Computers & Industrial Engineering最新文献_第8页

The integration of shared renewable resources considering setup times for the parallel machine scheduling problem

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.2024.110828

Mohammad Shafiee , Mehdi Amiri-Aref , Walid Klibi

With the advent of Industry 5.0, the rise of advanced technologies, and the fast deployment of human–machine collaborative systems, there is a need for novel approaches to optimizing resources and increasing overall production efficiency. In this regard, we investigate a parallel machine scheduling problem (PMS) where various renewable resources switch among unrelated parallel machines during the operational plan. Each machine can be equipped with a specified number of resources, one at a time, on its left and right sides. We propose a mathematical model for the renewable-resource-constrained parallel machine scheduling problem that minimizes total setup times. We further incorporate realistic features such as non-simultaneous start times of machines, machine eligibility, and due date constraints for jobs. We propose a hybrid meta-heuristic algorithm to solve large instances by employing a novel constructive heuristic and the simulated annealing algorithm. Several instances are tested to validate the solution approach and underline its efficiency for large-sized ones. Through the case of a wiring harness manufacturer, we provide managerial insights on the benefit of either increasing the number of sharing renewable resources or increasing the number of machines in PMS problems, which can reduce total setup times by up to 19%.

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引用次数: 0

Principles on balancing divisional seru with cross-trained workers

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.2024.110793

Yalin Li, Zhe Zhang

This paper addresses the challenge of preventing blocking in seru production system (SPS), in which seru is a new-type manufacturing mode deriving from Japanese manufacturing practice and it can fast respond to the fluctuate market demands. Specially, the divisional seru with cross-trained workers is concerned since it is particularly susceptible to blockages due to the heterogeneity of workers. Self-balancing production lines, i.e., bucket brigades, is employed to maximize the throughput of divisional seru for various system sizes. A three-station and two-worker case and m-station and n-worker general case are discussed respectively, and for the small-scale divisional seru, the maximum throughput in a specific region is obtained; while for large-scale seru, the chaos of the system is demonstrated by analyzing the staffing and workload. The rules for reaching the maximum throughput are clearly provided, based on the fact that it is not possible to self-management, and therefore the maximum throughput can be achieved by the principles obtained in the study. Valuable insights into effectively managing divisional seru systems and optimizing the performance are provided, thereby offering practical guidance for the manager of SPS.

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引用次数: 0

Prediction of waterborne freight activity with Automatic identification System using Machine learning

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.2024.110757

Sanjeev Bhurtyal , Hieu Bui , Sarah Hernandez , Sandra Eksioglu , Magdalena Asborno , Kenneth N. Mitchell , Marin Kress

This paper addresses latency issues related to publicly available port-level commodity tonnage reports. To predict commodity tonnage at the port-level, near real time vessel tracking data is used with historical Waterborne Commerce Statistics (WCS) with a machine learning model. Currently, commodity throughput is derived from WCS data which is released publicly approximately two years after collection. This latency presents a challenge for short-term planning and other operational uses. To reduce latency, this study leverages near real time vessel tracking data from the Automatic Identification System (AIS) data set. Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Temporal Fusion Transformer (TFT) machine learning models are developed using the features extracted from AIS and the historical WCS data. The output of the model is the prediction of the quarterly volume of commodities (in tons) at the port terminals for four quarters in the future. Two types of models are developed: (i) uncategorized- a single model trained on all port terminals; (ii) categorized- four models (one per dominant vessel type at the port terminal, i.e., cargo, tanker, tug/tow, and mixed). The uncategorized model outperformed the categorized model based on the Mean Absolute Percentage Error (MAPE). The uncategorized LSTM model has the highest accuracy among all model types. Results show that the model has higher accuracy for port terminals that handle a specific type of vessel, compared to the port terminals that handle more than one vessel type. Six of seven commodity groups have a MAPE of less than 30% under the LSTM uncategorized model framework. The application of the model enables port authorities and stakeholders to make short-term capacity expansion and infrastructure investment decisions based on commodity volume.

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引用次数: 0

EWMA control chart framework for efficient Maxwell quality characteristic monitoring: An application to the aerospace industry

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.2024.110753

Zahid Khan , Aamir Saghir , Attila Katona , Zsolt T. Kosztyán

The Maxwell distribution is extensively employed in statistical modeling to analyze data, notably in the fields of chemistry, astrophysics, demography, actuarial sciences, economics, industry, and engineering. Recently, the application of the Maxwell data generating process (DGP) has focused extensively on the domain of statistical control charts. The

V

chart and

V_{S Q}

chart are often used to identify unexpected changes in the distributional shift of the Maxwell process. The

V_{S Q}

chart offers considerably better performance than the V chart for detecting moderate to large shifts in the scale parameter. However, the

V_{S Q}

chart adopts the fundamental structure of the Shewhart monitoring scheme and is insensitive to small alterations in the target parameter. We propose a new control chart, namely, the Maxwell exponentially weighted moving average (MXEWMA) chart, for improved monitoring of quality attributes that are assumed to conform to Maxwell data generation. The factors used to design the parameters of the proposed chart are computed at different false alarm probabilities and across various sample sizes. The effectiveness of the suggested scheme is considered in terms of the different features of the run length (RL) distribution, including the average, median and standard deviation. A comparative study of the MXEWMA chart with the existing

V_{S Q}

chart was performed across various sample sizes. The comparative analysis showed that the MXEWMA chart is an effective alternative and performs well in detecting reasonably small shifts in the parameter. Simulated data are employed to describe the computational procedure of the MXEWMA scheme. The simulation analysis demonstrated that the MXEWMA chart outperforms the existing method in identifying slight changes in the studied parameters. A real dataset is also considered to support the theoretical part of the work.

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引用次数: 0

A multi-state model for the service quality evaluation of an electric vehicle charging network via universal generating function 通过通用生成函数评估电动汽车充电网络服务质量的多状态模型

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.2024.110839

Zhonghao Zhao , Carman K.M. Lee , Xiaoyuan Yan

With the large-scale proliferation of electric vehicles (EVs), a comprehensive evaluation of service quality for EV charging networks is crucial to develop effective policies that address the needs and concerns of EV users and operators. This paper investigates the service quality evaluation (SQE) problem for EV charging networks consisting of multiple public EV charging stations (EVCSs). The charging service of each EVCS is formalized from the standpoint of both queuing management and power grid operation considering demand interaction and epistemic uncertainty, where the mean waiting time in the queue and loss of load probability (LoLP) are utilized as the evaluation metrics. A universal generating function (UGF)-based technique is employed to derive the service quality of the overall charging network based on the performance level of each EVCS from a multi-state perspective. A case study is conducted to assess the validity and feasibility of the developed model and explore the relationship between parameter selection and policymaking for the planning and operation of the charging network. The proposed method will be helpful for policymakers in formulating appropriate policies to enhance service quality, thereby further promoting the mass adoption of EVs and accelerating the transportation electrification process.

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引用次数: 0

Biofuel supply chain network design in competitive feedstock markets: An interactive possibilistic programming-based intelligent agent system

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.2024.110849

H. Gitinavard , V. Mohagheghi , M. Akbarpour Shirazi , S.M. Mousavi

Investing in renewable energy has been a vital aspect of different governments’ attempts to handle unwanted negative social and environmental impacts of fossil-based energy production. Biomass energy production is considered one of the proper renewable alternatives to replace fossil fuels. Therefore, the optimal design of biofuel energy production networks is an essential part of strategic decision-making to enhance social, environmental, and economic aspects of attempts to produce clean energy. This paper investigates various aspects impacting biorefinery supply chain network design. This includes the possible disruptions in feedstock production as well as, market competition and interactions that can affect feedstock prices. As a result, a mathematical model is developed that minimizes the costs of network design under uncertainty in feedstock demands. The model computes the required demand of feedstock as well as other key parameters that are important in this agent-based simulation process. This is done by developing an agent-based learning approach based on the Roth-Erev method, which considers agents involved in feedstock pricing and flows between the main elements of its supply chain. Then, the final price of feedstock and the tradeoffs among the agents are applied as inputs in the mathematical model to relocate each facility in its supply chain. The data is computed and updated in the mathematical model by using the agent-based approach until the locations of the facilities in two consecutive runs stay intact. The applicability and validity of the model are investigated through a case study presentation. Moreover, the existing literature is utilized to validate each part of the proposed approach.

投资可再生能源一直是各国政府试图解决化石能源生产对社会和环境造成的负面影响的一个重要方面。生物质能源生产被认为是替代化石燃料的适当可再生替代品之一。因此，生物燃料能源生产网络的优化设计是战略决策的一个重要组成部分，可以在生产清洁能源的过程中提高社会、环境和经济效益。本文研究了影响生物精炼厂供应链网络设计的各个方面。这包括原料生产中可能出现的中断，以及可能影响原料价格的市场竞争和相互作用。因此，本文开发了一个数学模型，可在原料需求不确定的情况下最大限度地降低网络设计成本。该模型可计算所需的原料需求量以及在基于代理的模拟过程中非常重要的其他关键参数。这是通过开发基于 Roth-Erev 方法的代理学习方法实现的，该方法考虑了参与原料定价的代理及其供应链主要元素之间的流动。然后，将原料的最终价格和代理之间的权衡作为数学模型的输入，以重新定位其供应链中的每个设施。通过使用基于代理的方法计算和更新数学模型中的数据，直到连续两次运行中的设施位置保持不变。该模型的适用性和有效性通过案例研究进行了探讨。此外，还利用现有文献来验证所建议方法的各个部分。

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引用次数: 0

An integrated simulation–optimization method for flexible assembly job shop scheduling with lot streaming and finite transport resources 具有批量流和有限运输资源的灵活装配作业车间调度的综合模拟优化方法

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.2024.110790

Hao Wang , Tao Peng , Xinyu Li , Junke He , Weipeng Liu , Renzhong Tang

Lot streaming (LS) in manufacturing enhances production efficiency but often leads to increased job waiting times and prolonged makespan when combined with assembly job shop scheduling. The practical constraint of finite transport resources (TRs) is prevalent in practice but is frequently overlooked or simplified. This study addresses the Flexible Assembly Job Shop Scheduling Problem incorporating LS and finite TRs (FAJSSP-LS-TRs), where jobs are divided into equal-sized sublots, each requiring specific TRs with limited capacity for movement between machines and assembly stations. To efficiently explore the complex interplay between time-sensitive machining, transportation, and assembly activities within a network of heterogeneous TRs, we introduce an integrated simulation–optimization approach to minimize the makespan, using a self-repair genetic algorithm to optimize LS strategy, job operation allocation, machine processing sequence, and TRs designation. A case study in a furniture manufacturing workshop yields several pivotal insights: Firstly, practical constraints like setup time and finite TRs significantly affect the benefits of job splitting for makespan reduction. Secondly, compared to a single lot sizing strategy, a mixed lot sizing (LS-Mix) strategy leads to makespan reductions across varying-sized problem instances. Thirdly, the LS-Mix strategy outperforms the approach of merely increasing the number of TRs. Finally, managerial insights are derived.

制造业中的批量流水作业（Lot streaming，LS）可以提高生产效率，但在与装配作业车间调度相结合时，往往会导致作业等待时间增加和生产周期延长。有限运输资源（TRs）这一实际约束在实践中非常普遍，但经常被忽视或简化。本研究探讨了包含 LS 和有限 TRs 的灵活装配作业车间调度问题（FAJSSP-LS-TRs），在该问题中，作业被划分为大小相等的子批次，每个子批次都需要特定的 TRs，而这些 TRs 在机器和装配工位之间的移动能力是有限的。为了有效探索异构 TRs 网络中对时间敏感的加工、运输和装配活动之间复杂的相互作用，我们引入了一种综合仿真优化方法，利用自修复遗传算法来优化 LS 策略、作业操作分配、机器加工顺序和 TRs 指定，从而最大限度地缩短生产周期。通过对家具制造车间的案例研究，我们获得了一些重要启示：首先，设置时间和有限的 TRs 等实际限制因素会极大地影响作业拆分对缩短生产周期的益处。其次，与单一批量大小策略相比，混合批量大小（LS-Mix）策略能在不同大小的问题实例中减少作业间隔。第三，LS-Mix 策略优于单纯增加 TR 数量的方法。最后，得出了管理启示。

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引用次数: 0

Product complexity management enabled by a model-based approach 通过基于模型的方法实现产品复杂性管理

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.2024.110776

Zhenchao Hu , Jinwei Chen , Yuanfu Li , Jinzhi Lu , Huisheng Zhang , Dimitris Kiritsis

As contemporary engineered systems become more interdependent, there is a growing need to manage their system complexity. However, system complexity only exists as implicit and heterogeneous information. This situation makes it difficult to obtain and analyze system complexity. This paper proposes a model-based approach to supporting system complexity management, called MBCM, where system complexity includes dynamic complexity and structural complexity. The proposed approach includes complexity modeling and complexity analysis. For complexity modeling, a graph-matrix hybrid complexity modeling approach is proposed to describe the dynamic and structural complexity information. Graph-based architecture models are developed by a unified meta-meta modeling approach. The connections among different architecture models are then described using a design structure matrix. For complexity analysis, a general complexity metric provides dynamic and structural complexity values for the designer to evaluate the system. Moreover, automatic ontology modeling is introduced to integrate complexity modeling and complexity analysis. Finally, a case study on aero-engine design was conducted. The complexity management processes of three different scenarios were compared in this case study. The comparison results show that our approach could sensitively represent increased product complexity from both dynamic and structural aspects. Thus, the proposed approach is general and applicable to any engineered system and can support the trade-off between complex product design schemes.

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引用次数: 0

Single- and multi-task linear models for ATMs fault classification in human-centered predictive maintenance

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.2024.110763

Riccardo Rosati , Luca Romeo , Adriano Mancini

The recirculator, a complex component within Automated Teller Machines (ATMs) responsible for handling banknotes, poses a challenging task for fault diagnosis due to its intricate nature, which renders it impractical to integrate dedicated sensors and potential multiple faults. This paper presents advanced single-task (STL-LR) and multi-task (MTL-LR) logistic regression models explicitly designed for capturing specific and similar discriminative patterns of multiple faults. Our approach focuses on maintaining the expert human operator at the center of the model checking and development process (human-in-the-loop approach). This objective has been achieved by including training data extracted from the intervention management platform, which collects the annotations of human operators. By leveraging this data, our STL and MTL models enhance generalization performance, especially in cases where discrepancies exist between machine-reported errors and technician-observed anomalies. The results illustrate the potential of the STL-LR and MTL-LR models as the main core of PdM DSS to aid technicians in accurately pinpointing fault-prone areas. This research contributes to Industry 5.0 by presenting a novel predictive maintenance approach that evolves task-specific learning to the generalization advantages of MTL. This evolution holds promise for fostering more efficient and effective maintenance strategies in complex equipment environments.

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引用次数: 0

Multi-Objective optimization of selective maintenance process considering profitability and personnel energy consumption

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.2025.110870

Guangdong Tian , Miao Wang , Jianwei Yang , Hongjuan Mi , Saif Ullah , Mohammed M. Aljuaid , Amir M. Fathollahi-Fard

Mechanical equipment naturally deteriorates and may malfunction during regular use, resulting in substantial financial losses and downtime. Regular maintenance can effectively address these issues. However, poor maintenance planning for products with numerous components often leads to inefficiencies for maintenance personnel, higher maintenance costs, and unnecessary resource consumption. Selective maintenance helps create effective maintenance programs under resource constraints, scientifically allocate maintenance resources, promptly repair faulty equipment, and sustain production activities. This study develops a multi-objective optimization model to enhance the efficiency of maintenance activities, avoid resource wastage, and increase maintenance revenue. This model optimizes the serial maintenance sequence by considering factors such as maintenance benefits, costs, personnel energy consumption, and resource constraints. Additionally, an improved metaheuristic algorithm, combining brainstorming optimization and large neighborhood search, is proposed to optimize the maintenance scheme for a specific type of carrier booster device system. Finally, an analysis of maintenance practices validates the applicability of the proposed model and algorithm, demonstrating their effectiveness in real-world scenarios.

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引用次数: 0