Computers & Industrial Engineering最新文献

英文中文

Spare part demand forecasting using PSO trained Quantile Regression Neural Network 利用 PSO 训练的量子回归神经网络预测备件需求

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.2024.110841

Chu-ge Wu, Xingchang Fu, Yuanqing Xia

The after-sales service is a crucial component within the supply chain. Rapid upgrade of electronic product parts leads to the inability of part suppliers to maintain production lines all the time. After-sale service centers need to forecast the volume of the required repair parts to satisfy the needs of customers. In this paper, the demand for spare parts is forecast by considering both regular monthly and Last Time Buy replenishment volumes until the end of the product warranty period. Given the proven effectiveness of Quantile Regression Neural Network (QRNN) and Recurrent Neural Network (RNN) in time-series forecasting, this paper suggests a hybrid network structure combining QRNN and RNN for forecasting spare part demand. Furthermore, an improved Particle Swarm Optimization (PSO) method is designed to optimize the network training process. Real-world cases involving different categories of spare parts consumption, where the results demonstrate the effectiveness of the tailored mechanisms, such as RNN structure and PSO-inspired network training. Moreover, our proposed algorithm demonstrates better performance compared to the state-of-the-art algorithms in terms of six standard point forecast error metrics.

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引用次数: 0

Group dynamic game under bounded rationality in agreed transfer of China’s carbon trading secondary market

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.2025.110857

Zhen Peng, Zitao Hong

Carbon emission allowance trading in the secondary market determines both the efficiency of carbon resource allocation and the effectiveness of carbon trading system. To reveal the mechanism of agreed transfer in China’s carbon emission allowance trading, this paper proposes a model of group dynamic game under bounded rationality, which integrates elements of both dynamic game and evolutionary game. It adopts the inverse induction method, using a nested payoff matrix, to recursively solve the game layer-by-layer from the inside out. Theoretical and experimental studies have shown that (1) The stable result is independent of the initial strategy of bounded rationality and depends on the comparative combinations of the profits of both parties in different stages; (2) When the agreed transfer concludes in the first stage, the total benefits of both parties are the largest, and the transaction efficiency is the highest; (3) The seller group achieves maximum gains when the initial bid is not excessively high, the buyer group’s discount factor is low, and the buyer group’s marginal return is substantial; (4) The buyer group can maximize its gains in first stage or second stage. Considering carbon market efficiency, it is advisable for the buyer group to choose the first stage when marginal return is high.

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引用次数: 0

Stakeholder engagement in carbon reduction engineering: A perspective analysis of production optimization leveraging social-media interactions

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.2024.110807

Zakaria El Hathat , V.G. Venkatesh , V. Raja Sreedharan , Tarik Zouadi , Yangyan Shi , Manimuthu Arunmozhi

This study investigates the complex dynamics of stakeholder engagement on social media platforms within the context of carbon reduction engineering. To shed light on this underexplored phenomenon, we gather a unique dataset of 6,940 Facebook-verified page posts, and we employ advanced data mining techniques to analyze the factors influencing stakeholder engagement. The findings demonstrate the significant impact of post characteristics on stakeholder engagement rates. Factors such as post length, hashtags, vividness level, hyperlinks, and the inclusion of call-to-action (CTA) play essential roles in shaping engagement patterns. Specifically, we find that shorter posts without hashtags tend to have lower engagement, while posts with moderate character counts, low vividness, and no hyperlinks often generate higher engagement. Additionally, our topic modeling analysis identifies critical themes discussed in carbon reduction engineering, including collaborative efforts among stakeholders, the role of academic institutions, renewable energy adoption, AI technology, and climate change mitigation. This, in turn, highlights the diverse perspectives and concerns of stakeholders actively engaged in these discussions. Our results significantly expand the literature on stakeholder theory, social interaction management, and the application of data mining techniques in analyzing social media engagement.

{"title":"Stakeholder engagement in carbon reduction engineering: A perspective analysis of production optimization leveraging social-media interactions","authors":"Zakaria El Hathat , V.G. Venkatesh , V. Raja Sreedharan , Tarik Zouadi , Yangyan Shi , Manimuthu Arunmozhi","doi":"10.1016/j.cie.2024.110807","DOIUrl":"10.1016/j.cie.2024.110807","url":null,"abstract":"<div><div>This study investigates the complex dynamics of stakeholder engagement on social media platforms within the context of carbon reduction engineering. To shed light on this underexplored phenomenon, we gather a unique dataset of 6,940 Facebook-verified page posts, and we employ advanced data mining techniques to analyze the factors influencing stakeholder engagement. The findings demonstrate the significant impact of post characteristics on stakeholder engagement rates. Factors such as post length, hashtags, vividness level, hyperlinks, and the inclusion of call-to-action (CTA) play essential roles in shaping engagement patterns. Specifically, we find that shorter posts without hashtags tend to have lower engagement, while posts with moderate character counts, low vividness, and no hyperlinks often generate higher engagement. Additionally, our topic modeling analysis identifies critical themes discussed in carbon reduction engineering, including collaborative efforts among stakeholders, the role of academic institutions, renewable energy adoption, AI technology, and climate change mitigation. This, in turn, highlights the diverse perspectives and concerns of stakeholders actively engaged in these discussions. Our results significantly expand the literature on stakeholder theory, social interaction management, and the application of data mining techniques in analyzing social media engagement.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110807"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Balancing economy, user guarantee, and carbon emissions towards sustainable natural gas pipeline network planning

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.2025.110858

Bingyuan Hong , Wei Gao , Ming Yang , Bin Zhou , Yangfan Lu , Jing Gong , Kai Wen

Natural gas pipeline network is an indispensable public infrastructure, and its reasonable construction is of great importance to national energy security. Natural gas pipeline network planning not only involves huge investment, but also affects environmental protection. Most importantly, it is also related to the guarantee of gas consumption of downstream social users. At present, the trade-off between economy, user guarantee and carbon emissions is not clear. In order to find a more environmentally friendly natural gas pipeline planning, this paper proposed a natural gas pipeline network planning method that comprehensively considers economy, user guarantee and carbon emissions to minimize the total cost of pipeline construction and operation, improve the gas guarantee of users and reduce carbon emissions. Taking the actual pipeline network in a certain area as an example, the trade-off between economy, user guarantee and carbon emissions in pipeline network planning is investigated by comparing the traditional single economic objective scenario with six multi-objective scenarios. Results show that considering all three factors can cut emissions by 89 % compared to focusing solely on economic goals, with user guarantee also improving. When prioritizing economic efficiency in a multi-objective scenario, costs decrease by 12.2 %–13.6 % compared to other scenarios, though user guarantee and emission reductions are lower. These findings highlight the inherent trade-offs among economy, user assurance, and emissions, with different priorities leading to varying outcomes. This research offers a method to achieve multi-objective planning, allowing for tailored solutions based on planners’ preferences.

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引用次数: 0

Assessing product and warranty sales: Impact of assessments and supply chains

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.2024.110758

Keang Zhang , Tao Zhang , Shang Gao

Extended warranties are widely utilized in our lives to help mitigate potential losses. They can be offered either by the manufacturer (in supply chain M) or by a platform (in supply chain R). The decision of a customer to purchase an extended warranty primarily depends on the perceived quality. Customers derive their perceived quality through online assessments of the product found on video-sharing platforms. However, both unbiased and biased online assessments can lead to varying perceptions among customers. In this paper, we propose four models based on two assessments (biased and unbiased) and two supply chain structures (supply chain M and supply chain R). Furthermore, to explore the endogenous nature of biased assessments, we investigate the sponsorship dynamics concerning online assessment bloggers. Key findings include: (1) Underestimation (overestimation) can lead to reduced (increased) profits for both the manufacturer and the platform in supply chain M. However, in supply chain R, underestimation (overestimation) may prove advantageous (detrimental) for the platform. (2) Under unbiased assessments, the optimal retail price of a product in supply chain R consistently surpasses that in supply chain M. With biased assessments, when the actual quality is relatively high, the manufacturer earns more in supply chain M, while the platform earns more in supply chain R. (3) Sponsorship costs escalate with higher actual quality, prompting the manufacturer to allocate a higher sponsorship budget compared to the platform. (4) The extended warranty provider is advised against sponsoring online assessment bloggers. Conversely, the party not providing the extended warranty is recommended for sponsorship of bloggers.

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引用次数: 0

Optimizing mixed-model assembly line efficiency under uncertain demand: A Q-Learning-Inspired differential evolution algorithm

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.2024.110743

Kai Meng, Shujuan Li, Zhoupeng Han

Modern manufacturing heavily relies on mixed-model assembly lines to streamline production processes for various product configurations. However, most existing research in this area primarily focuses on deterministic demand scenarios, leaving the challenges posed by uncertain demand relatively unexplored. Such uncertainty can significantly impact assembly line efficiency, resource utilization, and throughput rates. This paper explores the complexities of balancing and sequencing in mixed-model assembly lines, particularly under conditions of uncertain demand. The proposed approach includes a robust mixed-integer linear programming model formulated to optimize production efficiency across diverse scenarios characterized by uncertain demand. To address this complex problem, a novel Q-Learning-Inspired Differential Evolution Algorithm (QL-DE) has been developed. This algorithm utilizes a population-based evolutionary operator, an intra-population crossover operator, six task-centric and three product-centric neighborhood exploration operators, along with a Q-learning-inspired strategy. These components collectively enable the QL-DE algorithm to adaptively handle uncertain demand while optimizing assembly line processes. Finally, through a comparative analysis with five variants and five evolutionary algorithms, the QL-DE approach demonstrates its superior capability in efficiently addressing uncertain demand scenarios and optimizing the performance of mixed-model assembly lines.

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引用次数: 0

Net benefit-oriented condition-based maintenance for lithium-ion battery packs in SGLS systems: Combining degradation updating and decision-making

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.2024.110850

Mengzi Zhen , Zhen Chen , Biao Lu , Zhaoxiang Chen , Ershun Pan

Driven by global sustainability goals, the integration of renewable energy into power grids has significantly increased the demand for advanced battery management solutions. In source-grid-load-storage (SGLS) systems, effective operation and maintenance (O&M) of lithium-ion battery packs (LiBPs) are critical for balancing energy supply, ensuring operational reliability, and enhancing economic viability. However, existing maintenance strategies often fail to address the combined impacts of benefits, risks, and costs and instead rely on inflexible criteria, such as fixed failure thresholds, which are insufficient for managing batteries. Additionally, these strategies lack adaptability and do not incorporate real-time data, limiting their effectiveness in managing the stochastic dependence and inherent randomness of battery degradation. To address these limitations, this paper presents a dynamic condition-based maintenance (DCBM) strategy. This approach employs degradation modeling and parameters updating via a multivariate Wiener process, utilizing real-time data to refine decision-making. It introduces a novel net benefit-oriented model that integrates energy storage benefits, risk losses, and maintenance costs. By framing the problem as a Markov decision process (MDP), an improved algorithm is developed to optimize decisions throughout the battery’s lifecycle. Numerical analyses demonstrate that the proposed approach manages battery degradation uncertainties more effectively than traditional methods. This research provides an economically viable strategy for maintaining battery energy storage systems (BESSs), incorporating financial, safety, and maintenance considerations, thereby contributing to broader sustainability and efficiency goals.

{"title":"Net benefit-oriented condition-based maintenance for lithium-ion battery packs in SGLS systems: Combining degradation updating and decision-making","authors":"Mengzi Zhen , Zhen Chen , Biao Lu , Zhaoxiang Chen , Ershun Pan","doi":"10.1016/j.cie.2024.110850","DOIUrl":"10.1016/j.cie.2024.110850","url":null,"abstract":"<div><div>Driven by global sustainability goals, the integration of renewable energy into power grids has significantly increased the demand for advanced battery management solutions. In source-grid-load-storage (SGLS) systems, effective operation and maintenance (O&M) of lithium-ion battery packs (LiBPs) are critical for balancing energy supply, ensuring operational reliability, and enhancing economic viability. However, existing maintenance strategies often fail to address the combined impacts of benefits, risks, and costs and instead rely on inflexible criteria, such as fixed failure thresholds, which are insufficient for managing batteries. Additionally, these strategies lack adaptability and do not incorporate real-time data, limiting their effectiveness in managing the stochastic dependence and inherent randomness of battery degradation. To address these limitations, this paper presents a dynamic condition-based maintenance (DCBM) strategy. This approach employs degradation modeling and parameters updating via a multivariate Wiener process, utilizing real-time data to refine decision-making. It introduces a novel net benefit-oriented model that integrates energy storage benefits, risk losses, and maintenance costs. By framing the problem as a Markov decision process (MDP), an improved algorithm is developed to optimize decisions throughout the battery’s lifecycle. Numerical analyses demonstrate that the proposed approach manages battery degradation uncertainties more effectively than traditional methods. This research provides an economically viable strategy for maintaining battery energy storage systems (BESSs), incorporating financial, safety, and maintenance considerations, thereby contributing to broader sustainability and efficiency goals.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110850"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181824","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

Research on incentive strategies and cost-sharing mechanisms for cross-regional pollution control 跨区域污染控制的激励战略和成本分担机制研究

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.2024.110791

Min Song , Yongzeng Lai , Lin Li

Regional environmental collaborative governance is an effective way of addressing increasingly complex and severe environmental pollution. This study constructs a differential game model for regional collaborative governance consisting of the central government and two heterogeneous local governments. From a dynamic game perspective, we compare and analyze the game equilibrium solutions of each participant under five scenarios: noncooperation, vertical compensation, horizontal compensation, comprehensive compensation, and collaborative governance. In addition, this study constructs a dynamically consistent cost-sharing scheme that considers each participant’s fairness concerns. The results indicate that compared to non-cooperative situations, vertical, horizontal, and comprehensive ecological compensation mechanisms achieve a reduction in pollution emission levels and costs as well as an increase in emission reduction efforts in underdeveloped areas. Second, the effect of comprehensive ecological compensation is better than that of horizontal compensation, which is superior to the effect of vertical compensation. Third, compared to ecological compensation mechanisms, the collaborative governance model is more effective in pollution control, not only in improving central government intervention and the pollution reduction level of local governments, but also in reducing governance costs. Finally, the bargaining power and degree of fairness concern for each player can affect the cost-sharing ratio. Additionally, underdeveloped regions tend to form alliances with developed regions and negotiate with the central government to reduce pollution control costs. The research conclusions can provide a theoretical reference for improving ecological compensation mechanisms and strengthening the long-term mechanisms of regional collaborative governance.

{"title":"Research on incentive strategies and cost-sharing mechanisms for cross-regional pollution control","authors":"Min Song , Yongzeng Lai , Lin Li","doi":"10.1016/j.cie.2024.110791","DOIUrl":"10.1016/j.cie.2024.110791","url":null,"abstract":"<div><div>Regional environmental collaborative governance is an effective way of addressing increasingly complex and severe environmental pollution. This study constructs a differential game model for regional collaborative governance consisting of the central government and two heterogeneous local governments. From a dynamic game perspective, we compare and analyze the game equilibrium solutions of each participant under five scenarios: noncooperation, vertical compensation, horizontal compensation, comprehensive compensation, and collaborative governance. In addition, this study constructs a dynamically consistent cost-sharing scheme that considers each participant’s fairness concerns. The results indicate that compared to non-cooperative situations, vertical, horizontal, and comprehensive ecological compensation mechanisms achieve a reduction in pollution emission levels and costs as well as an increase in emission reduction efforts in underdeveloped areas. Second, the effect of comprehensive ecological compensation is better than that of horizontal compensation, which is superior to the effect of vertical compensation. Third, compared to ecological compensation mechanisms, the collaborative governance model is more effective in pollution control, not only in improving central government intervention and the pollution reduction level of local governments, but also in reducing governance costs. Finally, the bargaining power and degree of fairness concern for each player can affect the cost-sharing ratio. Additionally, underdeveloped regions tend to form alliances with developed regions and negotiate with the central government to reduce pollution control costs. The research conclusions can provide a theoretical reference for improving ecological compensation mechanisms and strengthening the long-term mechanisms of regional collaborative governance.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110791"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181753","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

MERCI: Multi-agent reinforcement learning for enhancing on-demand Electric taxi operation in terms of Rebalancing, Charging, and Informing Orders

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.2024.110711

Jiawei Wang , Haiming Cai , Lijun Sun , Binliang Li , Jian Wang

The development of intelligent transportation systems is being driven by the increasing electrification and the Internet of Things. On-demand electric taxis (OETs) are seen as a potential way to meet personalized travel needs and improve transport efficiency. While research is being done to create a multi-agent reinforcement learning (MARL)-based framework to achieve intelligent operation, there are still challenges to be addressed, such as the balance between exploration and exploitation, and the non-stationary issue. This study proposes an ensemble MARL framework to manage the daily operations of OETs, such as rebalancing, charging and informing orders. To address the non-stationary issue caused by the dynamic nature of operations, a demand awareness augmented architecture is proposed to use order information to make better decisions. Experiments using real-world data in Shenzhen show the emergence of intelligence of the proposed framework during operation and its superiority over traditional greedy methods. Additionally, ablation studies demonstrate that the proposed framework outperforms basic MARL architectures.

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引用次数: 0

Improving the X̄-control chart: A novel scheme based on runs and scans rules

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.2024.110852

Tribhuvan Singh , Nirpeksh Kumar

To enhance the performance of Shewhart-type control charts for detecting small to moderate shifts, various schemes based on runs and scans rules have been introduced in the literature. This paper introduces a novel scheme based on runs and scans statistics, known as the improved modified runs and scans rules scheme. The proposed runs and scans rules scheme has been applied to the

\bar{X}

-chart and its performance has been evaluated in terms of average run length

(ARL)

, standard deviation of run length

(SDRL)

and extra quadratic loss

(EQL)

. The results indicate that newly scheme outperforms the existing competitive runs and scans rules schemes. The effectiveness of the improved modified runs and scans rules scheme is demonstrated through a case study of a white millbase process.

引用次数: 0

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Computers & Industrial Engineering

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