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1.	Zhang, Yingfeng, et al. (författare) Analytical target cascading for optimal configuration of cloud manufacturing services 2017 Ingår i: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 151, s. 330-343 Tidskriftsartikel (refereegranskat)abstract 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.
2.	Zhang, Yongping, et al. (författare) Data-driven smart production line and its common factors 2019 Ingår i: The International Journal of Advanced Manufacturing Technology. - : Springer. - 0268-3768 .- 1433-3015. ; 103:1-4, s. 1211-1223 Tidskriftsartikel (refereegranskat)abstract Due to the wide usage of digital devices and easy access to the edge items in manufacturing industry, massive industrial data is generated and collected. A data-driven smart production line (SPL), which is a basic cell in a smart factory, is derived primarily. This paper studies the data-driven SPL and its common factors. Firstly, common factors such as integration, data-driven, service collaboration, and proactive service of SPL are investigated. Then, a data-driven method including data self-perception, data understanding, decision-making, and precise control for implementing SPL is proposed. As a reference, the research of the common factors and the data-driven method could offer a systematic standard for both academia and industry. Moreover, in order to validate this method, this paper presents an industrial case by taking an energy consumption forecast and fault diagnosis based on energy consumption data in a prototype of LED epoxy molding compound (EMC) production lines for example.
3.	Zhang, Yingfeng, et al. (författare) Research on services encapsulation and virtualization access model of machine for cloud manufacturing 2015 Ingår i: Journal of Intelligent Manufacturing. - : Springer Science and Business Media LLC. - 0956-5515 .- 1572-8145. ; , s. 1-15 Tidskriftsartikel (refereegranskat)abstract 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.
4.	Guo, Zhengang, et al. (författare) An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization 2020 Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE. - 1524-9050 .- 1558-0016. ; 22:10, s. 6634-6645 Tidskriftsartikel (refereegranskat)abstract Recent advances in technologies such as the Internet of Things (IoT) and Cyber-Physical Systems (CPS) have provided promising opportunities to solve problems in urban traffic. With the help of IoT technologies, online data from road segments are captured by monitoring devices, while real-time data from vehicles are collected through preinstalled sensors. Based on these data, a CPS model is constructed to depict real-time status and dynamic behavior of road segments and vehicles. An online learning data-driven model is developed to extract prior knowledge and enhance collaboration between road segments and vehicles by combining short-term traffic forecasting and real-time routing optimization. A case study based on Xi’an city is presented to demonstrate the feasibility and efficiency of the proposed method, showing a reduction in the travel time with reasonable computation time, without much compromising the travel distance and fuel consumption. This work potentially strengthens the transparency and intelligence of urban traffic systems.
5.	Guo, Zhengang, et al. (författare) Exploring self-organization and self-adaption for smart manufacturing complex networks 2023 Ingår i: Frontiers of Engineering Management. - : Springer Nature. - 2095-7513 .- 2096-0255. ; 10:2, s. 206-222 Tidskriftsartikel (refereegranskat)abstract Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber—physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.
6.	Kantola, Jussi, et al. (författare) Call for papers for a Special Volume of the Journal of Cleaner Production on “Innovative Products and Services for Sustainable Societal Development” 2015 Ingår i: Journal of Cleaner Production. - : Elsevier. - 0959-6526 .- 1879-1786. ; 93, s. 1-4 Tidskriftsartikel (refereegranskat)abstract Papers were asked for a Special Volume of the Journal of Cleaner Production on Innovative products and services for sustainable societal development. For this Call for Papers (CfPs) authors are invited to submit papers about innovative strategies, methodologies and models for Sustainable Supply Chain Management. The topics of interest in this theme include innovative strategies and methodologies for sustainable supply chain configuration, implementation and monitoring, and new models and methods for logistics and supply chain management. Topics in the Remanufacturing and networked manufacturing theme include innovative production scheduling strategies and models for remanufacturing and networked manufacturing, real-time decision-making strategies and models for remanufacturing and networked manufacturing, and manufacturing resource configuration methods that are designed to catalyze the transition to equitable, post-fossil carbon societies. Papers must be written in good English. Authors with limitations in the command of written English are recommended to have their papers edited by a Native English Science Editor before the first submission because poorly written documents can compromise the decisions during the review process.
7.	Kantola, Jussi, et al. (författare) Innovative products and services for sustainable societal development: Current reality, future potential and challenges 2017 Ingår i: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 162, s. S1-S10 Tidskriftsartikel (refereegranskat)abstract This special volume originates from the International Conference on Innovation and Management held at University of Vaasa in Finland in 2014. Talks with the key note speaker and Editor-in-Chief of the Journal of Cleaner Production led to an idea to develop a special volume about innovative products and services according to the themes of the conference. Thus, the purpose of this special volume is to explore different viewpoints of how innovative products and services may support sustainable societal development. There are five thematic areas with papers that describe new advancements in different industries and organizations. The included papers cover relevant theoretical background and present case studies and practical results. This special volume shows that great progresses are being made in different thematic areas but also that there are so much more waiting to be done for sustainable societal development. This volume indicates that cross-disciplinary approach is truly needed to achieve societal sustainable development. This requires people to change their mindsets and genuinely co-operate towards better future. (C) 2017 Published by Elsevier Ltd.
8.	Liu, Sichao, et al. (författare) An ‘Internet of Things’ enabled dynamic optimization method for smart vehicles and logistics tasks 2019 Ingår i: Journal of Cleaner Production. - : Elsevier. - 0959-6526 .- 1879-1786. ; 215, s. 806-820 Tidskriftsartikel (refereegranskat)abstract Centralized and one-way logistics services and the lack of real-time information of logistics resources are common in the logistics industry. This has resulted in the increased logistics cost, energy consumption, logistics resources consumption, and the decreased loading rate. Therefore, it is difficult to achieve efficient, sustainable, and green logistics services with dramatically increasing logistics demands. To deal with such challenges, a real-time information-driven dynamic optimization strategy for smart vehicles and logistics tasks towards green logistics is proposed. Firstly, an ‘Internet of Things’-enabled real-time status sensing model of logistics vehicles is developed. It enables the vehicles to obtain and transmit real-time information to the dynamic distribution center, which manages value-added logistics information. Then, such information can be shared among logistics companies. A dynamic optimization method for smart vehicles and logistics tasks is developed to optimize logistics resources, and achieve a sustainable balance between economic, environmental, and social objectives. Finally, a case study is carried out to demonstrate the effectiveness of the proposed optimization method. The results show that it contributes to reducing logistics cost and fuel consumption, improving vehicles’ utilization rate, and achieving real-time logistics services with high efficiency.
9.	Liu, Yang, et al. (författare) How can smart technologies contribute to sustainable product lifecycle management? 2020 Ingår i: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 249 Tidskriftsartikel (refereegranskat)abstract This Virtual Special Issue (VSI) was proposed on par with the fascinating and exponentially growing development of smart enabling technologies, such as Internet of Things (IoT), Cyber-Physical System (CPS), Cloud Computing (CC), Artificial Intelligence (AI), Big Data Analytics (BDA), Digital Twin (DT), etc, which have greatly advanced the development of sustainable smart manufacturing throughout the lifecycle. The VSI addressed issues that were not properly or even incorrectly addressed in the existing literature. The authors of this VSI sought to introduce new knowledge and debates to lead the research directions to new paths. The editorial team invited well-established researchers in this area and received about 40 highly qualified submissions, out of which 12 were accepted after standard peer-review procedure of the Journal of Cleaner Production, which covered the three main themes defined in the "Call-for-Papers". The contributing authors were from Brazil, China, Finland, Pakistan, Sweden, USA (in alphabetical order). The coordinators of this VSI are confident that the contents of this VSI will advance the science of digitalisation and will help society to make real progress towards sustainable societies. (C) 2019 Elsevier Ltd. All rights reserved.
10.	Ma, Shuaiyin, et al. (författare) Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries 2020 Ingår i: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 274 Tidskriftsartikel (refereegranskat)abstract The circular economy plays an important role in energy-intensive industries, aiming to contribute to ethical sustainable societal development. Energy demand response is a key actor for cleaner production and circular economy strategy. In the Industry 4.0 context, the advanced technologies (e.g. cloud computing, Internet of things, cyber-physical system, digital twin and big data analytics) provide numerous opportunities for the implementation of a cleaner production strategy and the development of intelligent manufacturing. This paper presented a framework of data-driven sustainable intelligent/smart manufacturing based on demand response for energy-intensive industries. The technological architecture was designed to implement the proposed framework, and multi-level demand response models were developed based on machine, shop-floor and factory to save energy cost. Finally, an application of ball mills in a slurry shop-floor of a partner company was presented to demonstrate the proposed framework and models. Results showed that the energy efficiency of ball mills can be greatly improved. The energy cost of the slurry shop-floor saved approximately 19.33% by considering electricity demand response using particle swarm optimisation. This study provides a practical approach to make effective and energy-efficient decisions for energy-intensive manufacturing enterprises. (C) 2020 The Author(s). Published by Elsevier Ltd.
11.	Naghavi, Mohsen, et al. (författare) Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013 2015 Ingår i: The Lancet. - 1474-547X .- 0140-6736. ; 385:9963, s. 117-171 Tidskriftsartikel (refereegranskat)abstract Background Up-to-date evidence on levels and trends for age-sex-specifi c all-cause and cause-specifi c mortality is essential for the formation of global, regional, and national health policies. In the Global Burden of Disease Study 2013 (GBD 2013) we estimated yearly deaths for 188 countries between 1990, and 2013. We used the results to assess whether there is epidemiological convergence across countries. Methods We estimated age-sex-specifi c all-cause mortality using the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data. We generally estimated cause of death as in the GBD 2010. Key improvements included the addition of more recent vital registration data for 72 countries, an updated verbal autopsy literature review, two new and detailed data systems for China, and more detail for Mexico, UK, Turkey, and Russia. We improved statistical models for garbage code redistribution. We used six different modelling strategies across the 240 causes; cause of death ensemble modelling (CODEm) was the dominant strategy for causes with sufficient information. Trends for Alzheimer's disease and other dementias were informed by meta-regression of prevalence studies. For pathogen-specifi c causes of diarrhoea and lower respiratory infections we used a counterfactual approach. We computed two measures of convergence (inequality) across countries: the average relative difference across all pairs of countries (Gini coefficient) and the average absolute difference across countries. To summarise broad findings, we used multiple decrement life-tables to decompose probabilities of death from birth to exact age 15 years, from exact age 15 years to exact age 50 years, and from exact age 50 years to exact age 75 years, and life expectancy at birth into major causes. For all quantities reported, we computed 95% uncertainty intervals (UIs). We constrained cause-specific fractions within each age-sex-country-year group to sum to all-cause mortality based on draws from the uncertainty distributions. Findings Global life expectancy for both sexes increased from 65.3 years (UI 65.0-65.6) in 1990, to 71.5 years (UI 71.0-71.9) in 2013, while the number of deaths increased from 47.5 million (UI 46.8-48.2) to 54.9 million (UI 53.6-56.3) over the same interval. Global progress masked variation by age and sex: for children, average absolute diff erences between countries decreased but relative diff erences increased. For women aged 25-39 years and older than 75 years and for men aged 20-49 years and 65 years and older, both absolute and relative diff erences increased. Decomposition of global and regional life expectancy showed the prominent role of reductions in age-standardised death rates for cardiovascular diseases and cancers in high-income regions, and reductions in child deaths from diarrhoea, lower respiratory infections, and neonatal causes in low-income regions. HIV/AIDS reduced life expectancy in southern sub-Saharan Africa. For most communicable causes of death both numbers of deaths and age-standardised death rates fell whereas for most non-communicable causes, demographic shifts have increased numbers of deaths but decreased age-standardised death rates. Global deaths from injury increased by 10.7%, from 4.3 million deaths in 1990 to 4.8 million in 2013; but age-standardised rates declined over the same period by 21%. For some causes of more than 100 000 deaths per year in 2013, age-standardised death rates increased between 1990 and 2013, including HIV/AIDS, pancreatic cancer, atrial fibrillation and flutter, drug use disorders, diabetes, chronic kidney disease, and sickle-cell anaemias. Diarrhoeal diseases, lower respiratory infections, neonatal causes, and malaria are still in the top five causes of death in children younger than 5 years. The most important pathogens are rotavirus for diarrhoea and pneumococcus for lower respiratory infections. Country-specific probabilities of death over three phases of life were substantially varied between and within regions. Interpretation For most countries, the general pattern of reductions in age-sex specifi c mortality has been associated with a progressive shift towards a larger share of the remaining deaths caused by non-communicable disease and injuries. Assessing epidemiological convergence across countries depends on whether an absolute or relative measure of inequality is used. Nevertheless, age-standardised death rates for seven substantial causes are increasing, suggesting the potential for reversals in some countries. Important gaps exist in the empirical data for cause of death estimates for some countries; for example, no national data for India are available for the past decade.
12.	Qian, Cheng, et al. (författare) A cloud service platform integrating additive and subtractive manufacturing with high resource efficiency 2019 Ingår i: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 241 Tidskriftsartikel (refereegranskat)abstract Cloud manufacturing has been studied for years, yet commercial implementations are still limited. The recent advances in information technology have stimulated the free sharing of additive and subtractive manufacturing (A/SM) resources through cloud services. Currently, due to the lack of a general method to model manufacturing capabilities, as well as the absence of an open platform to integrate business and manufacturing processes, it is difficult to integrate A/SM resources within one platform efficiently and seamlessly. In this research, a service encapsulation model for A/SM resources was described using ontology modeling technique. A collaborative cloud platform integrating A/SM was designed that can provide optimal production plans considering time, cost, quality, and energy waste during manufacturing. The proposed platform and models were demonstrated by a prototype system and tested in a case study, which showed the integrated platform can increase the utilization rate of resources while reducing energy consumption. This research has provided a practical tool for virtualization, integration, and configuration of A/SM resource with high efficiency. (C) 2019 Elsevier Ltd. All rights reserved.
13.	Ren, Shan, et al. (författare) A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions 2019 Ingår i: Journal of Cleaner Production. - : Elsevier. - 0959-6526 .- 1879-1786. ; 210, s. 1343-1365 Forskningsöversikt (refereegranskat)abstract Smart manufacturing has received increased attention from academia and industry in recent years, as it provides competitive advantage for manufacturing companies making industry more efficient and sustainable. As one of the most important technologies for smart manufacturing, big data analytics can uncover hidden knowledge and other useful information like relations between lifecycle decisions and process parameters helping industrial leaders to make more-informed business decisions in complex management environments. However, according to the literature, big data analytics and smart manufacturing were individually researched in academia and industry. To provide theoretical foundations for the research community to further develop scientific insights in applying big data analytics to smart manufacturing, it is necessary to summarize the existing research progress and weakness. In this paper, through combining the key technologies of smart manufacturing and the idea of ubiquitous servitization in the whole lifecycle, the term of sustainable smart manufacturing was coined. A comprehensive overview of big data in smart manufacturing was conducted, and a conceptual framework was proposed from the perspective of product lifecycle. The proposed framework allows analyzing potential applications and key advantages, and the discussion of current challenges and future research directions provides valuable insights for academia and industry. (C) 2018 Elsevier Ltd. All rights reserved.
14.	Ren, Shan, et al. (författare) A personalised operation and maintenance approach for complex products based on equipment portrait of product-service system 2023 Ingår i: Robotics and Computer-Integrated Manufacturing. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0736-5845 .- 1879-2537. ; 80 Tidskriftsartikel (refereegranskat)abstract Based on the holistic data of product-service system (PSS) delivery processes, equipment portrait can be used to describe personalised user requirements and conduct targeted analysis on the performance of complex products. Therefore, a promising application combining PSS and equipment portrait is to establish a more refined portrait model to improve the accuracy and applicability of operation and maintenance (OM) schemes for industrial products. However, studies in the above field are facing many challenges. For example, the research on equip-ment portrait in the industrial field is still in its infancy. PSS and equipment portrait are studied separately, and the overall solution that integrates PSS and equipment portrait for complex products OM service is almost vacant. A personalised OM approach for complex products (POMA-CP) is proposed to address these challenges. First, a framework of POMA-CP is developed to show how the processes of refined OM can be implemented. Then, a solution of POMA-CP based on the framework is designed. A multi-level case library, dynamic equipment portrait model, and case-pushing mechanism are established and developed. Active pushing of the best similar cases and automatic generation of service schemes are realised. Finally, an application scenario for a high-speed electric multiple units (EMU) bogie is presented to illustrate the feasibility and effectiveness of the proposed approach. Higher accuracy and applicability for service schemes are achieved, resulting in the efficient reusing of OM knowledge, proactive implementation of refined maintenance, and reducing maintenance cost and resource consumption.
15.	Ren, Shan, et al. (författare) An Advanced Operation Mode with Product-Service System Using Lifecycle Big Data and Deep Learning 2022 Ingår i: International Journal of Precision Engineering and Manufacturing-Green Technology. - : Springer Science and Business Media LLC. - 2288-6206 .- 2198-0810. ; 9:1, s. 287-303 Tidskriftsartikel (refereegranskat)abstract As a successful business strategy for enhancing environmental sustainability and decreasing the natural resource consumption of societies, the product-service system (PSS) has raised significant interests in the academic and industrial community. However, with the digitisation of the industry and the advancement of multisensory technologies, the PSS providers face many challenges. One major challenge is how the PSS providers can fully capture and efficiently analyse the operation and maintenance big data of different products and different customers in different conditions to obtain insights to improve their production processes, products and services. To address this challenge, a new operation mode and procedural approach are proposed for operation and maintenance of bigger cluster products, when these products are provided as a part of PSS and under exclusive control by the providers. The proposed mode and approach are driven by lifecycle big data of large cluster products and employs deep learning to train the neural networks to identify the fault features, thereby monitoring the products health status. This new mode is applied to a real case of a leading CNC machine provider to illustrate its feasibility. Higher accuracy and shortened time for fault prediction are realised, resulting in the providers saving of the maintenance and operation cost.
16.	Sun, Huibin, et al. (författare) Evaluation method of product-service performance 2012 Ingår i: International journal of computer integrated manufacturing (Print). - : Taylor & Francis. - 0951-192X .- 1362-3052. ; 25:2, s. 150-157 Tidskriftsartikel (refereegranskat)abstract In order to analyse productâ€“service performance (PSP) during productâ€“service systems’ (PSSs) running time, a PSP evaluation method is proposed. Interrelationship between a PSS’s provider and accepter is defined as productâ€“service relationship (PSR). Concepts of productâ€“service network and productâ€“service chain are put forward to describe interrelationship among PSRs from different perspectives. The concept of PSP is proposed to evaluate interrelationship among PSR. Detailed evaluation indexes are designed to measure the five factors of PSP, including time, quality, cost, stability and reliability. A case study is provided to test the validity and efficiency of the evaluation model of PSP. The analysis results show that the evaluation model can make good efforts in benefiting productâ€“service platform’s running, monitoring and optimising.
17.	Wang, Jin, et al. (författare) Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods 2020 Ingår i: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 247 Tidskriftsartikel (refereegranskat)abstract 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.
18.	Wang, Jin, et al. (författare) Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop 2019 Ingår i: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 6:2, s. 2518-2531 Tidskriftsartikel (refereegranskat)abstract 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.
19.	Zhang, Yingfeng, et al. (författare) A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products 2017 Ingår i: Journal of Cleaner Production. - : Elsevier. - 0959-6526 .- 1879-1786. ; 142:2, s. 626-641 Tidskriftsartikel (refereegranskat)abstract 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.
20.	Zhang, Yingfeng, et al. (författare) A big data driven analytical framework for energy-intensive manufacturing industries 2018 Ingår i: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 197, s. 57-72 Tidskriftsartikel (refereegranskat)abstract 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.
21.	Zhang, Yingfeng, et al. (författare) A framework for Big Data driven product lifecycle management 2017 Ingår i: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 159, s. 229-240 Tidskriftsartikel (refereegranskat)abstract 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.
22.	Zhang, Yingfeng, et al. (författare) Agent-based intelligent medical diagnosis system for patients 2015 Ingår i: Technology and Health Care. - : IOS Press. - 0928-7329 .- 1878-7401. ; 23, s. S397-S410 Tidskriftsartikel (refereegranskat)abstract BACKGROUND: According to the analysis of the challenges faced by the current public health circumstances such as the sharp increase in elderly patients, limited medical personnel, resources and technology, the agent-based intelligent medical diagnosis system for patients (AIMDS) is proposed in this research. OBJECTIVE: Based on advanced sensing technology and professional medical knowledge, the AIMDS can output the appropriate medical prescriptions and food prohibition when the physical signs and symptoms of the patient are inputted. METHODS: Three core modules are designed include sensing module, intuition-based fuzzy set theory/medical diagnosis module, and medical knowledge module. RESULTS: The result shows that the optimized prescription can reach the desired level, with great curative effect for patient disease, through a case study simulation. CONCLUSION: The presented AIMDS can integrate sensor technique and intelligent medical diagnosis methods to make an accurate diagnosis, resulting in three-type of optimized descriptions for patient selection.
23.	Zhang, Yingfeng, et al. (författare) Clustering and Genetic Algorithm Based Hybrid Flowshop Scheduling with Multiple Operations 2014 Ingår i: Mathematical problems in engineering (Print). - : Hindawi Publishing Corporation. - 1024-123X .- 1563-5147. ; 2014 Tidskriftsartikel (refereegranskat)abstract This research is motivated by a flowshop scheduling problem of our collaborative manufacturing company for aeronautic products. The heat-treatment stage (HTS) and precision forging stage (PFS) of the case are selected as a two-stage hybrid flowshop system. In HTS, there are four parallel machines and each machine can process a batch of jobs simultaneously. In PFS, there are two machines. Each machine can install any module of the four modules for processing the workpeices with different sizes. The problem is characterized by many constraints, such as batching operation, blocking environment, and setup time and working time limitations of modules, and so forth. In order to deal with the above special characteristics, the clustering and genetic algorithm is used to calculate the good solution for the two-stage hybrid flowshop problem. The clustering is used to group the jobs according to the processing ranges of the different modules of PFS. The genetic algorithm is used to schedule the optimal sequence of the grouped jobs for the HTS and PFS. Finally, a case study is used to demonstrate the efficiency and effectiveness of the designed genetic algorithm.
24.	Zhang, Yingfeng, et al. (författare) Game theory based real-time multi-objective flexible job shop scheduling considering environmental impact 2017 Ingår i: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 167, s. 665-679 Tidskriftsartikel (refereegranskat)abstract 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.
25.	Zhang, Yingfeng, et al. (författare) Game theory based real-time shop floor scheduling strategy and method for cloud manufacturing 2017 Ingår i: International Journal of Intelligent Systems. - : John Wiley & Sons. - 0884-8173 .- 1098-111X. ; 32:4, s. 437-463 Tidskriftsartikel (refereegranskat)abstract With the rapid advancement and widespread application of information and sensor technologies in manufacturing shop floor, the typical challenges that cloud manufacturing is facing are the lack of real‐time, accurate, and value‐added manufacturing information, the efficient shop floor scheduling strategy, and the method based on the real‐time data. To achieve the real‐time data‐driven optimization decision, a dynamic optimization model for flexible job shop scheduling based on game theory is put forward to provide a new real‐time scheduling strategy and method. Contrast to the traditional scheduling strategy, each machine is an active entity that will request the processing tasks. Then, the processing tasks will be assigned to the optimal machines according to their real‐time status by using game theory. The key technologies such as game theory mathematical model construction, Nash equilibrium solution, and optimization strategy for process tasks are designed and developed to implement the dynamic optimization model. A case study is presented to demonstrate the efficiency of the proposed strategy and method, and real‐time scheduling for four kinds of exceptions is also discussed.
26.	Zhang, Yingfeng, et al. (författare) Production System Performance Prediction Model Based on Manufacturing Big Data 2015 Ingår i: ICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control. - : IEEE. - 9781479980697 ; , s. 277-280 Konferensbidrag (refereegranskat)abstract Existing production systems are short of real-time performance status of production process active perception, resulting in the production abnormal conditions processed lag, leading to the frequency problems of deviations in production tasks execution and planning. To address this problem, in this research, an advanced identification technology is extended to the manufacturing field to acquire the real-time performance data. Based on the sensed real-time manufacturing data, this paper presents a prediction method of production system performance by applying the Dynamic Bayesian Networks (DBN) theory and methods. It aims to achieve the prediction of the performance status of production system and potential anomalies, and to provide the important and abundant prediction information for real-time production control.
27.	Zhang, Yingfeng, et al. (författare) Real-time shop-floor production performance analysis method for the internet of manufacturing things 2014 Ingår i: Advances in Mechanical Engineering. - : Hindawi Publishing Corporation. - 1687-8132 .- 1687-8140. ; 6 Tidskriftsartikel (refereegranskat)abstract Typical challenges that manufacturing enterprises are facing now are compounded by lack of timely, accurate, and consistent information of manufacturing resources. As a result, it is difficult to analyze the real-time production performance for the shopfloor. In this paper, the definition and overall architecture of the internet of manufacturing things is presented to provide a new paradigm by extending the techniques of internet of things (IoT) to manufacturing field. Under this architecture, the real-time primitive events which occurred at different manufacturing things such as operators, machines, pallets, key materials, and so forth can be easily sensed. Based on these distributed primitive events, a critical event model is established to automatically analyze the real-time production performance. Here, the up-level production performance analysis is regarded as a series of critical events, and the real-time value of each critical event can be easily calculated according to the logical and sequence relationships among these multilevel events. Finally, a case study is used to illustrate how to apply the designed methods to analyze the real-time production performance.
28.	Zhang, Yingfeng, et al. (författare) Smart box-enabled product–service system for cloud logistics 2016 Ingår i: International Journal of Production Research. - : Taylor & Francis. - 0020-7543 .- 1366-588X. ; 54:22, s. 6693-6706 Tidskriftsartikel (refereegranskat)abstract 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.

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