Computers & Industrial Engineering最新文献_第8页

Software Defined Wide Area Networking for secure and resilient Unmanned Aerial Systems 用于安全和弹性无人机系统的软件定义广域网

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-12 DOI: 10.1016/j.cie.2025.111741

Tom Scully , Mark Robson , Mohanad Sarhan , Nour Moustafa , Javaan Chahl

Unmanned Aerial Systems (UAS), commonly referred to as drones, have evolved into sophisticated edge computing platforms capable of executing complex computational tasks and transmitting processed data through high-bandwidth communication links. The integration of advanced onboard compute resources and enhanced network connectivity has eliminated many traditional limitations, but it has also introduced new vectors for cyber threat actors to compromise the confidentiality, integrity, and availability of these systems. As a result, drones face heightened cybersecurity risks and a significantly expanded attack surface, increasing the probability of impactful cyber–physical incidents. This paper presents a comprehensive review of the current state of research in UAS security, revealing that existing solutions are still in a nascent stage of development. To address these gaps, we propose and validate a novel approach that leverages secure Software-Defined Wide Area Networking (SD-WAN) to enhance network resiliency, security, and performance for UAS. This work represents the implementation of a secure SD-WAN-based edge architecture for UAS, incorporating integrated sandboxing capabilities. The proposed architecture offers a technically robust and scalable foundation for securing unmanned aerial systems, with plans for future field trials to further validate its effectiveness in real-world environments.

无人机系统（UAS），通常被称为无人机，已经发展成为复杂的边缘计算平台，能够执行复杂的计算任务，并通过高带宽通信链路传输处理后的数据。先进的机载计算资源和增强的网络连接的集成消除了许多传统的限制，但它也为网络威胁行为者引入了新的载体，以破坏这些系统的机密性、完整性和可用性。因此，无人机面临着更高的网络安全风险和显著扩大的攻击面，增加了发生有影响的网络物理事件的可能性。本文全面回顾了无人机系统安全的研究现状，揭示了现有的解决方案仍处于发展的初级阶段。为了解决这些差距，我们提出并验证了一种利用安全软件定义广域网（SD-WAN）来增强UAS网络弹性、安全性和性能的新方法。这项工作代表了一种安全的基于sd - wan的无人机边缘架构的实现，结合了集成的沙盒功能。拟议的架构为确保无人机系统的安全提供了技术上强大和可扩展的基础，并计划在未来进行现场试验，以进一步验证其在现实环境中的有效性。

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引用次数: 0

A twin-reinforced evolutionary algorithm for flexible job shop scheduling problem under time-of-use tariffs 基于分时调度的柔性作业车间调度问题的双增强进化算法

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-12 DOI: 10.1016/j.cie.2025.111754

Shicun Zhao , Hong Zhou , Feng Chu , Da Wang , Kaizhou Gao

Global energy consumption is increasing substantially due to population growth and industrial transformation, with the industrial sector, characterized by high electricity usage, accounting for the largest share. Facing electricity supply constraints and time-varying demand, many governments implement time-of-use (ToU) tariffs to balance supply and mitigate peak loads. These tariffs introduce dynamic and period-dependent electricity costs, compelling industrial users to make trade-offs between operational efficiency and production costs. The flexible job shop scheduling problem (FJSP) is a widely adopted production model. To examine the impact of ToU tariffs on industrial operations, we investigate an FJSP variant with controllable processing speeds, sequence-dependent setups, and turn-on/off decisions under ToU settings (FJSPSS-ToU). A mathematical model is formulated to jointly optimize the production efficiency and total electricity costs, and its correctness is validated using CPLEX. To solve this problem, a twin-reinforced evolutionary algorithm (TREA) is introduced. TREA takes the evolutionary algorithm as its backbone and contains two reinforcement learning (RL) modules: an RL-guided parent-matching module that learns pairing utilities and an RL-assisted operator recommendation module that selects the most suitable operators. Moreover, a cost-saving strategy that combines a full-active decoding strategy with a right-shift operation is designed to reduce electricity cost. TREA is comprehensively evaluated against nine state-of-the-art algorithms, and the comparison results demonstrate its superior performance. Moreover, further experiments reveal that each learning module delivers measurable gains, and their organic integration makes the best contribution.

由于人口增长和工业转型，全球能源消耗正在大幅增加，其中以高用电量为特点的工业部门所占份额最大。面对电力供应的限制和时变的需求，许多政府实施分时电价来平衡电力供应和缓解高峰负荷。这些关税引入了动态的、随时间变化的电力成本，迫使工业用户在运营效率和生产成本之间做出权衡。柔性作业车间调度问题（FJSP）是一种被广泛采用的生产模型。为了检查ToU关税对工业运营的影响，我们研究了一个具有可控处理速度、序列依赖设置和在ToU设置下开/关决策的FJSP变体（fjsps -ToU）。建立了生产效率和总电力成本共同优化的数学模型，并利用CPLEX对其正确性进行了验证。为了解决这一问题，引入了一种双增强进化算法（TREA）。TREA以进化算法为主干，包含两个强化学习（RL）模块：一个是RL引导的父匹配模块，学习配对实用程序；一个是RL辅助的算子推荐模块，选择最合适的算子。此外，还设计了一种将全主动解码策略与右移操作相结合的成本节约策略，以降低电力成本。将TREA算法与9种最先进的算法进行了综合评价，对比结果表明TREA算法具有优异的性能。此外，进一步的实验表明，每个学习模块都可以获得可衡量的收益，并且它们的有机集成做出了最大的贡献。

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引用次数: 0

Multi-depot collaborative vehicle route problem with shared customer, delivery options and flexible transshipment 多仓库协同车辆路线问题，共享客户，交付选择和灵活转运

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-11 DOI: 10.1016/j.cie.2025.111755

Chao Li, Fan Yu, Qun Chen

This paper investigates a multi-depot collaborative vehicle routing problem with shared customers, delivery options, and flexible transshipment (MDCVRP-SCDOFT) in city logistics. In this problem, courier companies share delivery tasks and locker capacity, contributing to a more efficient logistics system while minimizing aggregate operating costs. We consider two customer types: those who prefer to collect their parcels from parcel lockers and those who require door-to-door delivery service. Parcel lockers serve dual purposes as both customer collection points and transshipment nodes (TNs). At TNs, couriers can deposit parcels for subsequent collection and delivery by couriers from different depots. We propose an adaptive large neighborhood search algorithm with embedded local search to solve this problem efficiently. Novel destroy and repair operators are developed by exploiting the problem structure, while existing operators from the literature are adapted accordingly. The proposed method is evaluated on benchmark instances derived from Solomon datasets and compared against genetic algorithm (GA), demonstrating superior performance. Furthermore, sensitivity analysis is conducted across instances of varying scales to derive valuable managerial insights.

本文研究了城市物流中具有共享客户、配送选择和灵活转运的多仓库协同车辆路径问题。在这个问题中，快递公司共享配送任务和储物柜容量，有助于提高物流系统的效率，同时最大限度地降低总运营成本。我们考虑两种客户类型：那些喜欢从包裹储物柜中收集包裹的客户和那些需要门到门送货服务的客户。包裹寄存柜有双重用途，既是客户收集点，也是转运点。在转运站，快递员可以寄存包裹，以便随后由不同仓库的快递员领取和派送。为了有效地解决这一问题，我们提出了一种嵌入局部搜索的自适应大邻域搜索算法。利用问题结构开发了新的破坏和修复算子，并对文献中已有的算子进行了相应的调整。在Solomon数据集的基准实例上对该方法进行了评估，并与遗传算法（GA）进行了比较，证明了该方法的优越性。此外，对不同规模的实例进行敏感性分析，以获得有价值的管理见解。

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引用次数: 0

A systematic approach to the sports scheduling problem for the Turkish professional football league 土耳其职业足球联赛运动调度问题的系统研究

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-10 DOI: 10.1016/j.cie.2025.111752

Eyüp Ensar Işık , Şebnem Demirkol Akyol , Adil Baykasoğlu

Scheduling of sports competitions has become an area of interest for researchers with the globalization of sports and its spread to large crowds. Round Robin tournament derivatives are generally applied among different types of tournaments, especially in leagues. This study aims to create schedules for the Turkish Professional Football League. Indicators such as the number of breaks, weighted carry-over effect, and specific requirements are considered as characteristic values reflecting the league’s quality. In this study, an integer programming (IP) model is developed to solve the scheduling problem, and it is observed that the IP model gives the optimum schedule for small-sized problems only. As a remedy, a two-phase heuristic solution procedure is proposed. The heuristic procedure first finds a pattern set and then constitutes the schedule. Different pattern sets and schedules are presented in the experimental results. The results show that the proposed heuristic method obtains the best schedules concerning the current schedule and the proposed IP models for various problem characteristics.

随着体育运动的全球化及其向人群的传播，体育比赛的日程安排已成为研究人员感兴趣的一个领域。循环赛衍生品通常适用于不同类型的比赛，特别是在联赛中。本研究旨在为土耳其职业足球联赛创建时间表。休息次数、加权结转效应、具体要求等指标被认为是反映联赛质量的特征值。本文建立了求解调度问题的整数规划（IP）模型，并且发现IP模型只对小规模问题给出最优调度。作为补救措施，提出了一种两阶段启发式求解方法。启发式过程首先找到一个模式集，然后构成时间表。实验结果显示了不同的模式集和时间表。结果表明，所提出的启发式方法得到了关于当前调度的最佳调度方案，以及针对各种问题特征所提出的IP模型。

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引用次数: 0

Opinion dynamics in large-scale group decision-making: considering multiple relationships and local-global interaction 大规模群体决策中的意见动态：考虑多重关系和局部-全局交互作用

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-10 DOI: 10.1016/j.cie.2025.111753

Tong Wu

This study proposes an opinion evolution model for large-scale group decision-making that considers multiple relationships and local–global interactions. By integrating multiple social relationships, a comprehensive network structure is constructed, and community detection is performed based on the nature of decision events for routine events, combining opinion similarity networks for non-routine events. The study finds that while multiple relationships provide richer connection information, they do not significantly shorten opinion convergence time, due to the more complex interactions that occur under multiple relationships. Besides, community opinions significantly influence the convergence value of overall opinions, especially in the presence of stubborn individuals. This research provides managerial implications for the rational utilization of group opinions and the prevention of malicious manipulation, thereby extending the applicability of traditional models and offering theoretical support for opinion management in various decision-making scenarios.

本研究提出了一个考虑多重关系和局部-全局交互作用的大规模群体决策意见演化模型。通过整合多种社会关系，构建综合网络结构，针对常规事件根据决策事件的性质进行社区检测，针对非常规事件结合意见相似网络进行社区检测。研究发现，虽然多重关系提供了更丰富的联系信息，但由于多重关系下发生的互动更为复杂，它们并没有显著缩短意见收敛时间。此外，社区意见显著影响整体意见的收敛价值，特别是在顽固个体存在的情况下。本研究为群体意见的合理利用和防止恶意操纵提供了管理启示，从而扩展了传统模型的适用性，为各种决策场景下的意见管理提供了理论支持。

引用次数: 0

Equitable power grid restoration considering decision maker’s risk tolerance 考虑决策者风险承受能力的公平电网恢复

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-10 DOI: 10.1016/j.cie.2025.111751

Behnam Sabzi , Gino J. Lim , Jian Shi , Saeedeh Abbasi

This paper examines power system restoration under uncertainties from climate hazards such as hurricanes, floods, and tornadoes. We propose a worst-case robust optimization model, formulated as a graph partitioning problem, to support restoration planning. Beyond minimizing load shedding cost and restoration time, the model integrates equity to ensure fair distribution of restored power. A conservativeness parameter allows decision-makers to adjust plans according to risk tolerance regarding uncertain transmission line status. The problem is structured as a bi-level, multi-objective mixed-integer program solved iteratively. The approach is tested on IEEE 14-bus and 39-bus systems. Results show that the equity-aware model outperforms benchmark models across multiple performance metrics, including average load shedding amount and percentage.

本文研究了在飓风、洪水和龙卷风等气候灾害的不确定性下电力系统的恢复。我们提出了一个最坏情况鲁棒优化模型，将其表述为图划分问题，以支持恢复计划。除了最大限度地减少减载成本和恢复时间外，该模型还集成了公平，以确保恢复电力的公平分配。保守性参数允许决策者根据不确定输电线路状态的风险承受能力调整方案。该问题被构造为一个双层、多目标的混合整数规划，可迭代求解。该方法在IEEE 14总线和39总线系统上进行了测试。结果表明，公平性感知模型在多个性能指标上优于基准模型，包括平均减载量和百分比。

引用次数: 0

Enhancing statistical process control with machine learning: The Iso-CUSUM control chart for multivariate process 用机器学习加强统计过程控制：多元过程的Iso-CUSUM控制图

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-10 DOI: 10.1016/j.cie.2025.111750

Marva Ajab , Babar Zaman , Faraz Mukhtiar , Naveed Razzaq Butt , Muhammad Iftikhar Faraz

Statistical process control (SPC) is a critical tool in quality control that ensures uniform production standards. Control charts (CCs) are fundamental tools in SPC, used to track process performance and detect out-of-control behavior in production outputs. The cumulative sum (CUSUM) CC is particularly effective for detecting small to moderate shifts in process parameters. While the normality assumption is often adopted for CC design, many real-world quality characteristics deviate from normality and may be high-dimensional or skewed, challenging the applicability of classical methods. The current study introduces

I s o -CUSUM

CC, a new type of CC that embeds the isolation forest technique (IsoForest) in the classical CUSUM framework for statistical process monitoring. Rather than assuming any underlying distribution, this method calculates anomaly scores from isolation trees to identify persistent shifts in process location parameters, making it especially effective for non-normally distributed data. CC thresholds are determined through Monte Carlo simulations and their efficiency is assessed using the median run length as a key performance metric. Its advantage is particularly notable for moderate and small shifts under both symmetric (multivariate normal) and heavy-tailed (multivariate

t

) distributions. The main contribution of this study is integrating IsoForest with CUSUM to create a robust, distribution-free CC that outperforms existing methods in detecting persistent process shifts. Finally, a real-life example is provided to demonstrate the practical applicability of the proposed

I s o -CUSUM

CC.

统计过程控制（SPC）是保证统一生产标准的质量控制的关键工具。控制图（cc）是SPC中的基本工具，用于跟踪过程性能和检测生产输出中的失控行为。累积和（CUSUM） CC对于检测过程参数的小到中等变化特别有效。虽然CC设计通常采用正态性假设，但许多现实世界的质量特征偏离正态性，可能是高维的或倾斜的，这对经典方法的适用性提出了挑战。本文介绍了一种将隔离森林技术（isofforest）嵌入到经典CUSUM框架中用于统计过程监测的新型CC——Iso-CUSUM CC。该方法不假设任何底层分布，而是从隔离树中计算异常分数，以识别进程位置参数的持续变化，这使得它对非正态分布的数据特别有效。CC阈值通过蒙特卡罗模拟确定，并使用中位数运行长度作为关键性能度量来评估其效率。在对称分布（多变量正态分布）和重尾分布（多变量t分布）下，其优势尤其明显。本研究的主要贡献是将isofforest与CUSUM集成在一起，创建了一个健壮的、无分布的CC，在检测持续进程转移方面优于现有方法。最后，通过实例验证了本文提出的Iso-CUSUM CC的实际适用性。

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引用次数: 0

Corporate social responsibility practices and quality performance among Chinese dairy companies: The mediating role of total quality management 中国乳业企业社会责任实践与质量绩效：全面质量管理的中介作用

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-09 DOI: 10.1016/j.cie.2025.111745

Yingxue Ren , Menghua Huang , Min Zhang , Zhen He

The continuous growing demand from stakeholders has driven more dairy companies to adopt Corporate Social Responsibility (CSR) to improve their performance. However, whether and how CSR is associated with quality performance (QP) of dairy companies is still unclear and lacks evidence. Building on the stakeholder theory and contingency theory, our study developed a conceptual framework with hypotheses that CSR is associated with dairy companies, with Total Quality Management (TQM) linking the two. A digital survey was design and conducted to collect data. 127 respondents from four dairy companies and 356 respondents from other 17 dairy companies were approached for pilot study and formal study respectively. The findings reveal that CSR is positively associated with both QP and TQM. Specifically, employee responsibility (EM) and customer responsibility (CU) are significantly positively associated with QP and TQM of dairy companies, indicating that effective engagement with both internal and external stakeholders is critical. TQM is linked to the association between CSR and QP, suggesting that the positive association between CSR and QP is strengthened when CSR is combined with TQM. An emerging trend toward shared responsibility in China’s dairy industry—combining CSR-driven stakeholder collaboration with TQM’s systemic quality control—demonstrates how actors across the dairy value chain can coordinate effectively, highlighting CSR–TQM integration as a promising direction for future research. Our findings enrich the CSR and quality-management literature with empirical evidence from the distinctive context of China’s dairy sector and offer managerial implications for firms and related stakeholders seeking to strengthen CSR and quality practices to ensure food safety.

利益相关者不断增长的需求促使越来越多的乳制品公司采取企业社会责任（CSR）来改善其绩效。然而，企业社会责任是否以及如何与乳制品公司的质量绩效（QP）相关联仍然不清楚，缺乏证据。在利益相关者理论和权变理论的基础上，我们的研究开发了一个概念框架，假设企业社会责任与乳制品公司有关，全面质量管理（TQM）将两者联系起来。设计并实施了一项数字调查来收集数据。对4家乳制品公司的127名受访者和其他17家乳制品公司的356名受访者分别进行了初步研究和正式研究。研究结果表明，企业社会责任与质量计划和全面质量管理呈正相关。具体而言，员工责任（EM）和客户责任（CU）与乳制品公司的QP和TQM显著正相关，表明与内部和外部利益相关者的有效参与至关重要。TQM与CSR和QP之间存在关联，表明CSR与TQM相结合时，CSR与QP之间的正相关关系得到加强。中国乳制品行业正在出现责任共分担的新趋势，将企业社会责任驱动的利益相关者合作与TQM的系统性质量控制相结合，展示了整个乳制品价值链的参与者如何有效协调，突出了企业社会责任与TQM的整合是未来研究的一个有前途的方向。我们的研究结果丰富了中国乳制品行业独特背景下的企业社会责任和质量管理文献，并为寻求加强企业社会责任和质量实践以确保食品安全的企业和相关利益相关者提供了管理启示。

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引用次数: 0

Untrained neural networks in data-scarce smart manufacturing: a paradigm for data-efficient industrial intelligence 数据稀缺智能制造中未经训练的神经网络：数据高效工业智能的范例

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-09 DOI: 10.1016/j.cie.2025.111744

Jiewu Leng , Jiahe Li , Qianwei Zhang , Xuyang Su , Bo Yang , Liang Guo , Qiang Liu , Xin Chen , Weiming Shen , Lihui Wang

The progression towards Industry 4.0 has intensified the need for intelligent systems in manufacturing. However, the prevailing deep learning paradigm is often hindered by its reliance on large, labeled datasets, which are frequently unavailable in industrial settings where defect data is inherently rare and costly to acquire. This review introduces Untrained Neural Networks (UNNs) as a powerful, data-efficient alternative. UNNs leverage the intrinsic architectural biases of neural networks, particularly Convolutional Neural Networks, as an implicit prior to solve complex problems without any pretraining. This approach, which requires no training and is exemplified by the Deep Image Prior (DIP) framework, enables tasks like image reconstruction and feature extraction using only a single data sample. This paper reviews the core principles of UNNs, including their inherent characteristics and their integration with physics-informed models and training-free Neural Architecture Search. We then detail their primary applications in solving industrial inverse imaging problems, such as computational microscopy and non-destructive testing, as well as in performing unsupervised anomaly detection. Furthermore, we analyze the distinct advantages of UNNs for smart manufacturing through the lens of the Industrial Internet of Things (IIoT). The review concludes by identifying key challenges, including reliability and scalability, and proposing future research directions focused on enhancing trustworthiness, developing specialized architectures, and enabling adaptation to dynamic industrial environments. This work highlights the significant potential of UNNs to lower the barrier for AI adoption, offering a flexible and cost-effective solution for industrial scenarios where data is scarce.

工业4.0的发展加剧了制造业对智能系统的需求。然而，主流的深度学习范式往往受到其对大型标记数据集的依赖的阻碍，这些数据集在工业环境中通常不可用，而工业环境中缺陷数据本质上是罕见的，并且获取成本很高。这篇综述介绍了未训练神经网络（UNNs）作为一种强大的、数据效率高的替代方法。unn利用神经网络（特别是卷积神经网络）固有的架构偏差，作为无需任何预训练即可解决复杂问题的隐式先验。这种方法不需要训练，并以深度图像先验（DIP）框架为例，仅使用单个数据样本即可实现图像重建和特征提取等任务。本文综述了unn的核心原理，包括其固有特征以及与物理信息模型和无训练神经结构搜索的集成。然后，我们详细介绍了它们在解决工业逆成像问题方面的主要应用，例如计算显微镜和无损检测，以及执行无监督异常检测。此外，我们通过工业物联网（IIoT）的视角分析了UNNs在智能制造方面的独特优势。报告最后指出了主要挑战，包括可靠性和可扩展性，并提出了未来的研究方向，重点是提高可信度，开发专业架构，并使其能够适应动态的工业环境。这项工作突出了unn在降低人工智能采用障碍方面的巨大潜力，为数据稀缺的工业场景提供了灵活且具有成本效益的解决方案。

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引用次数: 0

Inverse reinforcement learning driven cooperative optimization framework for carbon-efficiency integrated shop scheduling: A prefabricated building perspective 逆强化学习驱动的碳效率集成车间调度合作优化框架：预制建筑视角

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-08 DOI: 10.1016/j.cie.2025.111743

Zehua Fei , Yueyan Li

The existing manufacturing process of prefabricated buildings, which depends on long-distance material transport and energy-intensive processes, is unable to reduce the carbon emissions of the construction industry effectively. The distributed manufacturing process for prefabricated buildings, which is modeled as a carbon-efficient integrated distributed heterogeneous no-wait flow shop scheduling problem (CEDHFSP), is investigated to minimize makespan and total carbon emissions. The distributed production process is designed to alleviate the pressing contradiction between efficient production and reduced carbon emissions in prefabricated building manufacturing. An inverse reinforcement learning driven cooperative optimization framework (IRLCOF) is proposed in this paper to address CEDHFSP. The cooperative initialization method is designed to generate the initial population. The metaheuristic algorithm, inverse reinforcement learning, and Q-learning mechanism are introduced to explore solution space. The properties of CEDHFSP are summarized as knowledge employed in IRLCOF. The experiments are implemented to illustrate that the performance of IRLCOF outperforms the state-of-the-art algorithm. Specifically, IRLCOF is at least 20% better than other comparison algorithms for solving CEDHFSP in the Inverse Generational Distance metric.

现有的装配式建筑制造工艺依赖于长距离材料运输和能源密集型工艺，无法有效降低建筑行业的碳排放。将装配式建筑的分布式制造过程建模为碳效率集成分布式异构无等待流水车间调度问题（CEDHFSP），以最小化完工时间和总碳排放。分布式生产过程旨在缓解装配式建筑制造中高效生产与减少碳排放之间的紧迫矛盾。本文提出了一种逆强化学习驱动的协同优化框架（IRLCOF）。设计了协同初始化方法来生成初始种群。引入元启发式算法、逆强化学习和q -学习机制来探索解空间。将cehfsp的性质总结为IRLCOF中使用的知识。实验结果表明，IRLCOF算法的性能优于当前最先进的算法。具体来说，IRLCOF在求解逆代距度量中的CEDHFSP时比其他比较算法至少好20%。

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引用次数: 0