retrieval scheduling in four-directional shuttle-based compact storage and retrieval systems with heterogeneous lifts 异构升降机四向梭式紧凑存储检索系统的检索调度

IF 6.5 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-10-06 DOI: 10.1016/j.cie.2025.111559

Rong Wang , Peiran Tao , Rui Chen , Taoxing Zhu , Peng Yang

{"title":"Retrieval scheduling in four-directional shuttle-based compact storage and retrieval systems with heterogeneous lifts","authors":"Rong Wang , Peiran Tao , Rui Chen , Taoxing Zhu , Peng Yang","doi":"10.1016/j.cie.2025.111559","DOIUrl":"10.1016/j.cie.2025.111559","url":null,"abstract":"<div><div>As an emerging tier-to-tier shuttle-based system, the four-directional shuttle-based compact storage and retrieval system with heterogeneous lifts utilizes four-directional shuttles for in-tier bin transport; inter-tier movements are executed by two heterogeneous lift types, with shuttle-lifts transporting shuttles and bin-lifts transporting bins. Since the bin-lift is typically less expensive than the shuttle-lift, employing two types of lifts can reduce both investment and operational costs. The execution of retrieval requests in this system requires coordinated operation of three types of equipment, making the scheduling optimization problem both complex and challenging. This paper investigates the retrieval scheduling problem, which focuses on determining the task sequences of shuttles and lifts to minimize the makespan. We formulate a mixed integer programming model and propose an exact logic-based Benders decomposition algorithm. Additionally, we develop an effective two-stage heuristic to provide near-optimal solutions for large-scale instances. The two-stage heuristic consistently outperforms alternative scheduling strategies, delivering shorter makespan, lower average retrieval time, and reduced capital investment. Numerical experiments reveal that increasing the number of shuttles and bin-lifts, rather than shuttle-lifts, leads to a more significant reduction in makespan. In addition, bin-lifts demonstrate greater cost-effectiveness compared to shuttle-lifts; partial substitution of shuttle-lifts with bin-lifts yields substantial economic benefits, with the highest benefits observed when bin-lifts account for 20% of the total lift investment. Furthermore, positioning lifts at the midpoint along the picking tracks produces the minimum makespan. These findings provide valuable insights into equipment configuration for optimizing the performance of such systems.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"210 ","pages":"Article 111559"},"PeriodicalIF":6.5,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268484","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}

引用次数: 0

Prediction of measuring instrument calibration interval based on risk priority number and reliability using machine learning 基于风险优先级数和可靠性的测量仪器校准间隔的机器学习预测

IF 6.5 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-10-05 DOI: 10.1016/j.cie.2025.111570

Nassibeh Janatyan, Somaieh Alavi, Esmaeil Shafiee

{"title":"Prediction of measuring instrument calibration interval based on risk priority number and reliability using machine learning","authors":"Nassibeh Janatyan, Somaieh Alavi, Esmaeil Shafiee","doi":"10.1016/j.cie.2025.111570","DOIUrl":"10.1016/j.cie.2025.111570","url":null,"abstract":"<div><div>The present study develops and introduces a new technique for predicting the calibration interval of Measuring instruments using machine learning (ML) with the features of risk priority number (RPN) and reliability (R). The proposed method predicts the calibration interval by considering risk, R based on instrument life cycle distribution and ML techniques. To check this prediction method, the data related to 220 measuring instruments of the steel company were used, and for each measuring instrument in this section, according to the opinion of the company’s experts, RPN was determined, and then based on the life cycle distribution of each, the Reliability index was calculated. Two hundred twenty measuring instruments were placed in three clusters of 12-month, 18-month, and 36-month calibration intervals using the K-Means clustering technique to label the data. Then, to predict the calibration interval of new measuring instruments, three conventional classifiers in the application of ML in maintenance, namely K-NN, RF, and SVM, were employed and tested for the data of the new measuring instruments. Finally, evaluating the performance accuracy of these three methods for prediction according to the data class, K-NN, and RF methods provided better performance.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"210 ","pages":"Article 111570"},"PeriodicalIF":6.5,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267807","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}

引用次数: 0

An information-sharing and cost-aware custom loss machine learning framework for 3PL supply chain forecasting 一个用于第三方物流供应链预测的信息共享和成本意识的定制损失机器学习框架

IF 6.5 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-10-05 DOI: 10.1016/j.cie.2025.111573

Matteo Gabellini , Francesca Calabrese , Francesco Gabriele Galizia , Michele Ronchi , Alberto Regattieri

{"title":"An information-sharing and cost-aware custom loss machine learning framework for 3PL supply chain forecasting","authors":"Matteo Gabellini , Francesca Calabrese , Francesco Gabriele Galizia , Michele Ronchi , Alberto Regattieri","doi":"10.1016/j.cie.2025.111573","DOIUrl":"10.1016/j.cie.2025.111573","url":null,"abstract":"<div><div>Supply chain forecasting methods have traditionally been developed from the perspective of manufacturing companies, which historically held dominant roles within supply chain dynamics. However, the growing importance of third-party logistics providers (3PLs) calls for forecasting approaches tailored to their unique operational needs. This paper presents a novel forecasting framework specifically designed for 3PLs to accurately predict the truck space required for transporting their customers’ products. Unlike conventional methods, the proposed approach directly forecasts truck space demand by utilizing data obtained through information-sharing technologies to train machine learning models. Furthermore, a customized loss function is introduced for the first time, explicitly accounting for the asymmetric costs associated with overestimating and underestimating truck utilization. The framework was validated through a real-world case study involving a 3PL operating in the food sector. The results demonstrated significant improvements over traditional forecasting techniques, underscoring the benefits of integrating machine learning, information sharing, and a tailored loss function to enhance both predictive accuracy and cost-efficiency.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"210 ","pages":"Article 111573"},"PeriodicalIF":6.5,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267801","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}

引用次数: 0

A bi-objective optimization approach for multi-echelon supply network disruption risk assessment and critical supply path identification 多级供应网络中断风险评估和关键供应路径识别的双目标优化方法

IF 6.5 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-10-05 DOI: 10.1016/j.cie.2025.111572

Chengrui Lyu, Jing Chen

{"title":"A bi-objective optimization approach for multi-echelon supply network disruption risk assessment and critical supply path identification","authors":"Chengrui Lyu, Jing Chen","doi":"10.1016/j.cie.2025.111572","DOIUrl":"10.1016/j.cie.2025.111572","url":null,"abstract":"<div><div>This study tackles the critical challenge of managing uncertainty and disruption risk in multi-echelon supply networks (SNs) under data scarcity. We propose a novel bi-objective optimization approach integrating Bayesian Networks (BNs) and information theory to simultaneously assess disruption risk and identify the critical supply path. To address data scarcity and inherent ambiguity, BN parameters (probabilities) are represented as interval values. Entropy and information gain are used to quantify uncertainty and the value of supplier information. Two nonlinear optimization models are developed to find pareto optimal solutions representing the trade-off between minimizing the downstream manufacturer’s fully disrupted probability (reflecting operational continuity concerns) and maximizing the total information gain obtainable from supply paths (guiding uncertainty reduction efforts). The models explicitly consider two crucial scenarios based on whether the manufacturer initially possesses information about none or some of its direct suppliers. We employ a problem-specific Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the models. Experimental results on various SN topologies demonstrate the framework’s effectiveness. Findings indicate that identifying the critical supply path via information gain provides valuable insights for anticipating risks and strategically reducing uncertainty. Importantly, the determination of the critical supply path and the nature of the trade-off between risk and informational advantage are shown to be significantly influenced by both the SN’s topological structure and the manufacturer’s initial information state. This research offers a comprehensive decision support tool for enhancing SN resilience under uncertainty.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"210 ","pages":"Article 111572"},"PeriodicalIF":6.5,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268482","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}

引用次数: 0

Robust state of health estimation and remaining useful life prediction for lithium-ion battery with synchronous framework using data-driven integrated model 基于数据驱动集成模型的同步框架下锂离子电池健康状态估计与剩余使用寿命预测

IF 6.5 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-10-04 DOI: 10.1016/j.cie.2025.111575

Cheng Qian, Ning He, Ziqi Yang, Fuan Cheng

{"title":"Robust state of health estimation and remaining useful life prediction for lithium-ion battery with synchronous framework using data-driven integrated model","authors":"Cheng Qian, Ning He, Ziqi Yang, Fuan Cheng","doi":"10.1016/j.cie.2025.111575","DOIUrl":"10.1016/j.cie.2025.111575","url":null,"abstract":"<div><div>Accurate state of health estimation and remaining useful life prediction can improve the safety and prolong the service life of lithium-ion battery system. This paper proposes a robust framework for state of health estimation and remaining useful life prediction based on data-driven integrated model. Firstly, three representative health indicators are extracted to reflect the aging state of the battery, and these health indicators are developed based on the energy information in charging and discharging process. Secondly, a data-driven integrated model of battery ageing state-space representation is developed, in which Gaussian process regression is employed to establish state equation using historical capacity series and current capacity, and maps the relationship between capacity degradation and health indicators to construct an observation equation. Thirdly, the particle filter is introduced to realize the closed-loop estimation of battery capacity and suppress the measurement noises by combining with the data-driven integrated model and regarding current extracted health features as observations. Meanwhile, available estimated capacity is fed back to the model to build the dynamic architecture. Fourthly, for the unavailability of observations in remaining useful life prediction issue, an autoregressive model is introduced to roll predict the future observation from the historical health features to complete the closed-loop synchronous framework, and an error compensation mechanism based on the extreme learning machine scheme is proposed to further enhance the accuracy of estimation. Finally, a practical aging experience involving 7 batteries are performed, and the experimental results illustrate that the proposed method can guarantee high accuracy and robustness relatively.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"210 ","pages":"Article 111575"},"PeriodicalIF":6.5,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268485","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}

引用次数: 0

Remaining useful life prediction considering correlated multi-parameter nonlinear degradation and small sample conditions 考虑相关多参数非线性退化和小样本条件的剩余使用寿命预测

IF 6.5 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-10-04 DOI: 10.1016/j.cie.2025.111567

Cheng Qian , Yuhang Li , Yao Zhu , Dezhen Yang , Yi Ren , Quan Xia , Zili Wang

{"title":"Remaining useful life prediction considering correlated multi-parameter nonlinear degradation and small sample conditions","authors":"Cheng Qian , Yuhang Li , Yao Zhu , Dezhen Yang , Yi Ren , Quan Xia , Zili Wang","doi":"10.1016/j.cie.2025.111567","DOIUrl":"10.1016/j.cie.2025.111567","url":null,"abstract":"<div><div>This study establishes a RUL prediction method based on an improved Wasserstein GAN, a nonlinear Wiener process, and a Copula function (IWNC) to address correlated multi-parameter nonlinear degradation with small samples. Initially, the proposed IWNC method develops a correlation-aware multi-sequence degradation data augmentation model using an improved Wasserstein Generative Adversarial Network (WGAN) that combines an LSTM-based generator and a 1D CNN-based discriminator. Time series consistency and multi-parameter correlation terms are incorporated into the generator’s loss function to enhance the quality of the augmented degradation data. A nonlinear Wiener process model, integrated with a Copula-based correlation model, is then developed to construct a joint RUL prediction model. Experimental results demonstrated that the IWNC method effectively addresses the challenges of small sample sizes and correlated multi-parameter nonlinear degradation. The augmented data generated by the IWNC method significantly contributes to improving RUL prediction accuracy. Due to the IWNC method’s ease of implementation and broad applicability, it holds considerable potential for widespread adoption across various domains, including digital twins.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"210 ","pages":"Article 111567"},"PeriodicalIF":6.5,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268487","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}

引用次数: 0

Mathematical models for carpooling considering driver absence: Comparative analysis and heuristic strategies 考虑司机缺席的拼车数学模型：比较分析与启发式策略

IF 6.5 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-10-04 DOI: 10.1016/j.cie.2025.111577

Wushuang Wang , Yu Li , Yiliu Tan , Ryotaro Kobayashi , Hidenobu Hashikami , Maiko Shigeno

{"title":"Mathematical models for carpooling considering driver absence: Comparative analysis and heuristic strategies","authors":"Wushuang Wang , Yu Li , Yiliu Tan , Ryotaro Kobayashi , Hidenobu Hashikami , Maiko Shigeno","doi":"10.1016/j.cie.2025.111577","DOIUrl":"10.1016/j.cie.2025.111577","url":null,"abstract":"<div><div>Carpooling reduces travel costs, alleviates traffic congestion, and increases social interaction, making it an economical, environmentally friendly, and efficient mode of transportation. Considering the uncertainties involved in carpooling, this paper presents mathematical models to find alternative routes for a commuter carpooling service in the event of last-minute driver absences. When a driver cancels their carpooling service, it becomes necessary to secure alternative commuting routes for the riders scheduled to ride in that driver’s car. The proposed models construct alternative routes that minimize deviations from the initially planned route, ensuring that another driver can efficiently pick up and drop off the riders. Considering computational efficiency, a population-based heuristic algorithm is designed for large-scale problems. Numerical experiments based on real data are conducted to compare three different models. The superiority of our algorithm is also confirmed through these experiments. A commuting route is constructed in advance that accounts for potential driver absence, and this alternative route effectively prevents significant changes in the number of commuters riding together and the departure times, even in the event of driver absence.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"210 ","pages":"Article 111577"},"PeriodicalIF":6.5,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267803","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}

引用次数: 0

Developing multistep-ahead quality prediction models for early warning 开发多步超前质量预警预测模型

IF 6.5 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.cie.2025.111555

Yi Shan Lee , Sai Kit Ooi , Junghui Chen

{"title":"Developing multistep-ahead quality prediction models for early warning","authors":"Yi Shan Lee , Sai Kit Ooi , Junghui Chen","doi":"10.1016/j.cie.2025.111555","DOIUrl":"10.1016/j.cie.2025.111555","url":null,"abstract":"<div><div>In the manufacturing industry, product quality is a critical variable to monitor and control. Chemical plants, often large-scale to meet market demands, face challenges due to inherent nonlinearities and slow dynamics. These issues can hinder conventional methods from identifying disturbances before significant deviations in product quality occur, making early warning systems essential for preventing defects. This study introduces a sophisticated two-step multi-step ahead quality prediction framework for early warning. In the first step, a multi-step nonlinear state-space model (MS-NSSM) utilizes a lower-dimensional latent space with reduced noise to capture dynamic information from past process variables for future multi-step process variable prediction. In the second step, a regression variational autoencoder (Reg-VAE) uses these predicted future process variables to establish the process-quality relationship, enabling future multi-step quality prediction through another lower-dimensional latent space. The proposed method’s effectiveness is demonstrated through numerical simulations and real-world industrial cases. Performance metrics show a significant average accuracy for five prediction steps, with the proposed method achieving an <em>R</em><sup>2</sup> value of 0.81 and MAE of 0.26 in numerical cases. In the industrial cases, the proposed method achieves an <em>R</em><sup>2</sup> value of 0.995 and MAE of 0.03. The proposed method outperforms the comparative methods by providing early warnings 30 time points before offline laboratory tests and shows substantial potential for improving quality monitoring in industrial applications.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"210 ","pages":"Article 111555"},"PeriodicalIF":6.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267756","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}

引用次数: 0