Computers & Industrial Engineering最新文献_第3页

Built-in reliability-oriented R-KQC intelligent identification based on SA-HHO and proactive reliability assurance strategy 基于 SA-HHO 和前瞻性可靠性保证策略，内置面向可靠性的 R-KQC 智能识别功能

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110817

Xin Zheng , Yihai He , Zhiqiang Chen , Jiayang Li , Jing Lu , Shuang Yu

The integration of the built-in reliability (BIR) approach with reliability efforts from the design phase to the usage stage is crucial for ensuring the reliability of finished products. As an important carrier of product reliability, reliability-oriented key quality characteristics (R-KQCs) are present in all activities of the product life and are the core of BIR methods. Therefore, to improve the accuracy of identified R-KQCs from the big data of quality and reliability, a novel R-KQC intelligent identification method is proposed by adopting the simulated annealing-Harris hawk optimization (SA-HHO). First, the connotation of the BIR and R-KQC formation mechanism are introduced. Second, considering the large amounts of quality and reliability data, an identification method of R-KQCs is proposed based on the fuzzy PFMEA (Process Failure Mode and Effects Analysis) and SA-HHO algorithm. Third, on the basis of R-KQC identification, the assurance method of R-KQCs is proposed for proactive optimization of parameters and control of the process. Finally, an example of the shielding component reliability assurance is provided to verify the validity of the proposed method.

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引用次数: 0

Quantitative analysis of the U.S. chip embargo and China’s export controls on GaGe and graphite

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2025.110860

Lianbiao Cui , Yutao Jiang

In response to the U.S. chip embargo, China has proposed export controls on crucial materials like gallium, germanium, and graphite. However, few studies have explored the economic impacts of these trade sanctions policies. This study aims to address this gap by examining theoretical mechanisms and constructing a global input–output database for the chip, gallium-germanium, and graphite sectors. Using a dynamic computable general equilibrium model, we evaluate the dynamic economic impacts of Sino–U.S. technological competition and conduct robustness tests. The results show that in the initial stage of policy implementation, under the most extreme situation of chip embargo, the GDP of China, U.S., and the world decreases by 1.051%, 0.006%, and 0.201%, respectively; that of Japan, South Korea, and Chinese Taiwan, which follow the U.S. in implementing chip sanctions, decreases by 0.109%, 0.177%, and 0.330%, respectively. China’s export controls on crucial raw materials are shown to reduce national economic damage and have a large negative impact on Japan, South Korea, and Chinese Taiwan. Moreover, these negative impacts tend to worsen over time. Our findings reveal that Sino–U.S. technological competition is unfavorable to the economic interests of the two countries and poses challenges to global economic recovery in the post-pandemic era, indicating the importance of narrowing the gap and reducing the confrontation between China and the U.S. for global economic growth.

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引用次数: 0

Analysis and forecasting of the load of parcel pick-up points: Contribution of C2C e-commerce

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110770

Thi-Thu-Tam Nguyen , Adnane Cabani , Iyadh Cabani , Koen De Turck , Michel Kieffer

Second-hand shopping, primarily via online marketplaces, has rapidly increased during the last decade. Nowadays, consumers widely choose the Pick-Up Point (PUP) service to facilitate the delivery of products. Parcels related to this Customer-to-Customer (C2C) activity are dropped off in PUPs chosen by the sellers, shipped to PUPs selected by the buyers where they wait to be picked up. The increased impact of C2C parcels on PUPs requires an improved control of their load to reduce the risks of PUP overload, parcel rerouting, and resulting customer dissatisfaction.

This paper presents a forecasting approach for the load of PUPs receiving C2C parcels. The daily number of parcels dropped off with a given PUP as target is described by a Markov-Switching Auto-Regressive (MSAR) model to account for the non-stationarity of the second-hand shopping activity. A PUP Management Company, using this forecasting approach, is able to propose customers only target PUPs that are likely not to be overloaded at time of delivery. The proposed approach is compared to load prediction techniques involving SARIMA, Holt–Winters, LSTM, Prophet, and TiDE models. For the considered PUP, the load is predicted from one up to seven days ahead with mean absolute errors ranging from 5.5 parcels (1 day ahead) to 8.8 parcels (7 days ahead) for a PUP with an average load of 25 parcels. Similar results are shown for other PUPs.

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引用次数: 0

Blockchain-facilitated quality traceability and pre-sale inspection: Influencing geographical indication supply chain contracts and farmer quality decisions

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110769

Sensen Hu , Yishan Jin , Xinghong Qin

Geographical Indications (GIs) are increasingly prominent in the global economy, offering market leverage and propelling agricultural development. However, concerns arise that GI producers prioritize short-term gains over long-term quality assurance, leading to moral hazards and quality degradation. The prevalence of free-riding behavior within GI supply chains poses a collective action dilemma, significantly challenging the management of product quality.

In this paper, we explore the integration of blockchain technology and quality inspections into GI supply chains as potential solutions for establishing reliable and effective quality management measures. We develop a two-tier model involving one retailer and two farmers to analyze the effects of quality inspection and blockchain-based traceability on product quality and the handling of substandard products. By assessing the optimal profits of retailers and farmers under various quality management models, we ascertain the strategy that achieves a Pareto-efficient improvements.

Our research investigates the efficacy of blockchain-facilitated traceability and pre-sale inspection in shaping supply chain contracts, influencing quality decisions, and augmenting the overall value addition of blockchain integration. Despite the effectiveness of quality inspections in defect prevention, they fail to bolster overall earnings when implemented in isolation. Although the synergistic application of inspection and traceability substantially boosts GI supply chain profitability, our findings reveal that it does not increase farmers’ income. Interestingly, farmers are unlikely to prefer a singular reliance on quality inspection. Nevertheless, under specific market conditions, the adoption of blockchain presents a mutually beneficial scenario for both farmers and retailers. Based on these insights, we provide theoretical and practical guidance for optimizing quality management strategies in GI supply chains.

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引用次数: 0

A multi-item flexible-packaging model to minimise the cost of lost units and CO2 emissions for flexible flow shop scheduling 一种多项目柔性包装模型，用于最大限度降低柔性流水车间排程中的单位损失成本和二氧化碳排放量

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110806

Maria Jubiz-Diaz, Alcides Santander-Mercado, Carlos Granadillo-Diaz

Efficient production processes are crucial in satisfying customer requirements and achieving operational superiority. This implies synchronisation between links in a supply chain to adapt to dynamic market needs. Due to its complexity, flexible flow shop scheduling has attracted much concern from academics and practitioners. In addition, sustainability issues have gained attention since efficient energy management enhances economic competitiveness and ecological responsibility. However, research often integrates flexible flow shop scheduling with distribution, leaving aside other stages, such as finished product packaging. Therefore, this paper proposes a model to optimise scheduling and package sizing considering multiple products and package sizes to minimise the cost of lost units and carbon dioxide emissions. A genetic algorithm was developed to find high-quality solutions based on minimising the Mean Ideal Distance. An experimental design was conducted to determine the parameters influencing the algorithm’s performance. The results emphasised the impact of the crossover rate, mutation rate, and number of generations. Also, sensitivity analyses were performed to explore the relationship between the package types, inventory and lost units. Results highlighted the advantage of diverse package sizes in aligning dispatched final products with demand. Furthermore, a relation between inventory and lost units across demand levels was observed, underscoring the impact of overscheduling in production systems.

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引用次数: 0

Row and column-wise robust optimization model for biorefineries storing perishable biomass under weather uncertainty: Boosted by machine learning 天气不确定情况下生物精炼厂储存易腐生物质的行和列稳健优化模型：机器学习助推

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110823

Sobhan Razm , Nadjib Brahimi , Ramzi Hammami , Alexandre Dolgui

This study builds upon our earlier research (Razm et al., 2023). This study makes several contributions. First, we define three weather criteria (Rainfall, Temperature, Daylight hours) to incorporate weather conditions into the optimization model. We gather real weather data and conduct data preprocessing. Next, numerous calculations are performed based on the criteria to determine biomass availability ranges. Second, we immunize our system against uncertainties; however, uncertain parameters in our model possess specific features. Uncertainties exist both in the rows and columns. The traditional method cannot effectively address this issue. Therefore, we propose a row and column-wise robust optimization model to tackle weather and price uncertainties. Third, incorporating the aforementioned contributions into our previous model presents challenges. The new model is complex. Analyzing its behavior and interpreting results are challenging for this study. However, we conduct a series of numerical experiments and extract valuable managerial insights. Results show that despite incurring extra costs initially, the manager stands to gain more profit in the future, attribute to the system’s robustness. Finally, we enhance our model and increase system profitability by adopting data-driven robust optimization based on Machine Learning.

本研究以我们之前的研究（Razm 等人，2023 年）为基础。本研究做出了几项贡献。首先，我们定义了三个天气标准（降雨量、温度、日照时间），将天气条件纳入优化模型。我们收集了真实的天气数据，并进行了数据预处理。然后，根据这些标准进行大量计算，以确定生物量可用性范围。其次，我们将系统免疫于不确定性；然而，我们模型中的不确定参数具有特定的特征。行和列中都存在不确定性。传统方法无法有效解决这一问题。因此，我们提出了一种行列稳健优化模型，以应对天气和价格的不确定性。第三，将上述贡献纳入我们之前的模型是一项挑战。新模型非常复杂。对本研究而言，分析其行为和解释结果具有挑战性。不过，我们还是进行了一系列数值实验，并从中获得了宝贵的管理启示。结果表明，尽管最初会产生额外成本，但由于系统的稳健性，管理者在未来会获得更多利润。最后，我们通过采用基于机器学习的数据驱动稳健优化来增强我们的模型并提高系统的盈利能力。

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引用次数: 0

Multi-objective flexible flow-shop rescheduling with rigid–flexible hybrid constraints and preventive maintenance 具有刚柔混合约束条件和预防性维护的多目标灵活流动车间重新安排

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110813

Ziye Zhao , Xiaohui Chen , Youjun An

The synergy between production rescheduling and machine maintenance is critical, particularly in cases where unforeseen equipment failures, not fully prevented by maintenance, might threaten the viability of the original plan. In this context, this paper explores a novel integrated optimization problem of production rescheduling and preventive maintenance in a capacity-limited flexible flow-shop (CLFFS), in which random equipment failures, hybrid rigid–flexible constraints of buffer capacity and due window are considered. Specifically, (1) an integrated optimization model is established to minimize the makespan, average flow time, earliness

/

tardiness penalty, machine workload extreme deviation and system instability; (2) an adaptive hybrid rescheduling strategy (AHRS) that amalgamates three classical rescheduling approaches is designed to effectively respond to random equipment failures; and (3) an improved bi-population cooperative evolutionary algorithm with an adaptive environment selection mechanism (AES-IBCEA) is developed to deal with the integrated problem. In the numerical experiments, Taguchi method is first employed to set the parameters of the proposed algorithm. Second, the effectiveness and superiority of designed operators and proposed AES-IBCEA are validated through algorithm comparison. Next, the competitiveness of the proposed AHRS is demonstrated by contrasting it with other rescheduling strategies, and the average improvement rate is up to 22.12%. Finally, a sensitivity analysis on the fault impact threshold (

δ_{0}

) and the individual selection threshold (

β

) is performed, and the results reveal that

β

has a significant impact on the algorithm’s performance.

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引用次数: 0

Collaborative supervision of dangerous goods supply chain: A blockchain-based conceptual platform

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110818

Ao Wang , Guojun Zhu , Jian Li

Effective supervision and the prevention of concealment and false reporting in dangerous supply chain goods require collaboration among multiple departments. Because of its decentralized consensus mechanism, blockchain has potential as an efficient tool for the collaborative supervision of dangerous goods supply chains. In this study, a blockchain-based conceptual platform was proposed for the supervision of dangerous goods supply chains. It features multiple layers, namely, blockchain, storage, interaction, and interface, each fulfilling distinct roles to ensure a comprehensive and efficient system. To enable efficient on-chain data sharing of diverse types of data, three modes of on-chain data integration leveraging IPFS (InterPlanetary File System) and FISCO BCOS were implemented. Additionally, a traceable state machine was proposed, which uses smart contracts to facilitate the traceability of the supervision process. The feasibility of the conceptual platform was validated through the deployment of a prototype platform with eight blockchain nodes. The results indicate that the platform has a latency of 500–550 ms and approximately 2 TPS (Transactions Per Second) to complete data sharing. It also has a latency of less than 3 s and more than 1 TPS when carrying out complex supervision process tracing. The proposed conceptual platform has the ability to address data silo issues in the dangerous goods supply chain. Moreover, the traceability of the supervision process enhances the accurate tracing of accident liabilities.

对危险供应链货物进行有效监管，防止瞒报和谎报，需要多个部门的协作。区块链因其去中心化的共识机制，有望成为危险品供应链协同监管的有效工具。本研究提出了一个基于区块链的危险品供应链监管概念平台。该平台具有多个层次，即区块链、存储、交互和接口，每个层次都发挥着不同的作用，以确保系统的全面性和高效性。为了实现各种类型数据的高效链上数据共享，利用 IPFS（跨行星文件系统）和 FISCO BCOS 实现了三种链上数据集成模式。此外，还提出了一种可追溯状态机，利用智能合约促进监管过程的可追溯性。通过部署带有八个区块链节点的原型平台，验证了概念平台的可行性。结果表明，该平台完成数据共享的延迟时间为 500-550 毫秒，每秒交易量约为 2 TPS。在进行复杂的监管流程追踪时，其延迟时间也小于 3 秒，超过 1 TPS。拟议的概念平台有能力解决危险品供应链中的数据孤岛问题。此外，监管过程的可追溯性也提高了事故责任追查的准确性。

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引用次数: 0

Joint optimization of storage assignment and order batching in robotic mobile fulfillment system with dynamic storage depth and surplus items 具有动态存储深度和剩余物品的机器人移动履约系统中存储分配和订单分批的联合优化

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110767

Zhi Liu , Jiansha Lu , Chenhao Ren , Jun Chen , Zhilong Xu , Guoli Zhao

This paper studies the joint optimization of storage location assignment and order batching in robotic mobile fulfillment systems (RMFS), considering dynamic storage depth and surplus items. Firstly, a joint optimization model of storage location assignment and order batching is established. The model is divided into two stages. The first stage is the item location assignment optimization model, which is used to describe the types and quantities of items placed in each slot on each pod. The second stage is the joint optimization model of pod location assignment and order batching, which is used to describe the coordinates of each pod, the number of order batches, and the order combinations contained in each batch. Considering the vast solution space and the numerous constraints of the constructed model, a two-stage greedy variable neighborhood simulated annealing algorithm (TGVNSA) is introduced to address these challenges. Finally, numerical experiments prove that the algorithm can effectively solve the established model. TGVNSA is evaluated against two conventional methods: variable neighborhood search and adaptive genetic algorithms, focusing on metrics such as pod retrieval times, comprehensive picking costs, CPU time, and CQ value (comprehensive quality in the item location assignment). The findings demonstrate that TGVNSA boasts superior comprehensive performance. Compared with other commonly used strategies in the field such as random, classification, and optimal relevance, the method proposed in this paper demonstrates superior optimization performance, particularly when considering dynamic storage depth and surplus items. Moreover, this paper also proves that, under the same combination of strategies, the joint optimization method proposed in this paper reduces the comprehensive picking costs by 11.46% compared to the separate optimization approach.

本文研究了机器人移动履约系统（RMFS）中存储位置分配和订单分批的联合优化，并考虑了动态存储深度和剩余物品。首先，建立了存储位置分配和订单批量的联合优化模型。该模型分为两个阶段。第一阶段是物品位置分配优化模型，用于描述放置在每个 pod 上每个插槽中的物品类型和数量。第二阶段是吊舱位置分配和订单批次的联合优化模型，用于描述每个吊舱的坐标、订单批次的数量以及每个批次所包含的订单组合。考虑到所建模型的求解空间巨大、约束条件繁多，引入了两阶段贪婪可变邻域模拟退火算法（TGVNSA）来解决这些难题。最后，数值实验证明该算法能有效地解决所建立的模型。针对两种传统方法：可变邻域搜索和自适应遗传算法，对 TGVNSA 进行了评估，重点关注 pod 检索时间、综合拣选成本、CPU 时间和 CQ 值（物品位置分配的综合质量）等指标。研究结果表明，TGVNSA 具有卓越的综合性能。与该领域其他常用策略（如随机、分类和最佳相关性）相比，本文提出的方法表现出更优越的优化性能，特别是在考虑动态存储深度和剩余物品时。此外，本文还证明，在相同的策略组合下，本文提出的联合优化方法比单独优化方法降低了 11.46% 的综合拣选成本。

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引用次数: 0

Assembly line rebalancing problem with human-robot collaboration and a hyper-matheuristic solution approach

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110795

Aslihan Karas Celik, Feristah Ozcelik

In the context of the Industry 5.0 vision, which emphasises the importance of human-centred industries, assembly lines wherein humans and robots work together have emerged as innovative systems that allow the advantages of both to be combined. Accordingly, industrial managers are attempting to implement collaborative systems that will benefit from the consistency of robots and their capacity to work in hazardous environments, as well as the insight and adaptability of humans. Nevertheless, the process of eliminating an existing system and building another one from scratch is both costly and time-consuming. Rather than constructing an entirely new system, it is possible to reconfigure the line based on the new situation, thus enabling the system to adapt to changes. To the best of our knowledge, studies in the literature on collaborative assembly lines have focused on the initial installation phase, while the researchers who have dealt with the rebalancing process have not taken into account the change in the workforce structure as a reason for rebalancing. This study introduces the Assembly Line Rebalancing Problem with Human-Robot Collaboration as a means of filling the perceived gap in the literature. The considered problem addresses the need for line rebalancing to integrate traditional and collaborative robots as operators in existing manual assembly lines. In order to tackle this problem, a mathematical modelling approach and an artificial bee colony algorithm-based hyper-matheuristic algorithm are presented with the objective of optimising cycle time. The results of the computational tests on benchmark problems adapted from the literature demonstrate that the proposed algorithm outperforms the mathematical modelling approach and basic artificial bee colony algorithm.

工业 5.0 的愿景强调以人为本的工业，在这一背景下，人与机器人协同工作的装配线已成为一种创新系统，可将两者的优势结合起来。因此，工业管理者正试图实施协作系统，以利用机器人的一致性及其在危险环境中工作的能力，以及人类的洞察力和适应性。然而，淘汰现有系统并从头开始构建另一个系统的过程既昂贵又耗时。与其建造一个全新的系统，不如根据新情况重新配置生产线，从而使系统能够适应变化。据我们所知，有关协作式装配线的文献研究主要集中在初始安装阶段，而涉及重新平衡过程的研究人员并未将劳动力结构的变化作为重新平衡的原因加以考虑。本研究提出了 "人机协作装配线再平衡问题"，以填补文献中的空白。所考虑的问题涉及生产线再平衡的需求，以整合传统和协作机器人作为现有人工装配线的操作员。为了解决这个问题，提出了一种数学建模方法和基于人工蜂群算法的超智能算法，目的是优化周期时间。对文献中的基准问题进行的计算测试结果表明，所提出的算法优于数学建模方法和基本的人工蜂群算法。

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引用次数: 0