Sustainable Operations and Computers最新文献

Impacts of blockchain on recycling strategy in the presence of retailing platform under extended producer responsibility 生产者责任延伸下零售平台存在下b区块链对回收策略的影响

Sustainable Operations and Computers

Pub Date : 2026-01-01 DOI: 10.1016/j.susoc.2025.12.001

Xiukun Zhao , Fangfang Yang , Qianzhou Deng , Fangchao Xu , Jinhui Zou , Yu’e Wu

With the increasing popularity of extended producer responsibility (EPR) and blockchain implementation, new opportunities arise for recycling strategies between manufacturer and retailing platform in a closed-loop supply chain. We investigated the impact of blockchain on recycling strategies for retailing platform under EPR, using a Stackelberg game model that is manufacturer-led and platform-followed. Our results show that the implementation of Extended Producer Responsibility (EPR) and blockchain applications positively correlate with firm profits and social welfare when consumer green preferences and government subsidies are high, and the quality inspection time for remanufactured products and the cost of blockchain applications are relatively low. This correlation exists because government subsidies reduce the technological costs for retail platforms to implement blockchain, thereby incentivizing manufacturers to enhance their research and product quality. As a result, consumers are willing to accept higher prices for products, which in turn increases commission fees for retail platforms. Ultimately, this leads to increased total profits for both manufacturers and retail platforms, and government subsidies result in increased total social welfare.When government subsidies are endogenous, compared to the no subsidy and no blockchain (NN) model without EPR, the government green subsidy without blockchain (GSN) model under EPR, and the government green subsidy with blockchain (GSB) model under EPR, the government green and technology subsidy with blockchain (GTSB) model under EPR dominates in terms of total social welfare. When government subsidies are exogenous, under certain conditions, both the GSB and GTSB models exhibit superior social welfare. Our work highlights the driving effects of blockchain in facilitating manufacturer compliance with EPR, and suggests that to promote the enhancement of total social welfare, manufacturer and retailing platform should improve product quality and strengthen consumer trust in green products.

随着生产者责任延伸（EPR）和区块链实施的日益普及，在闭环供应链中，制造商和零售平台之间的回收策略出现了新的机会。我们使用制造商主导、平台跟随的Stackelberg博弈模型，研究了b区块链对EPR下零售平台回收策略的影响。研究结果表明，当消费者绿色偏好和政府补贴较高，再制造产品质量检验时间和应用区块链的成本较低时，实施生产者延伸责任（EPR）和区块链应用与企业利润和社会福利呈正相关。这种相关性的存在是因为政府补贴降低了零售平台实施区块链的技术成本，从而激励制造商加强研究和产品质量。因此，消费者愿意接受更高的产品价格，这反过来又增加了零售平台的佣金。最终，这导致制造商和零售平台的总利润增加，政府补贴导致社会总福利增加。当政府补贴是内生的，相对于没有EPR的无补贴和无区块链（NN）模型、EPR下没有区块链（GSN）模型的政府绿色补贴和有区块链（GSB）模型的政府绿色补贴，EPR下有区块链（GTSB）模型的政府绿色和技术补贴在社会总福利方面占主导地位。当政府补贴是外生的，在一定条件下，GSB和GTSB模型都表现出更优的社会福利。我们的研究突出了区块链在促进制造商遵守EPR方面的推动作用，并建议制造商和零售平台应提高产品质量，增强消费者对绿色产品的信任，以促进社会总福利的提高。

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引用次数: 0

Combating 3PCCL greenwashing with the blockchain technology under government enforcement 在政府的强制执行下，利用区块链技术打击第三方通信公司的“洗绿”行为

Sustainable Operations and Computers

Pub Date : 2026-01-01 DOI: 10.1016/j.susoc.2025.12.002

Lirong Wu, Yuan Guo, Xusheng Han

This study examines the role of blockchain technology in combating greenwashing within the context of Third-Party Cold Chain Logistics (3PCCL) under varying intensity of government enforcement. This paper explores the conditions under which 3PCCLs engage in greenwashing, the impact of such behavior, the strategic decision-making of supplier regarding the adoption of blockchain technology and its value considering the high cost and enhanced environmental awareness among consumers, and the environmental impact of greenwashing. We construct a game-theoretic model to analyze the interactions between a supplier, a 3PCCL, and a retailer, incorporating consumer preferences for freshness, government enforcement, and the cost of blockchain and enhanced environmental awareness, resulting in the following three key points: (1) Greenwashing by 3PCCLs diminishes supplier and retailer profits, reduces consumer surplus. (2) The adoption of blockchain is not uniformly beneficial to supplier. It is economically viable for supplier to adopt blockchain when the associated costs are low and when government enforcement is not stringent. Furthermore, the study also highlights the positive impact of blockchain on social welfare, as it not only bolsters supplier profits but also improves consumer surplus and overall societal benefits. (3) The negative environmental impacts of 3PCCL's engagement in greenwashing are significant. Additionally, it unveils legislative recommendations aimed at combating greenwashing and enhancing societal well-being.

本研究探讨了在政府执法力度不同的情况下，区块链技术在第三方冷链物流（3PCCL）背景下打击洗绿的作用。本文探讨了三氯联营企业进行绿洗的条件、绿洗行为的影响、供应商采用区块链技术的战略决策及其在高成本和消费者环保意识增强的情况下的价值、绿洗对环境的影响。我们构建了一个博弈论模型来分析供应商、第三方供应链供应商和零售商之间的相互作用，将消费者对新鲜度的偏好、政府执法、区块链成本和增强的环保意识结合起来，得出以下三个关键点：(1)第三方供应链供应商的“漂绿”减少了供应商和零售商的利润，减少了消费者剩余。(2)采用区块链并不一定对供应商有利。当相关成本较低且政府执法不严格时，供应商采用区块链在经济上是可行的。此外，该研究还强调了b区块链对社会福利的积极影响，因为它不仅增加了供应商的利润，还提高了消费者剩余和整体社会效益。(3)第三方通信公司参与“漂绿”的负面环境影响显著。此外，它还公布了旨在打击“洗绿”和提高社会福祉的立法建议。

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引用次数: 0

The role of quality governance in achieving sustainable development goals in North Africa: An integrated decision-support system 质量治理在实现北非可持续发展目标中的作用：综合决策支持系统

Sustainable Operations and Computers

Pub Date : 2025-01-01 DOI: 10.1016/j.susoc.2025.07.005

Muhammad Ikram, Chaymae Boudraa

North Africa is one of the most vulnerable regions to the impacts of climate change. Achieving the UN Sustainable Development Goals (SDGs) in North Africa requires effective governance, yet the governance factors influencing SDG progress remain underexplored. Therefore, this study examines the nexus between the Worldwide Governance Indicators (WGIs) of voice and accountability, political stability and violence, government effectiveness, regulatory quality, rule of law, and control of corruption and Sustainable Development Goals (SDGs) in North Africa. Previous research has insufficiently explored the relationship between global governance progress and the 2030 agenda. To bridge this gap, we applied an integrated Grey Relational Analysis (GRA) model to compute the ranks and weights the performance of North African countries in achieving the 17 SDGs from 2016–2022. The results show that Quality Education (SDG 4), Industry, Innovation and Infrastructure (SDG 9), Reduce Inequalities (SDG10), Sustainable Cities and Communities (SDG11), Responsible Consumption and Production (SDG12) and Peace Justice and Strong Institutions (SDG16) exhibited the highest performance in the region based on Second Synthetic Grey Relational Analysis Model (SSGRA). Additionally, a conservative maximin model was used to identify which North African country has the greatest impact of WGI on SDG outcomes. The findings indicate that Morocco faced the most significant challenges, particularly in achieving SDG 6 (clean water and sanitation). These results highlight critical implications for North African governments, emphasizing the need to prioritize (clean water and sanitation) SDG 6 and implement effective national strategies. This study recommends policymakers focus on governance reforms in lagging sectors and leverage data-driven frameworks to monitor and guide sustainable development interventions.

北非是最容易受到气候变化影响的地区之一。在北非实现联合国可持续发展目标（SDG）需要有效的治理，但影响可持续发展目标进展的治理因素仍未得到充分探索。因此，本研究考察了话语权和问责制、政治稳定和暴力、政府效率、监管质量、法治和腐败控制等全球治理指标与北非可持续发展目标之间的关系。以往的研究对全球治理进展与2030年议程之间的关系探讨不够。为了弥补这一差距，我们应用了一个综合灰色关联分析（GRA）模型来计算北非国家在实现2016-2022年17项可持续发展目标方面的表现排名和权重。结果显示，基于第二次综合灰色关联分析模型（SSGRA），优质教育（SDG 4）、产业、创新和基础设施（SDG 9）、减少不平等（SDG10）、可持续城市和社区（SDG11）、负责任的消费和生产（SDG12）、和平正义和强大的机构（SDG16）在该地区表现最好。此外，使用保守的最大值模型来确定哪个北非国家对可持续发展目标结果的全球指标影响最大。调查结果表明，摩洛哥面临着最重大的挑战，特别是在实现可持续发展目标6（清洁水和卫生设施）方面。这些结果突出了对北非各国政府的重要影响，强调了优先考虑（清洁水和卫生设施）可持续发展目标6并实施有效国家战略的必要性。本研究建议政策制定者关注落后部门的治理改革，并利用数据驱动的框架来监测和指导可持续发展干预措施。

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引用次数: 0

Data driven multi-objective optimization of sustainable aviation emergency network for forest fire rescue 数据驱动的森林火灾可持续航空应急网络多目标优化

Sustainable Operations and Computers

Pub Date : 2025-01-01 DOI: 10.1016/j.susoc.2025.04.001

Jun Huang , Qiuhong Zhao

Forest fires occur abruptly and can be detrimental, posing significant threats to human safety, forest resources, and the overall environment. Timely detection and effective response are important to fight against forest fires. Aviation emergency rescue plays an increasingly important role in forest fire response due to the characteristics of fast response speed, low terrain requirements and less fire site restrictions. At present, forest fire aviation emergency has been paid more attention in the world, however, current researches provide limited support to actual situation due to the lack of systematization and pertinently. In this paper, based on remote sensing information and other multi-party data, a two-stage multi-objective stochastic optimization model of sustainable aviation emergency network for forest fire rescue is presented. The proposed model aims to minimize the maximum of effective operational distance for aerial emergency rescue efforts and the total cost, which includes ecological losses caused by forest fires. To solve the model, an algorithm incorporating the NSGA-II algorithm and SAA method is proposed. Further, a case of Hainan Province in China is studied, guiding the application of the proposed theoretical methods. The findings demonstrate considerable value in addressing forest fires and safeguarding forest resources, thereby contributing to the sustainable development of both the environment and society.

森林火灾发生突然，可能是有害的，对人类安全、森林资源和整体环境构成重大威胁。及时发现和有效应对是扑救森林火灾的重要手段。航空应急救援具有响应速度快、地形要求低、火场限制少等特点，在森林火灾应急救援中发挥着越来越重要的作用。目前，森林火灾航空应急已受到国际社会的广泛关注，但由于缺乏系统性和针对性，目前的研究对实际情况的支持有限。基于遥感信息等多方数据，建立了森林火灾可持续航空应急网络的两阶段多目标随机优化模型。该模型的目标是使空中应急救援的最大有效操作距离和总成本（包括森林火灾造成的生态损失）最小。为了求解该模型，提出了一种结合NSGA-II算法和SAA方法的算法。最后，以海南省为例，对本文提出的理论方法的应用进行了指导。研究结果在解决森林火灾和保护森林资源方面显示出相当大的价值，从而有助于环境和社会的可持续发展。

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引用次数: 0

Enhancing circular economy through industrial symbiosis: An agent-based simulation analysis of supply chain dynamics 产业共生促进循环经济：基于agent的供应链动态仿真分析

Sustainable Operations and Computers

Pub Date : 2025-01-01 DOI: 10.1016/j.susoc.2025.03.002

Rebecca Fussone , Caterina Sammatrice , Salvatore Cannella , Roberto Dominguez

Industrial Symbiosis is recognized in the international context as a useful approach to move toward a Circular Economy. Here, the use of resources in industrial systems is improved, since waste and by-products generated by a firm are upcycled and used as input by other firms, reducing raw material extraction and waste sent to landfills. This work presents an analysis of the dynamic performance when an industrial symbiosis is implemented between two supply chains, where one represents the supplier of the other. Specifically, using the agent-based modeling simulation approach, the behavior and dynamics of the symbiotic supply chain are evaluated in different scenarios by adopting key performance indicators, such as the efficiency of symbiotic exchange, the environmental index, and the bullwhip effect. Based on our analysis, we provide useful managerial suggestions to create supportive and collaborative supply chains to support the Circular Economy.

工业共生在国际上被认为是迈向循环经济的有效途径。在这里，工业系统的资源利用得到改善，因为一家公司产生的废物和副产品被升级回收并作为其他公司的投入使用，减少了原材料的提取和送往垃圾填埋场的废物。这项工作提出了一个动态性能的分析，当一个工业共生是在两个供应链之间实施，其中一个代表另一个的供应商。具体而言，采用基于智能体的建模仿真方法，采用共生交换效率、环境指数和牛鞭效应等关键绩效指标，对不同场景下共生供应链的行为和动态进行了评估。根据我们的分析，我们提供了有用的管理建议，以创建支持性和协作性的供应链，以支持循环经济。

引用次数: 0

Improving supply chain responsiveness through the drivers of Industry 5.0: Sustainability implications for emerging economies 通过工业5.0驱动提高供应链响应能力：对新兴经济体的可持续性影响

Sustainable Operations and Computers

Pub Date : 2025-01-01 DOI: 10.1016/j.susoc.2025.10.003

Md. Hasin Arman, Ridwan Al Aziz, A. B. M. Mainul Bari, Chitra Lekha Karmaker, Sreedip Dasgupta

As industrialization and technological advancements accelerate, supply chain responsiveness has become crucial for global businesses. Industry 5.0 integrates human expertise with cutting-edge technologies such as machine learning, large-scale data analysis, and automation technologies, and creates new opportunities. However, while there has been a significant volume of research on Industry 5.0, less attention has been given to understanding and modeling the key drivers that influence the supply chain responsiveness, particularly in emerging economies. This study addresses this research gap by analyzing these drivers utilizing a mixed methodological approach. This study initially extracts sixteen primary drivers based on an extensive literature review and expert insights by utilizing a comprehensive methodology that integrates Pareto analysis, Modified Total Interpretive Structural Modeling, and Cross-Impact Matrix Multiplication Applied to Classification. Through Pareto analysis, this list is refined to the nine most influential and interconnected drivers. This analysis highlights the pivotal roles of "Big Data Analytics for supply chain cost optimization" and "Cloud Computing for dynamic supply chains" as the foremost drivers in enhancing supply chain responsiveness, especially from a sustainability standpoint. Moreover, "Application of the Internet of Things for real-time tracking," "Inventory control by using RFID," "Application of smart sensors for automation in SC," and "Artificial intelligence for smooth operation" are also identified as key drivers to supply chain improvement. When integrated, these drivers can facilitate a more resilient and adaptable supply chain, allowing businesses to leverage predictive analytics, real-time monitoring, and scalable demand-based responses. The insights obtained from this study can help managers and policymakers to pinpoint vulnerabilities within their supply chains and implement strategies to ensure sustainable and more responsive business operations.

随着工业化和技术进步的加速，供应链响应对全球企业来说变得至关重要。工业5.0将人类的专业知识与机器学习、大规模数据分析和自动化技术等尖端技术相结合，创造了新的机会。然而，尽管对工业5.0进行了大量研究，但对影响供应链响应能力的关键驱动因素的理解和建模的关注较少，尤其是在新兴经济体中。本研究通过使用混合方法分析这些驱动因素来解决这一研究差距。本研究基于广泛的文献综述和专家见解，利用综合方法提取了16个主要驱动因素，该方法集成了帕累托分析、修正的总解释结构模型和应用于分类的交叉影响矩阵乘法。通过帕累托分析，这个列表被细化为九个最具影响力和相互关联的驱动因素。该分析强调了“供应链成本优化的大数据分析”和“动态供应链的云计算”作为提高供应链响应能力的最重要驱动因素的关键作用，特别是从可持续发展的角度来看。此外，“应用物联网进行实时跟踪”、“使用RFID进行库存控制”、“应用智能传感器实现供应链自动化”、“人工智能实现顺畅运营”也被确定为供应链改善的关键驱动因素。当这些驱动因素集成在一起时，可以促进更具弹性和适应性的供应链，允许企业利用预测分析、实时监控和可扩展的基于需求的响应。从本研究中获得的见解可以帮助管理人员和政策制定者确定供应链中的脆弱性，并实施战略，以确保可持续和更具响应性的业务运营。

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引用次数: 0

Green logistics supply chain system based on B/S architecture and mileage saving algorithm 基于B/S架构和节省里程算法的绿色物流供应链系统

Sustainable Operations and Computers

Pub Date : 2025-01-01 DOI: 10.1016/j.susoc.2025.07.002

Dan Li

In response to the current needs of the logistics industry and the shortcomings of traditional logistics supply chain systems, this study adopts the B/S and front end/back end separation mode to construct the overall framework of the logistics supply chain system. The improved mileage saving algorithm is applied to solve the path optimization. Finally, a new type of green logistics supply chain system based on browser and server architecture and mileage saving algorithm is designed. The experimental results showed that the management module and distribution task module functional requirements of the green logistics supply chain system based on B/S and mileage saving algorithm achieved the expected results, which adapted to multiple browsers, with good universality. In addition, even in the case of a large number of users logging in simultaneously, the system proposed in the study maintained good stability, with a maximum stability of 93.68 % and a minimum running time of only 8 s. Compared with traditional logistics supply chain systems, the stability of the proposed system was improved by 12.16 %, and the running time was shortened by 21 s. In summary, the green logistics supply chain system based on browser and server architecture and mileage saving algorithm proposed in the study conforms to the logistics supply chain management in the 21st century, greatly promotes the development and efficiency of logistics supply chain management, and makes up for the shortcomings of management work.

针对当前物流业的需求和传统物流供应链系统的不足，本研究采用B/S和前端/后端分离模式构建物流供应链系统的整体框架。采用改进的节省里程算法求解路径优化问题。最后，设计了一种基于浏览器和服务器架构的新型绿色物流供应链系统。实验结果表明，基于B/S和节省里程算法的绿色物流供应链系统的管理模块和配送任务模块功能需求达到了预期效果，适应多种浏览器，具有良好的通用性。此外，即使在大量用户同时登录的情况下，本研究提出的系统也保持了良好的稳定性，最大稳定性为93.68%，最小运行时间仅为8 s。与传统的物流供应链系统相比，该系统的稳定性提高了12.16%，运行时间缩短了21 s。综上所述，本研究提出的基于浏览器和服务器架构的绿色物流供应链系统以及节余算法符合21世纪物流供应链管理的发展趋势，极大地促进了物流供应链管理的发展和效率，弥补了管理工作的不足。

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引用次数: 0

Guiding policy of green technology innovation and green consumption considering positive externality of products 考虑产品正外部性的绿色技术创新与绿色消费引导政策

Sustainable Operations and Computers

Pub Date : 2025-01-01 DOI: 10.1016/j.susoc.2025.10.002

Yanhong Ma , Hui Zhao , Haocong Ma

Green technology innovation (GTI) is the key path to promote green development and achieve China’s carbon peaking and carbon neutrality goals. Insufficient innovation motivation of enterprises and depressed market demand are still important reasons restricting GTI. The positive externalities of green products have significant impact on market demand and then the innovation motivation of enterprises. The optimized guiding policy should be proposed based on the consideration of products’ positive externalities. This paper divides green products into self-interested and altruistic. Then a tripartite evolutionary game model of government, enterprises and consumers is built. The impact of policy related parameters on evolutionary stable strategy (ESS) is analyzed, including subsidy and its distribution coefficient, carbon tax and its distribution coefficient. The results show that government can promote green behavior of enterprises and consumers through just implementing subsidy policies for self-interested products. While for altruistic products, government needs to implement subsidy and carbon tax policies simultaneously.

绿色技术创新（GTI）是推动绿色发展、实现中国碳峰值和碳中和目标的关键途径。企业创新动力不足、市场需求低迷仍然是制约GTI发展的重要原因。绿色产品的正外部性对市场需求产生显著影响，进而影响企业的创新动机。应在考虑产品正外部性的基础上提出优化的引导政策。本文将绿色产品分为自利型和利他型。在此基础上，构建了政府、企业和消费者三方博弈演化模型。分析了政策相关参数对进化稳定策略的影响，包括补贴及其分配系数、碳税及其分配系数。结果表明，政府可以通过对自利产品实施补贴政策来促进企业和消费者的绿色行为。而对于利他产品，政府需要同时实施补贴和碳税政策。

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引用次数: 0

Unraveling the obstacles to transform agro-wastes into value-added bioproducts: Implications for sustainability 消除将农业废物转化为增值生物产品的障碍：对可持续性的影响

Sustainable Operations and Computers

Pub Date : 2025-01-01 DOI: 10.1016/j.susoc.2025.05.002

Khandakar Shamit Raja, Ayesha Maliha, Sourav Paul, Md Shihab Shakur, A.B.M. Mainul Bari

The global population and economy heavily rely on agriculture. Various agricultural processes generate copious amounts of waste. Effectively managing these agricultural wastes, or agro-waste, is necessary to mitigate environmental pollution, reduce greenhouse gas emissions, and achieve carbon neutrality and sustainability. This has led to increased scientific interest in converting agro-waste into value-added bioproducts. However, the obstacles that hinder the adoption and implementation of this technology have still not been explored much. This study, therefore, employs a comprehensive novel integrated multi-criteria decision-making (MCDM) approach, combining the Interval-Valued Type 2 Intuitionistic Fuzzy (IVT2IF) theory with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to evaluate the obstacles. This approach aims to identify, rank, and analyze the interrelationships among the obstacles hindering agro-waste conversion into value-added bioproducts. Through literature review and expert validation, 17 obstacles were identified to be analyzed using the IVT2IF-DEMATEL method. The study’s findings suggest that "uncertainty about the return on investment (ROI) is the most influential obstacle, with a prominence value of 12.17, likely owing to its direct influence on investment decisions and the economic viability of agro-waste projects, followed by "lack of regulatory and legislative assistance" (12.02) and "necessity of a high initial investment" (11.77). The study’s findings have the potential to provide policymakers and decision-makers valuable insights for shaping investment decisions and developing effective long-term plans to facilitate and incentivize green agro-businesses that utilize agro-wastes and thus promote carbon neutrality and sustainable development in the agriculture-based industrial sectors.

全球人口和经济严重依赖农业。各种农业生产过程产生大量的废物。有效管理这些农业废物或农业废物对于减轻环境污染、减少温室气体排放以及实现碳中和和可持续性是必要的。这增加了科学对将农业废弃物转化为增值生物产品的兴趣。然而，阻碍采用和实施这项技术的障碍仍然没有被探索得太多。因此，本研究采用一种综合的新型集成多准则决策（MCDM）方法，将区间值型直觉模糊（IVT2IF）理论与决策试验与评估实验室（DEMATEL）方法相结合，对障碍进行评估。该方法旨在识别、排序和分析阻碍农业废弃物转化为增值生物产品的障碍之间的相互关系。通过文献回顾和专家验证，确定了17个障碍，使用IVT2IF-DEMATEL方法进行分析。研究结果表明，“投资回报率的不确定性是影响最大的障碍，其显著值为12.17，可能是因为其对投资决策和农业废物项目的经济可行性有直接影响，其次是”缺乏监管和立法援助“（12.02）和”需要高初始投资"（11.77）。研究结果有可能为政策制定者和决策者提供宝贵的见解，帮助他们制定投资决策和制定有效的长期计划，以促进和激励利用农业废弃物的绿色农业企业，从而促进以农业为基础的工业部门的碳中和和可持续发展。

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引用次数: 0

ML2MAS: a multi-agent reinforcement learning and BNNs-GAN integration framework for smart manufacturing optimization ML2MAS：用于智能制造优化的多智能体强化学习和BNNs-GAN集成框架

Sustainable Operations and Computers

Pub Date : 2025-01-01 DOI: 10.1016/j.susoc.2025.07.003

Shadia Yahya Baroud, Nor Adnan Yahaya

The rise of smart manufacturing, driven by advanced technological enablers, has transformed traditional production processes, creating new opportunities for operational efficiency and predictive maintenance (PdM). This study introduces ML2MAS, an innovative framework that integrates machine learning (ML) models within a multi-agent reinforcement learning (MAS-RL) system to enhance PdM capabilities in manufacturing environments. ML2MAS combines Bayesian Neural Networks (BNNs) for handling high-dimensional data and quantifying prediction uncertainty alongside Generative Adversarial Networks (GANs) for synthetic data generation, addressing the challenge of limited labelled datasets and improving model robustness. Integrating these components within MAS enables PdM to make decentralized, real-time decisions. Empirical results from a case study demonstrate substantial improvements, achieving a 99 % F1-score in predictive accuracy and notable reductions in maintenance costs. The proposed ML2MAS framework ensures a cohesive, adaptive PdM solution and contributes to more sustainable and efficient manufacturing operations.

在先进技术的推动下，智能制造的兴起改变了传统的生产流程，为运营效率和预测性维护（PdM）创造了新的机会。本研究介绍了ML2MAS，这是一个创新框架，将机器学习（ML）模型集成到多智能体强化学习（MAS-RL）系统中，以增强制造环境中的PdM能力。ML2MAS结合了贝叶斯神经网络（BNNs）处理高维数据和量化预测不确定性，以及生成对抗网络（GANs）合成数据生成，解决了有限标记数据集的挑战，提高了模型的鲁棒性。将这些组件集成到MAS中，使PdM能够做出分散的实时决策。案例研究的经验结果表明，该方法有了实质性的改进，在预测准确性方面达到了99%的f1分，并显著降低了维护成本。提议的ML2MAS框架确保了一个内聚的、自适应的PdM解决方案，并有助于更可持续和高效的制造运营。

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引用次数: 0