Computers & Industrial Engineering最新文献_第4页

Object segmentation dataset generation framework for robotic bin-picking: Multi-metric analysis between results trained with real and synthetic data 机器人拾取垃圾桶的目标分割数据生成框架：真实数据和合成数据训练结果之间的多度量分析

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-04-25 DOI: 10.1016/j.cie.2025.111139

Artur Cordeiro , Luís Freitas Rocha , José Boaventura-Cunha , Eduardo J. Solteiro Pires , João Pedro Souza

{"title":"Object segmentation dataset generation framework for robotic bin-picking: Multi-metric analysis between results trained with real and synthetic data","authors":"Artur Cordeiro , Luís Freitas Rocha , José Boaventura-Cunha , Eduardo J. Solteiro Pires , João Pedro Souza","doi":"10.1016/j.cie.2025.111139","DOIUrl":"10.1016/j.cie.2025.111139","url":null,"abstract":"<div><div>The implementation of deep learning approaches based on instance segmentation data remains a challenge for customized scenarios, owing to the time-consuming nature of acquiring and annotating real-world instance segmentation data, which requires a significant investment of semi-professional user labour. Obtaining high-quality labelled data demands expertise and meticulous attention to detail. This requirement can significantly impact the overall implementation process, adding to the complexity and resource requirements of customized scenarios with diverse objects.</div><div>The proposed work addresses the challenge of generating labelled data for large-scale robotic bin-picking datasets by proposing an easy-to-use automated framework designed to create customized data with accurate labels from CAD models. The framework leverages a photorealistic rendering engine integrated with physics simulation, minimizing the gap between synthetic and real-world data. Models trained using the synthetic data generated by this framework achieved an Average Precision of 86.95%, comparable to the performance of models trained on real-world datasets. Furthermore, this paper provides a comprehensive multi-metric analysis across diverse objects representing distinct industrial applications, including naval, logistics, and aerospace domains. The evaluation also includes the use of three distinct instance segmentation networks, alongside a comparative analysis of the proposed approach against two generative model techniques.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"205 ","pages":"Article 111139"},"PeriodicalIF":6.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878424","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

Large-scale group consensus decision making in social networks considering adverse selection in an asymmetric information environment 信息不对称环境下考虑逆向选择的社会网络大规模群体共识决策

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-04-25 DOI: 10.1016/j.cie.2025.111155

Yuxuan Chen , Yingming Wang

{"title":"Large-scale group consensus decision making in social networks considering adverse selection in an asymmetric information environment","authors":"Yuxuan Chen , Yingming Wang","doi":"10.1016/j.cie.2025.111155","DOIUrl":"10.1016/j.cie.2025.111155","url":null,"abstract":"<div><div>In consensus-based large scale group decision making (LSGDM) problems, some experts often exhibit adverse selection behavior due to asymmetry in information availability. This may lead to results deviating from the optimum, weakening decision making fairness and reducing consensus efficiency. For this reason, this paper proposes a large group consensus decision making method based on managing adverse selection behavior in an asymmetric information environment. Firstly, the directed Louvain algorithm is introduced to achieve the decision making subgroup division based on the directed social network. On this basis, considering the different qualifications and research fields of experts, a new weight allocation method is proposed based on the authority of experts. Next, focusing on the consensus-reaching process, a mechanism for identifying and managing adverse selection behaviors is proposed. A hierarchical recognition framework is designed for behavior identification, incorporating behavioral patterns and underlying motivations. A multidimensional dynamic adjustment strategy based on weight and preference is introduced for behavior management, then a comprehensive large-group consensus decision making method based on adverse selection behavior management is developed. Finally, the feasibility and effectiveness of the proposed method are verified using case studies and parameter discussions.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"205 ","pages":"Article 111155"},"PeriodicalIF":6.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887392","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 two-stage scheduling algorithm for dynamic interval multi-objective vehicle routing problem in medical waste collection 医疗废物收集中动态区间多目标车辆路径问题的两阶段调度算法

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-04-25 DOI: 10.1016/j.cie.2025.111136

Xiaoning Shen , Hui Lou , Zhongpei Ge

{"title":"A two-stage scheduling algorithm for dynamic interval multi-objective vehicle routing problem in medical waste collection","authors":"Xiaoning Shen , Hui Lou , Zhongpei Ge","doi":"10.1016/j.cie.2025.111136","DOIUrl":"10.1016/j.cie.2025.111136","url":null,"abstract":"<div><div>Proper scheduling of medical waste collection vehicles can reduce the cost of large-scale epidemic prevention and control, and improve the collecting efficiency. In this work, a multi-objective, multi-trip and multi-intermediate depot vehicle routing model for collecting medical wastes is developed, accounting for the uncertainty of vehicle speed and dynamic changes in customer requirements, as well as the differences in disposal capacity of various disposal sites. The cost and infection risk are minimized through the determination of the optimal collecting route and disposal site for each vehicle, while considering the constraints of vehicle capacity and number of vehicles. To solve the model, a novel two-stage scheduling method is proposed. In the stage of static optimization, a knowledge-guided interval multi-objective shuffled frog leaping algorithm is designed to obtain the initial collecting routes. The possibility degree of interval number is introduced to perform individual encoding and decoding for speed intervals, and also implement the interval non-dominated sorting. In the stage of dynamic optimization, a problem-specific neighborhood search method is adopted to provide a quick response to the dynamic collecting requirements. Systematic experimental studies are implemented on a real-world medical waste collection scenario and eight synthetic instances. Comparison results with state-of-the-art algorithms suggest that the proposed algorithm generates a set of interval non-dominated schedules with lower cost and infection risk.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"205 ","pages":"Article 111136"},"PeriodicalIF":6.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878423","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

Climate risk and green innovation in semi-conductor industry: Do supply chain concentration and resilience matter? 半导体产业的气候风险和绿色创新：供应链集中度和弹性重要吗？

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-04-24 DOI: 10.1016/j.cie.2025.111152

Shanyong Wang, Ling Ma

{"title":"Climate risk and green innovation in semi-conductor industry: Do supply chain concentration and resilience matter?","authors":"Shanyong Wang, Ling Ma","doi":"10.1016/j.cie.2025.111152","DOIUrl":"10.1016/j.cie.2025.111152","url":null,"abstract":"<div><div>Climate change presents a universal challenge affecting countries on a global scale. Given the exigencies imposed by extreme weather events and environmental degradation, corporations across diverse sectors have embraced green innovations (GI) as a strategy to address the imperatives of climate change adaptation and mitigation. This study examines the complex relationship between climate risks and corporate GI by analyzing data from 146 firms within the Chinese semiconductor industry over the period 2007 to 2022. Employing a combination of Poisson-Pseudo-Maximum-Likelihood (PPML), panel threshold, and moderating effect models, the analysis seeks to uncover the nuanced dynamics between environmental risks and corporate sustainability practices. The findings underscore that both annual and longer-term climate risks, as measured by the climate risk index (CRI), exert a significant promotional effect on GI. Additionally, the study reveals that CRI acts as a stimulus for GI by augmenting R&D intensity, attracting investments in green finance, and enhancing financial flexibility. Furthermore, the study incorporates an assessment of corporate resilience capacity through threshold analysis, shedding light on the pivotal nexus between CRI and GI. This nexus exhibits a pronounced positive association within the intermediate range delineated by two threshold values. Additionally, a moderating effect analysis, taking into consideration supply chain concentration and overall corporate production elements, underscores the reinforcing effect of both supply chain concentration and total factor productivity on the influence of CRI in promoting GI. The study also undertakes a comprehensive exploration of heterogeneity by considering various corporate and regional factors. In light of the findings articulated above, this research puts forth a series of recommendations tailored for managerial decision-making and the policy formulation process.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"205 ","pages":"Article 111152"},"PeriodicalIF":6.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878352","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

Energy-efficient and self-adaptive AGV scheduling approach based on hierarchical reinforcement learning for flexible shop floor 基于分层强化学习的柔性车间AGV节能自适应调度方法

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-04-24 DOI: 10.1016/j.cie.2025.111140

Xiao Chang, Xiaoliang Jia, Hao Hu

{"title":"Energy-efficient and self-adaptive AGV scheduling approach based on hierarchical reinforcement learning for flexible shop floor","authors":"Xiao Chang, Xiaoliang Jia, Hao Hu","doi":"10.1016/j.cie.2025.111140","DOIUrl":"10.1016/j.cie.2025.111140","url":null,"abstract":"<div><div>Driven by the recent trend of Industry 4.0, Automated Guided Vehicles (AGVs) have been widely applied in manufacturing industry to enhance the efficiency of the logistics system. However, the application of AGVs also aroused issues such as increasing energy consumption and various costs, especially in real-time AGVs scheduling in the complex flexible shop floor. To address these issues, a hierarchical reinforcement learning (HRL) based approach is hereby proposed to achieve real-time AGVs scheduling. At first, the scheduling task is decomposed into task assignment and AGV selection subtasks with the concept of hierarchy, and the problem of real-time AGVs scheduling is formulated as a Semi-Markov decision process (SMDP), aiming to simultaneously minimize makespan and total operational cost aroused by energy consumption, delay ratio, and maintenance. Then the HRL based real-time AGVs scheduling is presented to implement task assignment and AGV selection. In the end, a case study is illustrated to validate the effectiveness and superiority of the proposed approach. The results show that the maximum reduction of energy, maintenance, and total operational cost is 33.4%, 25.9%, and 24.1% respectively.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"205 ","pages":"Article 111140"},"PeriodicalIF":6.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892206","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 workers rotation in divisional seru production systems 示范分工生产系统中的工人轮岗

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-04-24 DOI: 10.1016/j.cie.2025.111141

Ashkan Ayough , Fatameh Sadeghi Nouri , Behrooz Khorshidvand , Farbod Farhadi

{"title":"Modeling workers rotation in divisional seru production systems","authors":"Ashkan Ayough , Fatameh Sadeghi Nouri , Behrooz Khorshidvand , Farbod Farhadi","doi":"10.1016/j.cie.2025.111141","DOIUrl":"10.1016/j.cie.2025.111141","url":null,"abstract":"<div><div>This study proposes a mathematical model for the job rotation problem in the Divisional <em>seru</em> Production System (DSPS) and develops an efficient solution algorithm. DSPS, a transitional phase toward a fully realized <em>seru</em> system, enhances flexibility and workforce adaptability in volatile manufacturing environments. A non-linear programming model optimizes maximum flow time in job rotation scheduling. Small-scale instances are solved using GAMS, while the Invasive Weed Optimization (IWO) <em>meta</em>-heuristic handles medium- and large-scale cases. Results show that IWO significantly outperforms GAMS in computation time while maintaining solution accuracy, with differences in objective values under 5% for most cases. Additionally, in 62.5% of cases, the number of assigned workers is fewer than the initial number of workers provided for each problem.</div><div>Randomly generated test instances validate the model and algorithm, confirming their effectiveness in reducing flow time and workforce requirements. Post-optimal trials indicate that the number of rotation periods can be adjusted to minimize the flow time. It was discussed that when the number of rotation periods is optimized to minimize the flow time, imbalances among cells are also minimized. This study fills a gap in the literature and provides new insights for optimizing DSPS.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"205 ","pages":"Article 111141"},"PeriodicalIF":6.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882403","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

Smart production strategies for economic growth and environmental sustainability 经济增长和环境可持续性的智能生产策略

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-04-24 DOI: 10.1016/j.cie.2025.111145

Lalremruati Lalremruati, Aditi Khanna

{"title":"Smart production strategies for economic growth and environmental sustainability","authors":"Lalremruati Lalremruati, Aditi Khanna","doi":"10.1016/j.cie.2025.111145","DOIUrl":"10.1016/j.cie.2025.111145","url":null,"abstract":"<div><div>This study examines sustainable strategies in a flexible production inventory system to reduce carbon emissions and enhance economic performance. As the need for sustainability in manufacturing intensifies, integrating green technologies and renewable energy sources into production processes emerges as a critical strategy for reducing environmental impact. One significant challenge is the disposal of defective products, which contribute to environmental degradation. To address this, the study proposes a defect management strategy that categorizes defective items into reworkable and recyclable products, offering an opportunity to reduce waste and generate additional revenue. By reprocessing these items into valuable outputs, this strategy not only mitigates environmental degradation but also supports sustainable business growth. A mathematical model is developed to optimize key factors such as selling price, green technology investment, flexible production rates, and production cycles, aiming to maximize overall profit while minimizing carbon emissions. Through in-depth numerical analysis, the study compares different scenarios to assess the economic and environmental benefits of the proposed strategy. The results show that failing to adopt renewable energy reduces profits by 6.53%, excluding defect management strategies leads to a 10.40% profit decrease, and not implementing green technologies results in an 11.31% profit loss. The combination of both defect management and green technologies yields a 12.64% reduction in profits. These findings underscore the significant potential of incorporating sustainable practices in manufacturing, offering a dual advantage of enhancing profitability while minimizing environmental impact.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"205 ","pages":"Article 111145"},"PeriodicalIF":6.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882404","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

Enhancing resource sharing in urban rail transit: a rolling stock sharing strategy for multi-line timetable optimization in cross-line operations 加强城市轨道交通资源共享：跨线运营中多线调度优化的车辆共享策略

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-04-24 DOI: 10.1016/j.cie.2025.111143

Huabo Lu , Yan Xu , Lishan Sun , Yue Liu , Xiaolei Ma , Jianfeng Liu

{"title":"Enhancing resource sharing in urban rail transit: a rolling stock sharing strategy for multi-line timetable optimization in cross-line operations","authors":"Huabo Lu , Yan Xu , Lishan Sun , Yue Liu , Xiaolei Ma , Jianfeng Liu","doi":"10.1016/j.cie.2025.111143","DOIUrl":"10.1016/j.cie.2025.111143","url":null,"abstract":"<div><div>Cross-line operation allows trains to travel between intersecting metro lines, which can provide direct travel for some transfer passengers, thereby alleviating transfer demands at transfer stations. Cross-line operation in urban rail transit allows trains to travel between intersecting lines, reducing transfer demands by enabling direct trips. However, existing studies focus on single-line optimization, lacking strategies for coordinated resource allocation across lines. This paper proposes a rolling stock sharing strategy to enable the sharing of train resources among different lines in cross-line operations. Taking the complex travel processes of both direct and transfer passengers into account, a mixed-integer nonlinear programming model (MINLP) is formulated to optimize train timetables and train resource allocation in cross-line operations, so as to minimize passengers’ waiting time and operating costs. A hybrid algorithm that combines a genetic algorithm, an adaptive large neighborhood search algorithm, and a train operation conflict elimination strategy is designed for the proposed model to find high-quality solutions. Finally, using Line 8 and the Changping Line of the Beijing Metro as a case study, results analysis and five sets of numerical experiments are conducted to prove the effectiveness of the proposed method. The experimental results demonstrate that the method can enhance train resource sharing among depots, improve transport efficiency, and reduce operating costs.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"205 ","pages":"Article 111143"},"PeriodicalIF":6.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924413","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

An integrated framework for managing vaccine supply chain shortages in the child immunization program of India 管理印度儿童免疫规划中疫苗供应链短缺的综合框架

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-04-24 DOI: 10.1016/j.cie.2025.111149

Dheeraj Chandra , Shweta , Amit Kumar Yadav , Vipul Jain

{"title":"An integrated framework for managing vaccine supply chain shortages in the child immunization program of India","authors":"Dheeraj Chandra , Shweta , Amit Kumar Yadav , Vipul Jain","doi":"10.1016/j.cie.2025.111149","DOIUrl":"10.1016/j.cie.2025.111149","url":null,"abstract":"<div><div>Ensuring a consistent supply of vaccines from manufacture to distribution, storage, and administration relies on efficient management of the Vaccine Supply Chain (VSC). However, in recent years, vaccine shortages have emerged as a major issue for vaccine producers and child immunization programs, especially in low- and middle-income countries, which hampers VSC overall performance. This study aims to address the ongoing problem of vaccine shortages in India’s child immunization program by identifying the main causes and investigating possible solutions to address the shortage issue. To do this, we propose an integrated framework that combines the Analytic Hierarchy Process (AHP) and Complex Proportional Assessment with Grey Theory (COPRAS-G) methodologies. This framework yields 12 potential solutions for the 10 issues causing shortages. We show that demand uncertainty is the primary cause of vaccine shortages and that a better monitoring system is necessary to detect and treat shortages in a timely manner. To validate the stability of the results, we run a Monte Carlo simulation using a uniform probability distribution on the interval [0, 1]. The results of this study will provide valuable insights for policymakers on how to effectively manage the vaccine shortage issue and improve the performance of child immunization programs.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"205 ","pages":"Article 111149"},"PeriodicalIF":6.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911404","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

Optimization of a closed-loop supply chain system considering government incentives mechanism under deep learning algorithms 深度学习算法下考虑政府激励机制的闭环供应链系统优化

IF 6.7 1区工程技术

Computers & Industrial Engineering Pub Date : 2025-04-23 DOI: 10.1016/j.cie.2025.111146

Jianquan Guo, Lian Chen, Zhen Wang

{"title":"Optimization of a closed-loop supply chain system considering government incentives mechanism under deep learning algorithms","authors":"Jianquan Guo, Lian Chen, Zhen Wang","doi":"10.1016/j.cie.2025.111146","DOIUrl":"10.1016/j.cie.2025.111146","url":null,"abstract":"<div><div>Against the backdrop of multiple uncertainties, our research endeavors to design a comprehensive closed-loop supply chain system that incorporates dual recycling channels and a series of manufacturing-remanufacturing processes. The focal point of this study lies in the establishment of a holistic profit model aimed at a thorough exploration of the effect of the government incentives mechanism (GIM) on this system. Additionally, we employ deep learning algorithms (DLA), a kind of AI technology, for calculation and solution analysis of the model. The results show: (1) Remanufacturers can develop reasonable recycling, remanufacturing, and manufacturing strategies based on different scenarios; (2) The quality level of recycled products, rather than different demands, has a significant impact on the amount of penalty and reward. Thus, when establishing a GIM to encourage recycling and remanufacturing, the government should primarily focus on the uncertainty of the recycled products’ quality. (3) In the absence of a GIM, remanufacturers are reluctant to strive to improve the recovery rate, and are more inclined to choose informal channels to reduce recovery costs; (4) The GIM can stimulate and regulate enterprises’ recycling activities. Hence, the government should formulate a reasonable GIM to regulate the recycling of enterprises and supervise and guide the transformation and upgrading of informal channels. This research provides profound insights for the establishment of a robust closed-loop supply chain under multiple uncertain environments, and also, this research combines AI technology to improve computational accuracy, provide more reasonable support for business decision-making and government supervision, and provide assistance in realizing a circular economy.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"205 ","pages":"Article 111146"},"PeriodicalIF":6.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882401","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