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  • 51.
    Wang, Gaihua
    et al.
    Hubei Univ Technol, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Xiong, Wei
    Hubei Univ Technol, Peoples R China.
    Li, Yan
    Hubei Univ Technol, Peoples R China; Univ Southern Queensland, Australia.
    An improved non-local means filter for color image denoising2018In: Optik (Stuttgart), ISSN 0030-4026, E-ISSN 1618-1336, Vol. 173, p. 157-173Article in journal (Refereed)
    Abstract [en]

    Non-local means filter is a special case of non-linear filter. It performs well for filtering Gaussian noise while preserving edges and details of the original images. In this paper, we propose an improved filter for color image denoising based on combining the advantages of non-local means filter and bilateral filter. To compare the similarity of patches, a new weight value is computed by adding texture information into weights. The experimental results of color image filtering show that the proposed method has a better performance for reducing Gaussian noise and mixture noise.

  • 52.
    Wang, Gaihua
    et al.
    School of Electrical and Electronic Engineering, Hubei University of Technology, China.
    Liu, Yang
    School of Electrical and Electronic Engineering, Hubei University of Technology, China / Faculty of Technology, University of Vaasa, Vaasa, Finland.
    Zhao, Tongzhou
    Hubei Province Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, China.
    A quaternion-based switching filter for colour image denoising2014In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 102, p. 216-225Article in journal (Refereed)
    Abstract [en]

    An improved quaternion switching filter for colour image denoising is presented. It proposes a RGB colour image as a pure quaternion form and measures differences between two colour pixels with the quaternion-based distance. Further, in noise-detection, a two-stage detection method is proposed to determine whether the current pixel is noise or not. The noisy pixels are replaced by the vector median filter (VMF) output and the noise-free ones are unchanged. Finally, we combine the advantages of quaternion-based switching filter and non-local means filter to remove mixture noise. By comparing the performance and computing time processing different images, the proposed method has superior performance which not only provides the best noise suppression results but also yields better image quality compared to other widely used filters.

  • 53.
    Wang, Jin
    et al.
    Northwestern Polytech Univ, Peoples R China; Xian Univ Posts and Telecommun, Peoples R China.
    Yang, Jiahao
    Northwestern Polytech Univ, Peoples R China.
    Zhang, Yingfeng
    Northwestern Polytech Univ, Peoples R China; Shaanxi Univ Technol, Peoples R China.
    Ren, Shan
    Northwestern Polytech Univ, Peoples R China; Xian Univ Posts and Telecommun, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods2020In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 247, article id 119093Article in journal (Refereed)
    Abstract [en]

    Production scheduling has great significance for optimizing tasks distribution, reducing energy consumption and mitigating environmental degradation. Currently, the research of production scheduling considering energy consumption mainly focuses on the traditional manufacturing workshop. With the wide application of the Internet of Things (IoT) technology, the real-time data of manufacturing resources and production processes can be retrieved easily. These manufacturing data can provide opportunities for manufacturing enterprises to reduce energy consumption and enhance production efficiency. To achieve these targets, a multi-period production planning based real-time scheduling (MPPRS) approach for the loT-enabled low-carbon flexible job shop (LFJS) is presented in this study to carry out real-time scheduling based on the real-time manufacturing data. Then, the mathematical models of real-time scheduling are established to achieve production efficiency improvement and energy consumption reduction. To obtain a feasible solution, an infinitely repeated game optimization approach is used. Finally, a case study is implemented to analyse and discuss the effectiveness of the proposed method. The results show that in general, the proposed method can achieve better results than the existing dynamic scheduling methods. (C) 2019 Elsevier Ltd. All rights reserved.

    The full text will be freely available from 2021-10-31 15:19
  • 54.
    Wang, Jin
    et al.
    Northwestern Polytech Univ, Peoples R China; Xian Aeronaut Univ, Peoples R China.
    Zhang, Yingfeng
    Northwestern Polytech Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Wu, Naiqi
    Macau Univ Sci and Technol, Peoples R China; Guangdong Univ Technol, Peoples R China.
    Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop2019In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 6, no 2, p. 2518-2531Article in journal (Refereed)
    Abstract [en]

    With the rapid advancement and widespread applications of information technology in the manufacturing shop floor, a huge amount of real-time data is generated, providing a good opportunity to effectively respond to unpredictable exceptions so that the productivity can be improved. Thus, how to schedule the manufacturing shop floor for achieving such a goal is very challenging. This paper addresses this issue and a new multiagent-based real-time scheduling architecture is proposed for an Internet of Things-enabled flexible job shop. Differing from traditional dynamic scheduling strategies, the proposed strategy optimally assigns tasks to machines according to their real-time status. A bargaining-game-based negotiation mechanism is developed to coordinate the agents so that the problem can be efficiently solved. To demonstrate the feasibility and effectiveness of the proposed architecture and scheduling method, a proof-of-concept prototype system is implemented with Java agent development framework platform. A case study is used to test the performance and effectiveness of the proposed method. Through simulation and comparison, it is shown that the proposed method outperforms the traditional dynamic scheduling strategies in terms of makespan, critical machine workload, and total energy consumption.

  • 55.
    Xie, Kefan
    et al.
    Management School, Wuhan University of Technology, Wuhan, China / Institute of Systems Science and Engineering, Wuhan University of Technology, Wuhan, China.
    Chen, Gang
    Management School, Wuhan University of Technology, Wuhan, China / Institute of Systems Science and Engineering, Wuhan University of Technology, Wuhan, China.
    Wu, Qian
    Management School, Wuhan University of Technology, Wuhan, China / Institute of Systems Science and Engineering, Wuhan University of Technology, Wuhan, China.
    Liu, Yang
    Department of Production, University of Vaasa, Vaasa, Finland.
    Wang, Pan
    Institute of Systems Science and Engineering, Wuhan University of Technology, Wuhan, China / School of Automation, Wuhan University of Technology, Wuhan, China.
    Research on the group decision-making about emergency event based on network technology2011In: Journal of Special Topics in Information Technology and Management, ISSN 1385-951X, E-ISSN 1573-7667, Vol. 12, no 2, p. 137-147Article in journal (Refereed)
    Abstract [en]

    In order to improve decision-making efficiency about emergency event, this paper proposes a novel concept, i.e., Agile-Delphi Method, which is an integration of agile decision and Delphi Method implicating that the decision-makers instantly deliver, respond, treat, and utilize information via Delphi process while conducting group decision-making about emergency event. The paper details the mechanism of group decision-making about emergency event based on network technology and Agile-Delphi Method. Finally, the paper conducts an empiric analysis taking the “111 event”, i.e., the liquid ammonia spill event happened on November 1, 2006 in a phosphorus chemical company in China, as an example.

  • 56.
    Xie, Kefan
    et al.
    Wuhan Univ Technol, Peoples R China.
    Mei, Yanlan
    Wuhan Univ Technol, Peoples R China; Wuhan Inst Technol, Peoples R China.
    Gui, Ping
    Wuhan Univ Technol, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Early-warning analysis of crowd stampede in metro station commercial area based on internet of things2019In: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721, Vol. 78, no 21, p. 30141-30157Article in journal (Refereed)
    Abstract [en]

    Crowd stampede has attracted significant attention of emergency management researchers in recent years. Early-warning of crowd stampede in metro station commercial area is discussed in this paper under the context of Internet of Things (IoT). Metro station commercial area is one of the entity carriers of E-commerce. IOT is a new concept of realizing intelligent sense, monitoring, tracking and management, which can be used in early-warning analysis of crowd stampede in metro station. Stampede risk early-warning in commercial area plays an important role in ensuring the operation of e-commerce online. Firstly, the laws and characteristics of the crowd movement in the commercial area of metro station are studied, which include the laeuna effect, block effect and aggravation effect. Secondly, the early-warning paradigm is constructed from four dimensions, ie. function, modules, principle and process. And then, under the IOT environment, the AHPsort II is applied to integrate the early-warning information and classify the stampede risk level. Finally, the paper takes the commercial area of Wuhan A metro station as an example to verify the practicability and effectiveness of the AHPsort II application to early-warning of crowd stampede in metro station commercial area.

  • 57.
    Xie, Yi
    et al.
    Wuhan Engineering Consulting Bureau, Wuhan, China.
    Takala, Josu
    Faculty of TechnologyUniversity of Vaasa, Vaasa, Finland.
    Liu, Yang
    Faculty of TechnologyUniversity of Vaasa, Vaasa, Finland.
    Chen, Yong
    Old Dominion University, Norfolk, USA.
    A combinatorial optimization model for enterprise patent transfer2015In: Journal of Special Topics in Information Technology and Management, ISSN 1385-951X, E-ISSN 1573-7667, Vol. 16, no 4, p. 327-337Article in journal (Refereed)
    Abstract [en]

    Enterprises need patent transfer strategies to improve their technology management. This paper proposes a combinatorial optimization model that is based on intelligent computing to support enterprises’ decision making in developing patent transfer strategy. The model adopts the Black–Scholes Option Pricing Model and Arbitrage Pricing Theory to estimate a patent’s value. Based on the estimation, a hybrid genetic algorithm is applied that combines genetic algorithms and greedy strategy for the optimization purpose. Encode repairing and a single-point crossover are applied as well. To validate this proposed model, a case study is conducted. The results indicate that the proposed model is effective for achieving optimal solutions. The combinatorial optimization model can help enterprise promote their benefits from patent sale and support the decision making process when enterprises develop patent transfer strategies.

  • 58.
    Yadav, Gunjan
    et al.
    VJTI, India.
    Luthra, Sunil
    State Inst Engn and Technol, India.
    Huisingh, Donald
    Univ Tennessee, TN 37996 USA.
    Mangla, Sachin Kumar
    Univ Plymouth, England.
    Narkhede, Balkrishna Eknath
    NITIE, India.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Development of a lean manufacturing framework to enhance its adoption within manufacturing companies in developing economies2020In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 245, article id 118726Article in journal (Refereed)
    Abstract [en]

    The urgent need to reduce negative corporate environmental impacts while enhancing their financial strength and positive societal benefits is attracting company leaders to implement various quality improvement systems such as lean manufacturing, six sigma, sustainable manufacturing, and circular economy concepts, approaches and technologies. All of these approaches are valuable, with Lean Manufacturing (LM) among the leading systems, if implemented within an appropriate framework. In that context, the objective of the authors was to document the drivers for improving implementation of LM within manufacturing companies. Implementation of LM practices is already providing competitive advantages such as improvements in product quality, productivity, worker health and safety and customer satisfaction in developed countries but has not been widely implemented in companies in developing countries. To help to enhance implementation of LM in developing countries, the authors developed a framework for enhancing the adoption of lean manufacturing processes in such companies. The hybrid Fuzzy Analytical Hierarchy Process (FAHP)- Decision Making Trial and Evaluation Laboratory (DEMATEL) tools were used as the framework to identify and to quantify the interrelationships among the drivers for implementation of LM. This hybrid approach facilitated documentation of the relative importance and priority of the thirty-one lean manufacturing drivers. The results revealed that improved shop-floor management, quality management, and manufacturing strategy drivers were among the most critical drivers, which enhance LM adoption. These findings are beneficial for company leaders and researchers working to improve environmental, economic and societal health, especially within companies in developing countries. (C) 2019 Elsevier Ltd. All rights reserved.

    The full text will be freely available from 2021-10-13 13:19
  • 59.
    Zhang, Abraham
    et al.
    Auckland Univ Technol, New Zealand; Excelsia Coll, Australia; Indiana Wesleyan Univ, Australia.
    Venkatesh, V. G.
    Ecole Management Normandie, France.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Jinan Univ, Peoples R China; Univ Vaasa, Finland.
    Wan, Ming
    Jinan Univ, Peoples R China.
    Qu, Ting
    Jinan Univ, Peoples R China.
    Huisingh, Donald
    Univ Tennessee, TN USA.
    Barriers to smart waste management for a circular economy in China2019In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 240, article id UNSP 118198Article in journal (Refereed)
    Abstract [en]

    Waste management requires a new vision and drastic improvements for a transition to a zero-waste circular economy. In reality, however, many economies are producing more and more waste, which poses a serious challenge to environmental sustainability. The problem is enormously complex as it involves a variety of stakeholders, demands behavioral changes, and requires a complete rethinking of the current waste management systems and the dominant linear economic model. Smart enabling technologies can aid in a transformation of waste management toward a circular economy, but many barriers persist. This study first shortlists twelve important barriers to smart waste management in China based on interviews with experienced practitioners. It then prioritizes these barriers through a scientific prioritization technique, fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), based on the survey data from three representative stakeholders. It identified three key causal barriers: the lack of regulatory pressures, the lack of environmental education and culture of environmental protection, and the lack of market pressures and demands. Practical and theoretical implications were discussed based on the research results and findings. (C) 2019 Elsevier Ltd. All rights reserved.

  • 60.
    Zhang, Fanshun
    et al.
    Jinan Univ, Peoples R China.
    Cao, Cejun
    Chongqing Technol and Business Univ, Peoples R China; Tianjin Univ, Peoples R China.
    Li, Congdong
    Jinan Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Jinan Univ, Peoples R China; Univ Vaasa, Finland.
    Huisingh, Donald
    Univ Tennessee, TN USA.
    A systematic review of recent developments in disaster waste management2019In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 235, p. 822-840Article, review/survey (Refereed)
    Abstract [en]

    Disaster waste management received increasing attention in recent years, but there was no review updating the evolving development after the study of Brown et al. (2011a). To explore how the topics in disaster waste management evolved in recent years and to analyze whether the gaps identified by Brown et al. (2011a) are covered, 82 papers published from 2011 to 2019 were selected from the Scopus database based on the defined process and criteria. This paper systematically examines the disaster waste management research from nine aspects of planning, waste, waste treatment options, environment, economics, social considerations, organizational aspects, legal frameworks and funding. The results suggested that there were no obvious changes or developments in the field of disaster waste management, although a few research gaps have been addressed, such as waste separation, waste quantities, case studies of incineration and waste to energy, direct economic effects, social considerations as well as application of GIS technology. Except for the comparative studies, future directions were suggested by the gaps that persist since Brown et al. (2011a) and the new gaps that were identified in this review. (C) 2019 Elsevier Ltd. All rights reserved.

    The full text will be freely available from 2021-06-22 15:27
  • 61.
    Zhang, Kai
    et al.
    Jinan Univ, Peoples R China.
    Qu, Ting
    Jinan Univ, Peoples R China.
    Zhou, Dajian
    Guangdong Univ Technol, Peoples R China.
    Jiang, Hongfei
    Jinan Univ, Peoples R China.
    Lin, Yuanxin
    Jinan Univ, Peoples R China.
    Li, Peize
    Jinan Univ, Peoples R China; Xian Univ Sci and Technol, Peoples R China.
    Guo, Hongfei
    Jinan Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Jinan Univ, Peoples R China.
    Li, Congdong
    Jinan Univ, Peoples R China.
    Huang, George Q.
    Jinan Univ, Peoples R China; Univ Hong Kong, Peoples R China.
    Digital twin-based opti-state control method for a synchronized production Check toroperation system2020In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 63, article id 101892Article in journal (Refereed)
    Abstract [en]

    The intelligent manufacturing strategy and customer demand have mutually promoted each other. Also, the production mode is shifting towards customized production, and more rental resources or services are introduced to the production system, therefore, the systems are becoming more complex. Compared with traditional production systems, such systems have some new features, this work calls this type of system as a synchronized production operation system (SPOS). Under such circumstances, production systems are influenced by more frequent uncertainties, and the planning-based production decision and control approach is no longer applicable. The opti-state control (OsC) method is proposed to help SPOS keep in an optimal state when uncertainties affect the system. Besides, a digital twin-based control framework (DTCF) is designed for getting the full element information needed for decision making. Based on the comprehensive information of the production system obtained by the DTCF, the OsC method is introduced to the virtual control layer to formulate the optimal target guiding the path of the system in real time through the dynamic matching mechanism (qualitative perspective). Then multi-stage synchronized control with analysis target cascading (ATC) method is used to get the local optimal state decisions (quantitative perspective). From both qualitative and quantitative aspects to ensure the system is under an optimal target path for optimal operation procedure. At last, a case study in a large-size paint making company in China is used to validate the effectiveness of the approach.

  • 62.
    Zhang, Kai
    et al.
    Jinan Univ, Peoples R China.
    Qu, Ting
    Jinan Univ, Peoples R China.
    Zhou, Dajian
    Guangdong Univ Technol, Peoples R China.
    Thurer, Matthias
    Jinan Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Jinan Univ, Peoples R China.
    Nie, Duxian
    South China Agr Univ, Peoples R China.
    Li, Congdong
    Jinan Univ, Peoples R China.
    Huang, George Q.
    Jinan Univ, Peoples R China; Univ Hong Kong, Peoples R China.
    IoT-enabled dynamic lean control mechanism for typical production systems2019In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 10, no 3, p. 1009-1023Article in journal (Refereed)
    Abstract [en]

    The emergence and subsequent popularization of lean has been one of the most significant developments in the history of operations management. However, there is a lack of systematic theory on the control framework underlying lean production. It is therefore difficult to conduct more in-depth research on Lean theory, specifically in the context of emerging technologies as smart manufacturing or Industry 4.0. In this study, process control theory is used to re-define several major lean methods and tools. Then a Lean-Oriented Optimum-State Control Theory (L-OSCT) is proposed that integrates these lean methods and tools into optimum-state control theory. On the level of method and mechanism, we adopt a recently emerged synchronization approach to obtain global-wide leanness of a large-scale system. L-OSCT provides dynamic process control in industrial networking systems. At last, a case study in a large-size paint making company in China is used to validate the effectiveness of the approach.

  • 63.
    Zhang, Yingfeng
    et al.
    Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, School of Mechanical Engineering, Northwestern Polytechnical University, Shaanxi, China.
    Liu, Sichao
    Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, School of Mechanical Engineering, Northwestern Polytechnical University, Shaanxi, China.
    Liu, Yang
    Department of Production, University of Vaasa, Vaasa, Finland.
    Li, Rui
    Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, School of Mechanical Engineering, Northwestern Polytechnical University, Shaanxi, China.
    Smart box-enabled product–service system for cloud logistics2016In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 54, no 22, p. 6693-6706Article in journal (Refereed)
    Abstract [en]

    Modern logistics takes significant progress and rapid developments with the prosperity of E-commerce, particularly in China. Typical challenges that logistics industry is facing now are composed by a lack of sharing, standard, cost-effective and environmental package and efficient optimisation method for logistics tasks distribution. As a result, it is difficult to implement green, sustainable logistics services. Three important technologies, Physical Internet (PI), product–service system (PSS) and cloud computing (CC), are adopted and developed to address the above issues. PI is extended to design a world-standard green recyclable smart box that is used to encapsulate goods. Smart box-enabled PSS is constructed to provide an innovative sustainable green logistics service, and high-quality packaging, as well as reduce logistics cost and environmental pollution. A real-time information-driven logistics tasks optimisation method is constructed by designing a cloud logistics platform based on CC. On this platform, a hierarchical tree-structure network for customer orders (COs) is built up to achieve the order-box matching of function. Then, a distance clustering analysis algorithm is presented to group and form the optimal clustering results for all COs, and a real-time information-driven optimisation method for logistics orders is proposed to minimise the unused volume of containers. Finally, a case study is simulated to demonstrate the efficiency and feasibility of proposed cloud logistics optimisation method. © 2016 Informa UK Limited, trading as Taylor & Francis Group.

  • 64.
    Zhang, Yingfeng
    et al.
    Northwestern Polytech Univ, Peoples R China.
    Liu, Sichao
    Northwestern Polytech Univ, Peoples R China; KTH Royal Inst Technol, Sweden.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Yang, Haidong
    Guangdong Univ Technol, Peoples R China.
    Li, Miao
    Northwestern Polytech Univ, Peoples R China.
    Huisingh, Donald
    Univ Tennessee, TN USA.
    Wang, Lihui
    KTH Royal Inst Technol, Sweden.
    The Internet of Things enabled real-time scheduling for remanufacturing of automobile engines2018In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 185, p. 562-575Article in journal (Refereed)
    Abstract [en]

    Typical challenges that managers of remanufacturing face are composed of the lack of timely, accurate, and consistent information of remanufacturing resources. Therefore, it is difficult to implement real-time production scheduling for the shop floor. To address this problem, the authors applied the concept of the Internet of Things to the remanufacturing of automobile engines to form an Internet of Manufacturing Things environment. Under the Internet of Manufacturing Things, an identification technology for disassembled engine parts was designed, and the real-time status of the remanufacturable resources can be monitored. Based on the captured remanufacturing information, a real-time production scheduling method was developed, and a mathematical model was developed to achieve cost reduction, dynamic management of remanufacturable resources, and energy consumption decrease. To obtain an optimal solution, a Pareto-based optimization method was used. Finally, a case study was performed to analyze the effectivity of the proposed method. The results showed that the remanufacturing cost and energy consumption were reduced by 34% and 34% respectively, and the worker load rate was more balanced. These improvements can contribute to more sustainable development and greener production within the remanufacturing industry, especially for remanufacturing of automobile engines. (C) 2018 Elsevier Ltd. All rights reserved.

  • 65.
    Zhang, Yingfeng
    et al.
    Northwestern Polytech Univ, Peoples R China; Northwestern Polytech Univ, Peoples R China.
    Ma, Shuaiyin
    Northwestern Polytech Univ, Peoples R China.
    Yang, Haidong
    Guangdong Univ Technol, Peoples R China.
    Lv, Jingxiang
    Northwestern Polytech Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    A big data driven analytical framework for energy-intensive manufacturing industries2018In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 197, p. 57-72Article in journal (Refereed)
    Abstract [en]

    Energy-intensive industries account for almost 51% of energy consumption in China. A continuous improvement in energy efficiency is important for energy-intensive industries. Cleaner production has proven itself as an effective way to improve energy efficiency and reduce energy consumption. However, there is a lack of manufacturing data due to the difficult implementation of sensors in harsh production environment, such as high temperature, high pressure, high acid, high alkali, and smoky environment which hinders the implementation of the cleaner production strategy. Thanks to the rapid development of the Internet of Things, many data can be sensed and collected in the manufacturing processes. In this paper, a big data driven analytical framework is proposed to reduce the energy consumption and emission for energy-intensive manufacturing industries. Then, two key technologies of the proposed framework, namely energy big data acquisition and energy big data mining, are utilized to implement energy big data analytics. Finally, an application scenario of ball mills in a pulp workshop of a partner company is presented to demonstrate the proposed framework. The results show that the energy consumption and energy costs are reduced by 3% and 4% respectively. These improvements can promote the implementation of cleaner production strategy and contribute to the sustainable development of energy intensive manufacturing industries. (C) 2018 Elsevier Ltd. All rights reserved.

  • 66.
    Zhang, Yingfeng
    et al.
    Northwestern Polytech University, Peoples R China.
    Ren, Shan
    Northwestern Polytech University, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering. Linköping University, Faculty of Science & Engineering. University of Vaasa, Finland.
    Sakao, Tomohiko
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Huisingh, Donald
    University of Tennessee, TN USA.
    A framework for Big Data driven product lifecycle management2017In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 159, p. 229-240Article in journal (Refereed)
    Abstract [en]

    Optimization of the process of product lifecycle management is an increasingly important objective for manufacturing enterprises to improve their sustainable competitive advantage. Originally, this approach was developed to integrate the business processes of an organization and more effectively manage and utilize the data generated during lifecycle studies. With emerging technologies, product embedded information devices such as radio frequency identification tags and smart sensors are widely used to improve the efficiency of enterprises routine management on an operational level. Manufacturing enterprises need a more advanced analysis approach to develop a solution on a strategic level from using such lifecycle Big Data. However, the application of Big Data in lifecycle faces several challenges, such as the lack of reliable data and valuable knowledge that can be employed to support the optimized decision-making of product lifecycle management. In this paper, a framework for Big Data driven product lifecycle management was proposed to address these challenges. Within the proposed framework, the availability and accessibility of data and knowledge related to lifecycle can be achieved. A case study was presented to demonstrate the proof-of-concept of the proposed framework. The results showed that the proposed framework was feasible to be adopted in industry, and can provide an overall solution for optimizing the decision-making processes in different phases of the whole lifecycle. The key findings and insights from the case study were summarized as managerial implications, which can guide manufacturers to ensure improvements in energy saving and fault diagnosis related decisions in the whole lifecycle. (C) 2017 Elsevier Ltd. All rights reserved.

  • 67.
    Zhang, Yingfeng
    et al.
    Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Shaanxi, PR China.
    Ren, Shan
    Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Shaanxi, PR China; Department of Mechanical Engineering, Honghe University, Yunnan, PR China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Department of Production, University of Vaasa, Vaasa, Finland.
    Si, Shubin
    Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Shaanxi, PR China.
    A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products2017In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 142, no 2, p. 626-641Article in journal (Refereed)
    Abstract [en]

    Cleaner production (CP) is considered as one of the most important means for manufacturing enterprises to achieve sustainable production and improve their sustainable competitive advantage. However, implementation of the CP strategy was facing barriers, such as the lack of complete data and valuable knowledge that can be employed to provide better support on decision-making of coordination and optimization on the product lifecycle management (PLM) and the whole CP process. Fortunately, with the wide use of smart sensing devices in PLM, a large amount of real-time and multi-source lifecycle big data can now be collected. To make better PLM and CP decisions based on these data, in this paper, an overall architecture of big data-based analytics for product lifecycle (BDA-PL) was proposed. It integrated big data analytics and service-driven patterns that helped to overcome the above-mentioned barriers. Under the architecture, the availability and accessibility of data and knowledge related to the product were achieved. Focusing on manufacturing and maintenance process of the product lifecycle, and the key technologies were developed to implement the big data analytics. The presented architecture was demonstrated by an application scenario, and some observations and findings were discussed in details. The results showed that the proposed architecture benefited customers, manufacturers, environment and even all stages of PLM, and effectively promoted the implementation of CP. In addition, the managerial implications of the proposed architecture for four departments were analyzed and discussed. The new CP strategy provided a theoretical and practical basis for the sustainable development of manufacturing enterprises.

  • 68.
    Zhang, Yingfeng
    et al.
    Northwestern Polytech University, Peoples R China.
    Wang, Jin
    Northwestern Polytech University, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering. Linköping University, Faculty of Science & Engineering. University of Vaasa, Finland.
    Game theory based real-time multi-objective flexible job shop scheduling considering environmental impact2017In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 167, p. 665-679Article in journal (Refereed)
    Abstract [en]

    Production scheduling greatly contributes to optimising the allocation of processes, reducing resource and energy consumption, lowering production costs and alleviating environmental pollution. It is an effective way to progress towards green manufacturing. With the extensive use of the Internet of Things in the manufacturing shop floor, a huge amount of real-time data is created. A typical challenge is how to achieve the real-time data-driven optimisation for the manufacturing shop floor to improve energy efficiency and production efficiency. To address this problem, a dynamic game theory based two-layer scheduling method was developed to reduce makespan, the total workload of machines and energy consumption to achieve real-time multi-objective flexible job shop scheduling. To obtain an optimal solution, a sub-game perfect Nash equilibrium solution was designed. Then, a case study was employed to analyse the performance of the proposed method. The results showed that the makespan, the total workload of machines and energy consumption were reduced by 4.5%, 8.75%, and 9.3% respectively. These improvements can contribute to sustainable development and cleaner production of manufacturing industry. (C) 2017 Elsevier Ltd. All rights reserved.

  • 69.
    Zhang, Yingfeng
    et al.
    Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Shaanx, China / Department of Industrial Engineering, Northwestern Polytechnical University, Xi’an, China.
    Zhang, Geng
    Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Shaanx, China.
    Liu, Yang
    Department of Production, University of Vaasa, Vaasa, Finland.
    Hu, Di
    Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Shaanx, China.
    Research on services encapsulation and virtualization access model of machine for cloud manufacturing2015In: Journal of Intelligent Manufacturing, ISSN 0956-5515, E-ISSN 1572-8145, p. 1-15Article in journal (Refereed)
    Abstract [en]

    Considering the new requirements of the services encapsulation and virtualization access of manufacturing resources for cloud manufacturing (CMfg), this paper presents a services encapsulation and virtualization access model for manufacturing machine by combining the Internet of Things techniques and cloud computing. Based on this model, some key enabling technologies, such as configuration of sensors, active sensing of real-time manufacturing information, services encapsulation, registration and publishing method are designed. By implementing the proposed services encapsulation and virtualization access model to manufacturing machine, the capability of the machine could be actively perceived, the production process is transparent and can be timely visited, and the virtualized machine could be accessed to CMfg platform through a loose coupling, ‘plug and play’ manner. The proposed model and methods will provide the real-time, accurate, value-added and useful manufacturing information for optimal configuration and scheduling of large-scale manufacturing resources in a CMfg environment.

  • 70.
    Zhang, Yingfeng
    et al.
    Northwestern Polytech University, Peoples R China; Northwestern Polytech University, Peoples R China.
    Zhang, Geng
    Northwestern Polytech University, Peoples R China.
    Qu, Ting
    Jinan University, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Jinan University, Peoples R China; University of Vaasa, Finland.
    Zhong, Ray Y.
    University of Auckland, New Zealand.
    Analytical target cascading for optimal configuration of cloud manufacturing services2017In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 151, p. 330-343Article in journal (Refereed)
    Abstract [en]

    Combining with advanced technologies (e.g., cloud computing, Internet of Things, and service-oriented technology), cloud manufacturing was proposed and gained wide attention. By managing a huge amount of distributed and idle manufacturing resources to meet various manufacturing requirements, cloud manufacturing provides sustainable means for promoting cleaner production. Manufacturing service configuration plays an important role in implementing cloud manufacturing. Most research adopted central optimization methods to get optimal service configuration results. However, these all-in-one methods with an individual decision model can hardly maintain the autonomous decision rights of different service providers. Consequently, service providers may lose their flexibility to achieve private decision objectives, which is unfavorable for keeping the sustainable competitive advantages of enterprises. In this paper, a decentralized decision mechanism named analytical target cascading is introduced to solve the manufacturing service configuration problem. An analytical target cascading model for the manufacturing service configuration problem is proposed based on the hierarchical structure of cloud manufacturing system. Elements in the proposed model are formulated and solved in a loose coupling and distributed manner. The situation when alternative service providers owned autonomous decision rights to configure their respective upstream manufacturing stages is also considered. A case study is employed to verify the effectiveness of analytical target cascading in solving the manufacturing service configuration problem. It shows that analytical target cascading can not only obtain the same manufacturing service configuration results as central optimization method but also maintain the autonomous decision rights of different service providers. (C) 2017 Elsevier Ltd. All rights reserved.

  • 71.
    Zhang, Yongping
    et al.
    School of Automation Science and Electrical Engineering, Beihang University, 12633 Beijing China.
    Tao, Fei
    School of Automation Science and Electrical Engineering, Beihang University, 12633 Beijing China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Zhang, Pengyuan
    School of Automation Science and Electrical Engineering, Beihang University, Beijing China.
    Cheng, Ying
    School of Automation Science and Electrical Engineering, Beihang University, Beijing China.
    Zuo, Ying
    School of Automation Science and Electrical Engineering, Beihang University, Beijing China.
    Long/short-term utility aware optimal selection of manufacturing service composition towards Industrial Internet platform2019In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 15, no 6, p. 3712-3722Article in journal (Refereed)
    Abstract [en]

    As numerous Industrial Internet platforms emerge, manufacturing services are shared among multiple stakeholders more frequently than ever. The optimal selection of shared manufacturing service composition (MSC) should both promise the task completion and the stakeholders’ satisfaction. However, as commercial entities, stakeholders concentrate on not only the temporary benefits but also the long-term acquisitions. Most of the existing MSC problems neglect the stakeholders’ prospect on the manufacturing service sharing. This leads to the disappointment and dissatisfaction of the stakeholders with long-term expectations, who will abandon the participation in Industrial Internet platform. Therefore, the long/short-term preferences of various stakeholders should be satisfied and balanced. In this paper, the long/short-term preferences of three sides (provider, consumer, and operator) are discussed. And the models considering short-term utility of a consumer and long-term utility of providers, are established. The potential tasks assigned to providers are taken into account to estimate the long-term utility if the current task is accepted. Then, to solve the bi-objective optimization problem, an improved Non-dominated Sorting Genetic Algorithm-II algorithm, combining Tabu search and improved K-means mechanism, is proposed to find the optimal solution set. Finally, the effectiveness of the method is verified by the experimental results in terms of solution diversity, astringency and stability, in which a finding is further observed that the changes of consumers’ preferences have little impact on the long-term utility of providers.

  • 72.
    Zhang, Yongping
    et al.
    Beihang Univ, Peoples R China.
    Zhang, Pengyuan
    Beihang Univ, Peoples R China.
    Tao, Fei
    Beihang Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Zuo, Ying
    Beihang Univ, Peoples R China.
    Consensus aware manufacturing service collaboration optimization under blockchain based Industrial Internet platform2019In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 135, p. 1025-1035Article in journal (Refereed)
    Abstract [en]

    To realize collaboration among distributed enterprises, manufacturing service collaboration on Industrial Internet platform is an efficient method. However, the low degree of participation resulted by the sense of distrust, dissatisfaction, and insecurity hinders the widely application of Industrial Internet platform. Therefore, a secure, trustworthy, and multi-user satisfied manufacturing service collaboration method is in urgent need. A blockchain based platform could be utilized to support collaboration among distributed participants to complete trustworthy transactions. In addition, in order to satisfy multiple users, there should be a suitable collaboration mechanism that allows interest-independent participants to fulfil their short/long-term expectations and guide them to a consensus. Based on the establishment of the underlying data and network layer of the blockchain, the collaboration optimization of manufacturing services based on consensus is proposed. By utilizing Memetic algorithm, both the long-term utility of providers and the short-term utility of consumers are combined to choose the optimal providers for the tasks. The providers are selected with higher satisfaction degree of consumers.

    The full text will be freely available from 2022-05-31 13:13
  • 73.
    Zheng, Pai
    et al.
    Nanyang Technol Univ, Singapore.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Tao, Fei
    Beihang Univ, Peoples R China.
    Wang, Zuoxu
    Nanyang Technol Univ, Singapore.
    Chen, Chun-Hsien
    Nanyang Technol Univ, Singapore.
    Smart Product-Service Systems Solution Design via Hybrid Crowd Sensing Approach2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 128463-128473Article in journal (Refereed)
    Abstract [en]

    The third wave of information technology (IT) competition has enabled one promising value co-creation proposition, Smart PSS (smart product-service systems). Manufacturing companies offer smart, connected products with various e-services as a solution bundle to meet individual customer satisfaction, and in return, collect and analyze usage data for evergreen design purposes in a circular manner. Despite a few works discussing such value co-creation business mechanism, scarcely any has been reported from technical aspect to realizing this data-driven manufacturer/service provider-customer interaction cost-effectively. To fill this gap, a novel hybrid crowd sensing approach is proposed, and adopted in the Smart PSS context. It leverages large-scale mobile devices and their massive user-generated/product-sensed data, and converges with reliable static sensing nodes and other data sources in the smart, connected environment for value generation. Both the proposed hybrid crowd sensing conceptual framework and its systematic information modeling process are introduced. An illustrative example of smart water dispenser maintenance service design is given to validate its feasibility. The result shows that the proposed approach can be a promising manner to enable value co-creation process cost-effectively.

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