Computers & Industrial Engineering最新文献

Design of random maintenance strategies for systems under random collaborative warranty with cost-division 基于成本分摊的随机协同保修系统随机维修策略设计

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-05-28 DOI: 10.1016/j.cie.2025.111257

Lijun Shang , Baoliang Liu , Liying Wang , Rui Peng

{"title":"Design of random maintenance strategies for systems under random collaborative warranty with cost-division","authors":"Lijun Shang , Baoliang Liu , Liying Wang , Rui Peng","doi":"10.1016/j.cie.2025.111257","DOIUrl":"10.1016/j.cie.2025.111257","url":null,"abstract":"<div><div>In the current era, characterized by the extensive application of advanced digital technologies, numerous random warranty models have been developed to effectively manage reliability throughout the entire warranty period. Nevertheless, these existing models fall short of fully meeting the practical and individualized needs of users. Additionally, they do not serve as an independent profit-making model for manufacturers. To address these limitations, this paper presents innovative random collaborative warranty models. Specifically, the two-limit random collaborative warranty first with cost-division (2LRCWF-CD) and the two-limit random collaborative warranty last with cost-division (2LRCWL-CD) are proposed. These models incorporate a user-participation mechanism to satisfy users’ requirements, such as broader warranty coverage. Moreover, they serve as an independent profit-making model, helping manufacturers achieve their profit-seeking goals. They are constructed from both cost and time perspectives. The shutdown losses caused by delay-repairs and the costs associated with rectifying failures are shared between the user and the manufacturer. Moreover, considering the diversity of after-warranty performance indicators, including users’ usage patterns and system reliability, this paper customizes random maintenance strategies. Namely, the bivariate random periodic replacement with preventive maintenance (BRPR-PM) and the bivariate customized random replacement with PM (BCRR-PM) are designed to achieve more precise reliability management. Finally, through comprehensive numerical analyses, the effectiveness of the proposed models and strategies is verified, and some notable findings are obtained.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"206 ","pages":"Article 111257"},"PeriodicalIF":6.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178781","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

Event triggered NN-embedding compensation fault tolerant control for high-speed trains with actuator saturation 动器饱和高速列车事件触发神经网络嵌入补偿容错控制

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-05-28 DOI: 10.1016/j.cie.2025.111213

Zixu Hao, Yumei Liu, Ting Hu, Pengcheng Liu, Ming Liu

{"title":"Event triggered NN-embedding compensation fault tolerant control for high-speed trains with actuator saturation","authors":"Zixu Hao, Yumei Liu, Ting Hu, Pengcheng Liu, Ming Liu","doi":"10.1016/j.cie.2025.111213","DOIUrl":"10.1016/j.cie.2025.111213","url":null,"abstract":"<div><div>In this paper, an event-triggered neural network (NN) embedding compensation control scheme for a high-speed train (HST) with unknown dynamics, unknown disturbances, actuator faults, and asymmetric nonlinear actuator saturation (ANAS) is investigated. The adaptive PID sliding mode fault-tolerant control (PID-SMFTC) with mix basis function approximation (MBF) and finite-time nonlinear disturbance observer (FTNDOB) is proposed as base controller of event triggered NN-embedding compensation control. The MBF is employed to approximate the unknown dynamics term in HST system and eliminate the effect of ANAS. The FTNDOB is used to estimate unknown disturbances within a finite time. Then, NN-embedding compensation control scheme with two event triggered threshold strategies are proposed to optimize the performance of the base controller. Comparing with NN-embedding compensation controller, these methods reduce the consumption of communication and computation resources by optimizing the base controller’s performance only when events are triggered. Finally, simulation results using an actual train dynamic model are showcased to validate the effectiveness and feasibility of the proposed schemes.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"206 ","pages":"Article 111213"},"PeriodicalIF":6.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166526","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

Optimising timing points for effective automatic train operation 优化时间点，使列车有效自动运行

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-05-28 DOI: 10.1016/j.cie.2025.111237

Ziyulong Wang , Egidio Quaglietta , Maarten G.P. Bartholomeus , Alex Cunillera , Rob M.P. Goverde

{"title":"Optimising timing points for effective automatic train operation","authors":"Ziyulong Wang , Egidio Quaglietta , Maarten G.P. Bartholomeus , Alex Cunillera , Rob M.P. Goverde","doi":"10.1016/j.cie.2025.111237","DOIUrl":"10.1016/j.cie.2025.111237","url":null,"abstract":"<div><div>Automatic Train Operation (ATO) aims to partially or fully automate train driving, enhancing railway capacity, punctuality, and energy efficiency. However, a key challenge arises from the mismatch between discrete event-time decisions at the Traffic Management System (TMS) level, assuming fixed running times, and the continuous speed–distance trajectory optimisation at the ATO level, leading to possible misalignments between planned and executed train movements. To bridge this gap, this paper introduces a novel optimisation-based method that dynamically computes Train Path Envelopes (TPEs) based on multiple driving strategies, defined as time targets or windows over a sequence of timing points, which ATO-equipped trains must comply with to align their movements with traffic management constraints. The method follows a two-stage approach: First, a linear programming model determines conflict-free blocking time ranges across the multiple driving strategies. Second, a structured optimisation process establishes operationally feasible TPEs by determining departure tolerances and configuring intermediate timing points. By integrating a critical-block strategy, the optimised TPEs provide the flexibility needed for ATO while accommodating variations in train driving strategies. The method is validated through experiments and a real-life case study in The Netherlands, demonstrating that optimised timing points at critical track locations improve energy efficiency, enhance punctuality, increase capacity, and provide an approach to align traffic management with ATO.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"206 ","pages":"Article 111237"},"PeriodicalIF":6.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166672","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 machine learning approach to predictive maintenance: Remaining useful life and motor fault analysis 预测性维护的机器学习方法：剩余使用寿命和电机故障分析

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-05-27 DOI: 10.1016/j.cie.2025.111222

Xueping Li , John Williams , Colter Swanson , Thomas Berg

{"title":"A machine learning approach to predictive maintenance: Remaining useful life and motor fault analysis","authors":"Xueping Li , John Williams , Colter Swanson , Thomas Berg","doi":"10.1016/j.cie.2025.111222","DOIUrl":"10.1016/j.cie.2025.111222","url":null,"abstract":"<div><div>Rotary motors are integral to various modern technological domains, playing a crucial role in areas such as manufacturing and medical equipment. Consistency in motor performance is vital in these domains, as any downtime can lead to substantial time and financial losses. The advent of Predictive Maintenance (PdM) has provided a means to mitigate this challenge. This paper presents a comprehensive framework designed to predict the specific fault type occurring within a given motor and determine its remaining useful life (RUL). Utilizing Industry 4.0 applications, the proposed framework harnesses real-time vibration and motor current signature analysis (MCSA) data, feeding it into Machine Learning (ML) classification and regression models. These models promptly alert maintenance personnel of potential motor faults. To validate the effectiveness of the proposed framework, experimental verification was conducted using a one-horsepower (HP) motor, in which faults were systematically introduced at specified time intervals. The experimental results affirm the efficacy of the proposed framework in accurately classifying various fault conditions and determining the RUL of the motor. Consequently, the framework enhances the PdM capabilities for motors deployed in practical settings.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"206 ","pages":"Article 111222"},"PeriodicalIF":6.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146991","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

Identification of key enterprise sources and design of target emission reduction pathways contributing to air quality based on unsupervised learning and explainable prediction – A case study of Beijing 基于无监督学习和可解释预测的重点企业污染源识别与空气质量目标减排路径设计——以北京市为例

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-05-26 DOI: 10.1016/j.cie.2025.111260

Zhen Peng , Caixiao Zhang , Zitao Hong

{"title":"Identification of key enterprise sources and design of target emission reduction pathways contributing to air quality based on unsupervised learning and explainable prediction – A case study of Beijing","authors":"Zhen Peng , Caixiao Zhang , Zitao Hong","doi":"10.1016/j.cie.2025.111260","DOIUrl":"10.1016/j.cie.2025.111260","url":null,"abstract":"<div><div>Industrial enterprises are the emission sources whose operations directly affect air quality. Identification of key sources and design of emission reduction pathways for enterprises have been crucial elements of air quality prevention. However, most current studies on air quality prevention focus primarily on macro-level pollution influencing factors or pollutant sources, often failing to to trace the main entity sources contributing to air quality degradation. Therefore, this paper firstly selects Kmeans to cluster the enterprises’ emissions data based on their geographical locations, considering that the enterprises data is noise-free, exhibits clear distance relationships, and is supported by comparisons of multiple clustering effects. Subsequently, we align the industrial enterprise emission clusters with air quality monitoring data, select SVR suitable for predicting the integrated small-sample dataset for air quality forecasting, and utilize the interpretability method SHAP to identify industrial enterprise clusters that have a significant impact on air quality. Finally, the entropy weight method is utilized to determine the different pollution levels of enterprises based on their contributions to air quality and the four quadrants method is applied to classify the enterprises under each pollution level. Based on this classification, the sources of enterprise pollution emissions are investigated, and emission reduction pathways are designed for each category.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"206 ","pages":"Article 111260"},"PeriodicalIF":6.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146987","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

Modeling and analysis of cascade failures in Industrial Internet of Things based on task decomposition and service communities 基于任务分解和服务社区的工业物联网级联故障建模与分析

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-05-26 DOI: 10.1016/j.cie.2025.111177

Lingli Zhu , Xiuwen Fu , Xiangwei Liu , Shichang Du

{"title":"Modeling and analysis of cascade failures in Industrial Internet of Things based on task decomposition and service communities","authors":"Lingli Zhu , Xiuwen Fu , Xiangwei Liu , Shichang Du","doi":"10.1016/j.cie.2025.111177","DOIUrl":"10.1016/j.cie.2025.111177","url":null,"abstract":"<div><div>In Industrial Internet of Things (IIoT) systems, the overload of certain nodes may lead to the failure of the entire network, a phenomenon known as cascade failures. This occurrence has an immeasurable impact on industrial production. Establishing a realistic IIoT cascade failure model is crucial for enhancing the cascade reliability of IIoT systems. However, existing research on cascade failures in IIoT lacks an in-depth exploration of the actual characteristics in industrial scenarios, hindering accurate modeling of the cascade failure process in IIoT. To better characterize the cascade failure process of IIoT, this study proposes an interdependent network model from a cyber-service coupling perspective, considering task decomposition, service community structure, and coupling patterns. On this basis, we design a realistic cascade failure model that is based on production supply relationships between manufacturing units. In the experiments, we first validate the rationality of the proposed model through load distribution and load probability density analysis. Furthermore, the study of key modeling parameters then reveals that cyber network attacks cause more damage than service network attacks. Finally, we apply the model to four industrial manufacturing scenarios and explore their cascade failure performance.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"206 ","pages":"Article 111177"},"PeriodicalIF":6.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146990","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 new ordinal-cardinal consensus reaching method for multi-attribute group decision-making considering individual belief transformation 考虑个体信念转换的多属性群体决策序基数共识新方法

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-05-24 DOI: 10.1016/j.cie.2025.111262

Suqiong Hu , Mei Cai , Jingmei Xiao , Zaiwu Gong

{"title":"A new ordinal-cardinal consensus reaching method for multi-attribute group decision-making considering individual belief transformation","authors":"Suqiong Hu , Mei Cai , Jingmei Xiao , Zaiwu Gong","doi":"10.1016/j.cie.2025.111262","DOIUrl":"10.1016/j.cie.2025.111262","url":null,"abstract":"<div><div>Individual beliefs dynamically transform during negotiation and interaction in the consensus reaching process (CRP), influencing preference formation and posing significant challenges to group decision-making methods. In this paper, we develop a method for multi-attribute group decision-making (MAGDM) that incorporates individual belief transformation from the ordinal and cardinal perspectives. First, we gather two types of preference information from individuals: pairwise comparison relationship and alternative-attribute evaluation information. To preserve as much original preference information as possible, we propose verification and adjustment models that consider consistent attribute prioritization to obtain consistent preference information. On this basis, an optimization model that minimizes bias variables is developed to obtain the individual alternative ranking results. Subsequently, an ordinal-cardinal consensus feedback adjustment mechanism is designed to refine the adjusted preference information of individuals following the attainment of group consensus in the CRP. This mechanism employs quantum probability theory (QPT) to effectively model belief transformation occurring during interpersonal interactions. Additionally, a maximum support degree model is proposed to obtain the final group alternative ranking result. Finally, a case study involving healthcare workers and patient sharing decision-making is illustrated, providing invaluable insights into the practicality and acceptability of drug treatment selection solutions within real-world contexts. This analysis aims to inform future advancements in shared decision-making practices.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"206 ","pages":"Article 111262"},"PeriodicalIF":6.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166668","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

Learning to optimize termination decisions under hybrid uncertainty of system lifetime and task duration 学习在系统寿命和任务持续时间混合不确定性下的终止决策优化

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-05-24 DOI: 10.1016/j.cie.2025.111208

Junqi Lu , Bosen Liu , Cuicui Pei , Qingan Qiu , Li Yang

{"title":"Learning to optimize termination decisions under hybrid uncertainty of system lifetime and task duration","authors":"Junqi Lu , Bosen Liu , Cuicui Pei , Qingan Qiu , Li Yang","doi":"10.1016/j.cie.2025.111208","DOIUrl":"10.1016/j.cie.2025.111208","url":null,"abstract":"<div><div>The lifetime distribution of engineering systems typically demonstrates significant heterogeneity, influenced by various factors such as material quality, manufacturing variations, usage intensity, and environmental conditions. Meanwhile, the distribution of random task durations can vary considerably, depending on resource availability, task complexity, and external disruptions. Accurately characterizing these heterogeneities is vital for improving the overall operational efficiency of engineering systems. This study explores optimal task termination decisions that effectively address the hybrid uncertainty stemming from the diverse distributions of system lifetimes and task durations. Utilizing a Bayesian statistical learning framework, the study models the uncertainties associated with random task durations and system lifetimes through unobserved distribution parameters. Bayesian parameter updating techniques are employed to derive posterior distributions for these parameters, informed by observed data collected during task executions regarding task durations and system lifetimes. By iteratively refining these parameters, the study dynamically determines the optimal task termination time. Furthermore, the properties of the optimal task termination decisions are investigated within a Markov Decision Process framework. A series of numerical examples are presented to validate the theoretical findings and highlight the practical implications of the proposed approach. The experimental results reveal a potential cost reduction of up to 45.11% compared to existing policies, emphasizing the efficacy and of the proposed methodology.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"206 ","pages":"Article 111208"},"PeriodicalIF":6.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166525","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 novel data-driven rule-base approach with driving factor decomposition for multi-scenario prediction on carbon emission reduction 基于驱动因子分解的多情景碳减排预测新方法

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-05-24 DOI: 10.1016/j.cie.2025.111217

Fei-Fei Ye , Rongyan You , Long-Hao Yang , Haitian Lu , Hongzhong Xie

{"title":"A novel data-driven rule-base approach with driving factor decomposition for multi-scenario prediction on carbon emission reduction","authors":"Fei-Fei Ye , Rongyan You , Long-Hao Yang , Haitian Lu , Hongzhong Xie","doi":"10.1016/j.cie.2025.111217","DOIUrl":"10.1016/j.cie.2025.111217","url":null,"abstract":"<div><div>Reducing carbon emissions is an ongoing goal of the whole world and its achievement requires an outstanding approach to accurately predict future carbon emissions and explore the factors driving carbon emissions. Hence, this study proposes a driving factor decomposition-based data-driven rule-base (DFD-DDRB) approach for the aim of analyzing carbon emission reduction pathway from predictive perspective, where the approach includes three processes: 1) generating a rule-base from historical carbon emission data; 2) predicting multi-scenario carbon emissions using the rule-base; 3) providing predictive analytics for future carbon emission reduction. In empirical study, the China’s provincial data from 2004 to 2021 are used to justify the applicability of the proposed approach. The experimental findings not only show that the approach can accurately predict multi-scenario carbon emissions until 2035 and reveal the factors driving carbon emissions, but also provide three implications for reducing China’s carbon emissions: 1) resource endowment should be considered to establish carbon emission management policies of 30 Chinese provinces; 2) economic development effect can be regarded as the main factor driving China’s future carbon emissions; 3) optimizing energy structure and consumption is much important for reducing China’s provincial carbon emissions. Beside the work in China, the DFD-DDRB approach can be also used as the generic analytical framework served for some developed economies and other carbon-emitting countries.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"206 ","pages":"Article 111217"},"PeriodicalIF":6.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166669","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

Using machine learning for production scheduling problems in the supply chain: A review 在供应链中使用机器学习解决生产调度问题：综述

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-05-24 DOI: 10.1016/j.cie.2025.111243

Khalid Ait Ben Hamou , Zahi Jarir , Selwa Elfirdoussi

{"title":"Using machine learning for production scheduling problems in the supply chain: A review","authors":"Khalid Ait Ben Hamou , Zahi Jarir , Selwa Elfirdoussi","doi":"10.1016/j.cie.2025.111243","DOIUrl":"10.1016/j.cie.2025.111243","url":null,"abstract":"<div><div>Supply Chain Management (SCM) faces significant complexities and challenges in its operational processes, particularly in production scheduling. These challengeshave been the subject of a great deal of research. Machine learning (ML) is widely and successfully used in various fields, including SCM, to help decision-makers cope with complex situations. This article provides a historical overview of research into the application of ML to production scheduling within SCM. It also discusses the major contributions, limitations and future directions of the field. This study shows that (i) the integration of ML algorithms with traditional optimization methods offers significant advantages in terms of flexibility and efficiency for solving complex scheduling problems; (ii) hybrid approaches combining ML techniques with heuristic and metaheuristic methods are particularly effective for dealing with dynamic and uncertain production environments; (iii) although reinforcement learning techniques dominate applications in this field, supervised and unsupervised learning algorithms also play an important role in improving the accuracy and performance of planning models; and (iv) the main limitations identified include dependence on high-quality data, computational complexity, complexity of model generalization, and the difficulty of adapting models to rapid and unforeseen changes in the production environment. Although ML algorithms provide promising solutions for optimizing scheduling processes in SCM, challenges persist, requiring ongoing research to enhance the efficiency, robustness, and interpretability of these systems. Future research should prioritize the development of more efficient hybrid methods, improvements in data quality, and the adaptability of ML models to diverse production environments.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"206 ","pages":"Article 111243"},"PeriodicalIF":6.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139568","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