检索结果-内蒙古大学图书馆

Reentrant hybrid flow shop scheduling with stockers in automated material handling systems using deep reinforcement learning

引用

Computers and Industrial Engineering 2024年 189卷

作者： Lin, Chun-Cheng Peng, Yi-Chun Chang, Yung-Sheng Chang, Chun-Hsiang Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

In smart factories, automated material handling system (AMHSs) replace manual material handling to increase production efficiency, in which stockers serve as the temporary storage for work-in-process inventories. Furthermore, costly machines and complex processes in advanced manufacturing lead to the need for multiple reentrant processes of the same workstation. However, previous works on scheduling problems rarely considered the function of stockers and reentrant processes, and their approaches were mostly based on metaheuristic algorithms. Recent advances in artificial intelligence enable the possibility of solving scheduling problems using deep reinforcement learning (DRL). Therefore, this work investigates the reentrant hybrid flow shop scheduling problem with stockers (RHFS2) inspired by AMHSs, and solves it by DRL. Firstly, the states, actions, and rewards of the Markov decision process for this problem are designed;and then, two deep Q network (DQN) approaches based on the actions for determining machines and jobs, respectively, are proposed. Simulation results demonstrate that our proposed DQN approaches outperform for finding better solutions of different-scale problems than classical metaheuristic algorithms. © 2024 Elsevier Ltd

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Survey on Recent Applications of Artificial Intelligence and Optimization for smart Grids in smart manufacturing

引用

Energies 2023年第22期16卷 7660-7660页

作者： Hsu, Chao-Chung Jiang, Bi-Hai Lin, Chun-Cheng Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program in Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

To enable highly automated manufacturing and net-zero carbon emissions, manufacturers have invested heavily in smart manufacturing. Sustainable and smart manufacturing involves improving the efficiency and environmental sustainability of various manufacturing operations such as resource allocation, data collecting and monitoring, and process control. Recently, a lot of artificial intelligence and optimization applications based on smart grid systems have improved the energy usage efficiency in various manufacturing operations. Therefore, this survey collects recent works on applications of artificial intelligence and optimization for smart grids in smart manufacturing and analyzes their features, requirements, and challenges. In addition, potential trends and further challenges for the integration of smart grids with renewable energies for smart manufacturing, applications of 5G and B5G (beyond 5G) technologies in the SG system, and next-generation smart manufacturing systems are discussed to provide references for further research. © 2023 by the authors.

关键词： smart power grids

来源：评论

学校读者我要写书评

暂无评论

Joint green dynamic order batching and picker routing problem using PSO with global worst experience

引用

applied Soft Computing 2024年 154卷

作者： Lin, Chun-Cheng Peng, Yi-Chun Kang, Jia-Rong Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Management Tatung University Taipei104 Taiwan

With the increasing importance of various countries’ initiatives on reducing carbon emissions (e.g., carbon border adjustment mechanism, CBAM), intelligent order picking systems have assisted electronic retailers in realizing a green supply chain. Previous studies on picker routing operations focused on adopting offline static customer order information to carry out operational decisions. However, in practice, the customer order information is updated dynamically;and high-efficiency warehouse layouts have been increasingly concerned. Therefore, this study creates a mixed-integer programming model for minimizing both the total carbon footprint and the total penalty cost for delayed orders in the joint order batching and picker routing problem with dynamic arriving orders and shipping time constraints in a high-efficiency fishbone warehouse layout. To solve this complex problem, a particle swarm optimization algorithm that integrates the swarm's previous global and local worst experiences and a migration mechanism is further proposed to increase solution quality and computing efficiency. In addition, a three-dimensional space for the warehouse floorplan and shipping time constraints is designed to divide orders into batches to address the constraints. Theoretical analysis of this algorithm is conducted, and experimental analysis shows that this algorithm finds superior solutions than current practical strategies, to offer a valuable reference for carbon emission reduction in greening warehousing. © 2024 Elsevier B.V.

关键词： Carbon footprint

来源：评论

学校读者我要写书评

暂无评论

Optimal deployment of private 5G multi-access edge computing systems at smart factories: Using hybrid crow search algorithm

引用

Journal of Network and Computer Applications 2024年 227卷

作者： Lin, Chun-Cheng Deng, Der-Jiunn Hsieh, Li-Tsung Pan, Pei-Tzu Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Changhua University of Education Changhua500 Taiwan

In smart factories, an increasing number of mobile intelligent devices are deployed to meet the growing demands for flexible manufacturing. These devices, equipped with various sensors, synchronize a substantial amount of data with cloud servers for real-time monitoring and control. Fifth generation mobile networks (5G) combined with multi-access edge computing (MEC) provide the capabilities for multiple connections, large-scale data transmission, and efficient response times, becoming the mainstream network architectures for the needs. Additionally, with the emerging trend of energy-saving, recent works have addressed energy consumption as a significant cost factor. However, most previous studies tended to focus solely on cloud servers, neglecting energy consumption at edge computing and underestimating the impact of heterogeneous mobile device. As the scope of smart factory networks continues to expand, the challenges become increasingly critical. This study presents a private 5G MEC system architecture tailored for smart panel factories. The concerned problem is formulated as an integer programming model, which determines deployment locations and quantities for MEC servers and 5G small cells, including picocells and femtocells, so as to minimize the overall deployment costs while meeting the constraints of connectivity, capacity, latency, coverage, serviceability, and energy consumption. Since the edge server deployment problem is NP-hard, this study further proposes a hybrid metaheuristic algorithm, CSAVNS, which combines the strengths of crow search algorithm (CSA) and variable neighborhood search (VNS). In the global search phase of CSA, the VNS local search is introduced to enhance the algorithm's capabilities. Experimental analysis demonstrates that the proposed CSAVNS outperforms other algorithms in terms of solution solving capabilities. © 2024 Elsevier Ltd

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in smart Factories Using Dual Deep Q Networks

引用

Mobile Networks and Applications 2024年第3期29卷 886-904页

作者： Lin, Chun-Cheng Peng, Yi-Chun Chen, Zhen-Yin Annie Fan, Yu-Hong Chin, Hui-Hsin Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Technology Overseas Chinese University Taichung407 Taiwan

Flexible job shop scheduling (FJSP) has garnered enormous attention within the realm of smart manufacturing, where, beyond job sequencing, the selection of machines holds considerable importance. As smart factories progress with the Internet of things (IoT) and cyber-physical systems (CPS), scheduling methodologies are advancing towards intelligent decentralization. However, with the expansion of factories, conventional cloud computing struggles to manage the substantial influx of data. To tackle this issue, this work incorporates a fog computing and edge computing framework into the distributed FJSP workstations. In this framework, the workstations each of which consists of multiple machines are categorized based on the different nature of the accommodated machines, and operate independently to reduce unnecessary information transmission, in which each machine is equipped with edge computing capacity. The fusion of fog computing and edge computing allows for the offloading of computational tasks from cloud computing, effectively reducing latency. While previous solutions for FJSP have predominantly relied on linear programming or metaheuristic algorithms, this work proposed a novel distributed approach based on a dual deep Q networks (dual DQN) architecture, integrating deep learning (DL) with reinforcement learning (RL). Within the cloud center, the initial neural network determines the machine selection rules for fog computing, while the secondary neural network decides the job dispatching rules for edge computing devices. Edge computing devices execute the schedule and provide feedback to the cloud, which refines the results through an iterative training process, so that to minimize the makespan. The experimental findings indicate that employing dual DQNs outperforms the methods of utilizing only one single machine selection rule. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

引用

IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Assessing the ecosystem service values of tea plantations using conventional and organic farming methods: Is organic farming always better?

引用

Ecological Indicators 2024年 158卷

作者： Liu, Wan-Yu Chiang, Che-Yu Yap, Jia-Ler Lin, Chun-Cheng Department of Forestry National Chung Hsing University Taichung402 Taiwan Innovation and Development Center of Sustainable Agriculture National Chung Hsing University Taichung402 Taiwan Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of International Master Program in Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

Low-elevation mountainous (LEM) areas in some countries are reclaimed for agriculture (especially, for deep cultivation of tea trees) due to their accessibility, higher temperature, and more rainfall, contributing to biodiversity and ecological balance. However, extensive development in these areas causes lasting ecological damage;and hence it is crucial to evaluate the ecological value of these areas for formulating relevant ecological protection policies. Therefore, this study evaluated the ecosystem service value of tea plantations in LEM areas using the contingent valuation method based on the subjective consciousness of farmers, rather than an objective assessment. A questionnaire survey was conducted on tea farmers in Mingjian Township (which is one of the major tea-producing LEM areas in Mid-Taiwan);and ecosystem service values of tea plantations between conventional farming methods (CFMs) and organic farming methods (OFMs) were compared. Firstly, the differences in willingness to pay (WTP) for the samples including and excluding protest responses were investigated, and then a regression analysis to estimate the WTP price for maintaining the tea plantation ecology was conducted. Empirical results showed a significantly negative correlation between the WTP price and the education level of farmers in tea plantations using CFMs, but a significantly positive correlation in tea plantations using both CFMs and OFMs. Most respondents were male and used CFMs to operate tea plantations;and those with higher monthly income and using both CFMs and OFMs were willing to pay more. Regarding ecosystem services of tea plantations, the respondents preferred the "provisioning" ecosystem services, and were willing to pay an average of NT$ 718.92 and NT$ 794.32 per year for maintaining the ecology of tea plantations using CFMs and OFMs, respectively. The unit ecological value of tea plantations was not inferior to those of coniferous and broad-leaved forests, paddy fields, and a

关键词： Biodiversity

来源：评论

学校读者我要写书评

暂无评论

Enhancing urban climate resilience: A holistic evaluation of urban forest disservices in the aftermath of typhoons

引用

Urban Climate 2024年 54卷

作者： Liu, Wan-Yu Liu, Chen-Rui Lin, Chun-Cheng Department of Forestry National Chung Hsing University Taichung 402 Taiwan Innovation and Development Center of Sustainable Agriculture National Chung Hsing University Taichung 402 Taiwan Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Department of International Master Program in Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung 411 Taiwan

Ecosystem services associated with urban forests have gained increasing attention, but there has been a relative lack of focus on the corresponding ecosystem disservices, including financial burdens, air pollution, allergens, pests, diseases, and safety concerns. Situated in the northwest Pacific Ocean, Taiwan faces the highest global number of tropical cyclones. Typhoons significantly damage the urban forest ecosystem, especially impacting peripheral residents. Consequently, comprehensive evaluation of urban forest-related policies should also consider disservices induced by natural disasters. Understanding public perspectives on urban forests is crucial for affirming public support, and outcomes of this study can guide government agencies in crafting urban forest planning policies, minimizing risks and casualties during natural disasters. Survey results from Taichung City residents (the second-largest city in Taiwan), highlight key urban forest benefits: shade, picturesque scenery, and stress relief. Disruptions perceived include increased maintenance costs, hazards from broken branches, trunks, and road damage from tree roots. Three prominent ecosystem disservices from natural disasters are identified: power failures, casualties, and property losses from falling branches, and increased cleaning costs. To mitigate disservices and enhance urban forest resilience, recommendations include augmenting the budget for removing unhealthy urban forests, planting structurally stable ones, reinforcing underground cables, and increasing pruning budgets. © 2024 Elsevier B.V.

关键词： Ecosystem disservices Public perception Typhoon impact Urban forest management Urban forests

来源：评论

学校读者我要写书评

暂无评论

Organic solar cells: beyond 20%

引用

science China Materials 2025年 1-13页

作者： Ma, Ruijie Luo, Zhenghui Zhang, Youdi Zhan, Lingling Jia, Tao Cheng, Pei Yan, Cenqi Fan, Qunping Liu, Shengjian Ye, Long Zhang, Guangye Xu, Xiaopeng Gao, Wei Wu, Yue Wu, Jiaying Li, Yuxiang Liu, Yuhang Liu, Feng Song, Jiali Chen, Haiyang Chen, Weijie Zhang, Xin Liu, Yahui Yuan, Jun Liu, Quan Kan, Zhipeng Yin, Hang Li, Xiaojun Ma, Yunlong Deng, Dan Zhu, Lei Huo, Yong Fan, Baobin Fu, Huiting Liao, Xunfan Hu, Hanlin Li, Chao Yu, Runnan Hu, Huawei Yao, Zhaoyang Cai, Yunhao Qian, Deping Cui, Yong Yao, Huifeng Xu, Bowei Kan, Bin Gao, Ke Duan, Chunhui Hu, Xiaotian Sun, Huiliang School of Chemistry and Chemical Engineering Gannan Normal University Ganzhou 341000 China Department of Electrical and Electronic Engineering Research Institute for Smart Energy (RISE) Guangdong-Hong Kong-Macao (GHM) Joint Laboratory for Photonic-Thermal-Electrical Energy Materials and Devices The Hong Kong Polytechnic University 999077 Hong Kong Guangdong Provincial Key Laboratory of New Energy Materials Service Safety Shenzhen Key Laboratory of New Information Display and Storage Materials College of Materials Science and Engineering Shenzhen University Shenzhen 518060 China Key Laboratory of Advanced Green Functional Materials College of Chemistry Changchun Normal University Changchun 130032 China Key Laboratory of Organosilicon Chemistry and Material Technology Ministry of Education Zhejiang Key Laboratory of Organosilicon Material Technology College of Material Chemistry and Chemical Engineering Hangzhou Normal University Hangzhou 311121 China School of Optoelectronic Engineering Guangdong Polytechnic Normal University Guangzhou 510665 China College of Polymer Science and Engineering National Key Laboratory of Advanced Polymer Materials Sichuan University Chengdu 610065 China State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong University Xi’an 710049 China School of Chemistry Key Laboratory of Electronic Chemicals for Integrated Circuit Packaging South China Normal University (SCNU) Guangzhou 510006 China School of Materials Science and Engineering State Key Laboratory of Advanced Materials for Intelligent Sensing Key Laboratory of Organic Integrated Circuits Ministry of Education Tianjin Key Laboratory of Molecular Optoelectronic Sciences Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Tianjin 300350 China College of New Materials and New Energies Shenzhen Technology University Shenzhen 518118 China College of Chemical Engineering Sichuan University Chengdu 610065 China Xiame

Organic solar cells (OSCs) have experienced remarkable performance progress up to 20% benchmark power conversion efficiency (PCE) in past years. Considering the <1% initial PCE obtained by OSC decades ago, the milestone of surpassing 20% efficiency is of great significance. Meanwhile, further performance promotion is urgently required for OSCs as other solution-processable photovoltaic technologies are also competitive. This review article aims to demonstrate a comprehensive summary of recent reports on OSCs with over 20% PCE, delving into key strategies including material innovations, multi-component system construction, deposition protocol optimization, solid/solvent additive engineering, as well as hole/electron transport layer development. In addition, this study identifies the next-stage scientific and technological issues that warrant greater attention. These issues are proposed to drive more prosperous research development, particularly in the field of flexible and wearable power suppliers. (Figure presented.) © science China Press 2025.

关键词： device engineering molecule design opportunities and challenges organic solar cells power conversion efficiency

来源：评论

学校读者我要写书评

暂无评论

Exploring barriers to utilizing local agricultural products in the catering industry

引用

Agribusiness 2024年

作者： Wan-Yu Liu Hsing-Wei Lin Chun-Cheng Lin Department of Forestry National Chung Hsing University Taichung 402 Taiwan Innovation and Development Center of Sustainable Agriculture National Chung Hsing University Taichung 402 Taiwan Department of Agricultural Economics National Taiwan University Taipei 106 Taiwan Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu 300 Taiwan International Master Program in Smart Manufacturing and Applied Information Science National Chin-Yi University of Education Taichung 411 Taiwan

Both the proportion and expenditure of dining out and takeaway in Taiwan have been increasing year by year. This study investigates the factors influencing the catering service providers (CSPs) procurement of domestically produced or imported food materials. The empirical results showed that the procurement cost, quality, safety, and availability of ingredients were the major considerations for CSPs to purchase ingredients. Different types of CSPs had different cognitions, attitudes, and purchasing behaviors toward domestically produced and imported ingredients. Bento establishments, self-service cafeterias, and noodle shops adopted higher proportions of domestically produced ingredients (DPIs), and they also had more confidence in these ingredients. On the contrary, table service restaurants, all-you-can-eat restaurants, breakfast eateries, coffee shops, and fast food restaurants adopted higher proportions of imported ingredients. The results may be related to the food items purchased and catering services provided by different CSPs. Therefore, CSPs should be included in the scope and promotion of food and agriculture education so as to increase their emphasis on ingredients for health, increase their tendency to purchase DPIs, promote the disclosure of information on food producers and ingredients, and enhance the communications among food producers, buyers, CSPs, and dining-out consumers.

关键词： catering service provider dining out domestic agricultural products factor analysis stepwise regression analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：