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

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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

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

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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

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Smart cities and electrical and electronic waste management: a review of challenges and opportunities

Urbanization, Sustainability and Society

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Urbanization, Sustainability and Society 2025年第1期2卷 282-307页

作者： Deividson Sá Fernandes de Souza Simone Sehnem Patricia Guarnieri Thyago Celso Cavalcante Nepomuceno Fernando Henrique Taques Lucio Camara e Silva Post-Graduate Program in Production Engineering – CAA Federal University of Pernambuco Recife Brazil Department of Management Unoesc Chapecó Brazil and Department of Management Unisul Florianópolis Brazil Faculty of Economics Business Accounting and Public Policies Universidade de Brasília Brasília Brazil Technology Center Universidade Federal de Pernambuco Recife Brazil and Department of Computer Control and Management Engineering Antonio Ruberti University of Rome La Sapienza Rome Italy Post-Graduate Program in Production Engineering – CAA Federal University of Pernambuco Recife Brazil and Department of Applied Economics – ECONRES Research Group Universidad Autónoma de Madrid Madrid Spain

Purpose This paper aims to provide a comprehensive literature review on the practices and challenges in managing waste electrical and electronic equipment (WEEE) in smart cities. Design/methodology/approach A systematic literature review was conducted using the Methodi Ordinatio. Articles published between 2012 and 2022 were analyzed, totaling 149 references, of which 30 were included in the final review. Findings Emerging technologies such as the Internet of Things (IoT), big data and artificial intelligence (AI) are frequently highlighted as promising solutions for efficient e-waste management. Governance models and public policies are widely recognized as crucial for the successful implementation of WEEE management practices in smart cities. Originality/value This study underscores the role of advanced technologies, such as IoT and AI, in enhancing urban mobility and WEEE management. Key challenges include information security, privacy, interoperability, costs and sustainability. The findings reveal a convergence between smart cities and WEEE management, fostering the circular economy and the recovery of valuable materials.

关键词： Smart cities WEEE Systematic literature review Methodi Ordinatio

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The Impact of Innovation, Circular Economy and Sustainable Development Strategies Aimed at Low-carbon Economy on Brazilian Business Performance

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Circular Economy and Sustainability 2025年第2期5卷 777-796页

作者： Severo, Eliana Andréa Dorion, Eric Charles Henri Master’s Program in Management Innovation and Consumption (PPGIC) at UFPE (Campus Caruaru) Department of Administrative Sciences (DCA/UFPE) Center for Applied Social Sciences (CCSA/UFPE) Federal University of Pernambuco (UFPE) Av. Professor Moraes Rego PE Recife 1235 50670-901 Brazil IT Engineering Department École de Technologie Supérieure (ÉTS) 1100 Notre-Dame St W Montreal H3C 1K3 QC Canada

The Covid-19 Pandemic ended up having a negative impact on any organisations, as there was a slowdown in the economy and a jobs’ reduction, however, the environment also suffered immeasurable influences. In this context, this study analyzes the relationships between product and process innovations, the circular economy and sustainable development strategies aimed at low-carbon economy and their impact on Brazilian business performance. This descriptive research has been brought through the application of a survey submitted to 1,188 managers from Brazilian Brazilian National Confederation of Industry member companies. It has processed a multivariate data analysis for its investigation and interpretation. The results indicate that the product and process innovations, practices from the circular economy and sustainable development strategies have an impact on the low-carbon economy, and they show a positive influence on organizational performance. Their social and economic implications enhance the creation of an organizational culture that have a direct influence on the use of low-carbon strategies. In this perspective, this study contributes to the improvement in the quality of managerial decisions, where their practical and managerial contributions confirm the increase of organizational competitiveness, by generating a prescriptive inference and a useful theoretical base for organizational and governmental actions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Brazilian business performance Circular economy Innovation Low-carbon economy Sustainable development

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Simulation-Free Determination of Microstructure Representative Volume Element Size via Fisher Scores

arXiv

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arXiv 2024年

作者： Liu, Wei Mojumder, Satyajit Liu, Wing Kam Chen, Wei Apley, Daniel W. Department of Industrial Engineering and Management Sciences Northwestern University EvanstonIL60208 United States Theoretical and Applied Mechanics Program Northwestern University EvanstonIL60208 United States Department of Mechanical Engineering Northwestern University EvanstonIL60208 United States

A representative volume element (RVE) is a reasonably small unit of microstructure that can be simulated to obtain the same effective properties as the entire microstructure sample. Finite element (FE) simulation of RVEs, as opposed to much larger samples, saves computational expense, especially in multiscale modeling. Therefore, it is desirable to have a framework that determines RVE size prior to FE simulations. Existing methods select the RVE size based on when the FE-simulated properties of samples of increasing size converge with insignificant statistical variations, with the drawback that many samples must be simulated. We propose a simulation-free alternative that determines RVE size based only on a micrograph. The approach utilizes a machine learning model trained to implicitly characterize the stochastic nature of the input micrograph. The underlying rationale is to view RVE size as the smallest moving window size for which the stochastic nature of the microstructure within the window is stationary as the window moves across a large micrograph. For this purpose, we adapt a recently developed Fisher score-based framework for microstructure nonstationarity monitoring. Because the resulting RVE size is based solely on the micrograph and does not involve any FE simulation of specific properties, it constitutes an RVE for any property of interest that solely depends on the microstructure characteristics. Through numerical experiments of simple and complex microstructures, we validate our approach and show that our selected RVE sizes are consistent with when the chosen FE-simulated properties converge. Copyright © 2024, The Authors. All rights reserved.

关键词： Microstructure

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

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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

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A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing

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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

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Assessing the Potential of Biodegradable Plastics in a Circular Economy: A Methodological Outlook

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Circular Economy and Sustainability 2025年第2期5卷 1291-1305页

作者： Scheers, Quinten Nimmegeers, Philippe Galant, Or Spatari, Sabrina Van den Brande, Niko Brancart, Joost Billen, Pieter Faculty of Applied Engineering Intelligence in Processes Advanced Catalysts and Solvents (iPRACS) University of Antwerp Groenenborgerlaan 171 Antwerp 2020 Belgium Faculty of Business and Economics Department of Engineering Management Environmental Economics (EnvEcon) University of Antwerp Prinsstraat 13 Antwerp 2000 Belgium Flanders Make@UAntwerp Antwerp 2000 Belgium Faculty of Civil and Environmental Engineering Technion– Israel Institute of Technology Haifa 3200003 Israel Grand Technion Energy Program Technion– Israel Institute of Technology Haifa 3200003 Israel Physical Chemistry and Polymer Science (FYSC) Vrije Universiteit Brussel Pleinlaan 2 Brussels 1050 Belgium IVL Swedish Environmental Research Institute Valhallavägen 81 Stockholm 11428 Sweden

Increased biodegradability, in other words reduced environmental recalcitrance, has the potential to be an important asset in the battle against plastics pollution. It can partially mitigate global plastic leakages into the environment, which is next to their impact on climate change the biggest concern regarding plastics use. Biodegradation should be regarded as an optimal safety net in many applications and should often not be the targeted waste management option for biodegradable plastics. Biodegradability should not preclude reuse or proper recyclability, which are impactful means to reduce the carbon intensity of plastics lifecycles. Despite the added advantages, the market penetration of biodegradable plastics is only very slowly growing. Moreover, policy recommends its use only in limited applications, driven by sometimes-higher cost and limited recyclability. In this article, we posit that arguments against widespread application of biodegradable plastics are largely driven by misperceptions arising from intrinsic limitations of contemporary sustainability assessments and offer an alternative viewpoint that could compensate for this. In general, sustainability assessments fail to properly assess learning curves, often do not take littering potential into account, and are rather conservative with respect to background systems. Hence, recyclability– or better: circularity– judgements often do not resolve the paradoxical situation in which increased market shares lead to easier and more profitable recycling schemes and vice versa. Judging disruptive innovations by incorporating thermodynamics-inspired, less data-intensive, state-based methods, based on, e.g., statistical entropy analysis, into the decision support framework could allow to forecast packaging markets with performant materials that are intrinsically circular, having reduced environmental recalcitrance. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Biodegradable Plastics Decision Making Statistical Entropy Sustainability Assessments

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Vinod Kumar Isha Jain Durga Sritha Dongla Shivangi Rai Vani Rastogi Eva Kaushik Rohit Kaushik School of Engineering & Applied Science The George Washington University Washington D.C USA Computer Science & Engineering Department Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Computer Science & Engineering Department Gandhi Institute of Technology and Management Visakhapatnam Andhra Pradesh Data Scientist DXC Technology Noida India CS and Mathematics Department Data Analytics Program University of Illinois Springfield USA

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions.

关键词： Deep learning Pathology Accuracy Melanoma Computer architecture Benchmark testing Skin Convolutional neural networks Lesions Medical diagnosis

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Strategic legislation for the promotion of university–industry collaborations: a case study of Taiwan

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Journal of Technology Transfer 2025年第1期50卷 304-344页

作者： Truong, Thanh Hue Lin, Bou-Wen Tung, Ching-Pin International Degree Program in Climate Change and Sustainable Development National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan Research Center for Future Earth National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan Graduate Institute of Business Administration College of Management National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan National Applied Research Laboratories No. 106 Sec. 2 Heping E. Rd. Da’an Dist. Taipei City106214 Taiwan Department of Bioenvironmental Systems Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan

The strategic utilization of technology to enhance industrial competitiveness and product quality is pivotal for economic growth. As universities own more inventions, collaborative efforts between industry and academia become essential for regional competitiveness and overall economic advancement. This study explores Taiwan’s systematic approach to upgrading its infrastructure, emphasizing the significance of University–Industry Collaborations (UIC) in realizing Industry 4.0. Government strategies and policy decisions emerge as key drivers in fostering successful collaborative projects between industry and university teams. The relaxation of restrictions, particularly related to part-time teachers and technology transfer, along with the promotion of corporate programs, effective use of public funding, and the development of an innovative digital platform system for UIC, collectively contribute to establishing a robust university–industry cooperation mechanism. This mechanism has contributed to Taiwan’s move towards new digital advances in innovation and entrepreneurship, fostering industrial leadership and efficient governance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Technology transfer

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