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Yang Liu received the M.Sc. (Tech.) degree in telecommunication engineering and D.Sc. (Tech.) degree in industrial management from the University of Vaasa, Finland, in 2005 and 2010, respectively. He is currently a tenured Associate Professor and Doctoral Supervisor with the Department of Management and Engineering, Linköping University, Sweden; a Visiting Faculty with the Department of Production, University of Vaasa, Finland; and a Chair Professor with Jinan University, China. Meanwhile, he is an Adjunct/Visiting Professor at multiple other universities. His research interests include sustainable smart manufacturing, product service innovation, decision support system, competitive advantage, control system, autonomous robot, signal processing, and pattern recognition. Prof. Liu has authored or co-authored more than 120 peer-reviewed scientific articles. His publications have appeared in several distinguished journals, and some ranked as top 0.1% ESI Hot Papers and top 1% ESI Highly Cited Papers. He serves as an Associate Editor of the prestigious Journal of Cleaner Production (2019 Impact Factor: 7.246) as well as a referee in over 50 SCI leading journals and external reviewer for NSERC of Canada, CONICYT of Chile.
Aiming at human-robot collaboration in manufacturing, the operator's safety is the primary issue during the manufacturing operations. This paper presents a deep reinforcement learning approach to realize the real-time collision-free motion planning of an industrial robot for human-robot collaboration. Firstly, the safe human-robot collaboration manufacturing problem is formulated into a Markov decision process, and the mathematical expression of the reward function design problem is given. The goal is that the robot can autonomously learn a policy to reduce the accumulated risk and assure the task completion time during human-robot collaboration. To transform our optimization object into a reward function to guide the robot to learn the expected behaviour, a reward function optimizing approach based on the deterministic policy gradient is proposed to learn a parameterized intrinsic reward function. The reward function for the agent to learn the policy is the sum of the intrinsic reward function and the extrinsic reward function. Then, a deep reinforcement learning algorithm intrinsic reward-deep deterministic policy gradient (IRDDPG), which is the combination of the DDPG algorithm and the reward function optimizing approach, is proposed to learn the expected collision avoidance policy. Finally, the proposed algorithm is tested in a simulation environment, and the results show that the industrial robot can learn the expected policy to achieve the safety assurance for industrial human-robot collaboration without missing the original target. Moreover, the reward function optimizing approach can help make up for the designed reward function and improve policy performance.
Quan Liu; Zhihao Liu; Bo Xiong; Wenjun Xu; Yang Liu. Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function. Advanced Engineering Informatics 2021, 49, 101360 .
AMA StyleQuan Liu, Zhihao Liu, Bo Xiong, Wenjun Xu, Yang Liu. Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function. Advanced Engineering Informatics. 2021; 49 ():101360.
Chicago/Turabian StyleQuan Liu; Zhihao Liu; Bo Xiong; Wenjun Xu; Yang Liu. 2021. "Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function." Advanced Engineering Informatics 49, no. : 101360.
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 provider’s saving of the maintenance and operation cost.
Shan Ren; Yingfeng Zhang; Tomohiko Sakao; Yang Liu; Ruilong Cai. An Advanced Operation Mode with Product-Service System Using Lifecycle Big Data and Deep Learning. International Journal of Precision Engineering and Manufacturing-Green Technology 2021, 1 -17.
AMA StyleShan Ren, Yingfeng Zhang, Tomohiko Sakao, Yang Liu, Ruilong Cai. An Advanced Operation Mode with Product-Service System Using Lifecycle Big Data and Deep Learning. International Journal of Precision Engineering and Manufacturing-Green Technology. 2021; ():1-17.
Chicago/Turabian StyleShan Ren; Yingfeng Zhang; Tomohiko Sakao; Yang Liu; Ruilong Cai. 2021. "An Advanced Operation Mode with Product-Service System Using Lifecycle Big Data and Deep Learning." International Journal of Precision Engineering and Manufacturing-Green Technology , no. : 1-17.
To improve industrial sustainability performance in manufacturing, energy management and optimisation are key levers. This is particularly true for aluminium extrusions manufacturing —an energy-intensive production system with considerable environmental impacts. Many energy management and optimisation approaches have been studied to relieve such negative impact. However, the effectiveness of these approaches is compromised without the support of refined supply-side energy consumption information. Industrial internet of things provides opportunities to acquire refined energy consumption information in its data-rich environment but also poses a range of difficulties in implementation. The existing sensors cannot directly obtain the energy consumption at the granularity of a specific job. To acquire that refined energy consumption information, a supply-side energy modelling method based on existing industrial internet of things devices for energy-intensive production systems is proposed in this paper. First, the job-specified production event concept is proposed, and the layout of the data acquisition network is designed to obtain the event elements. Second, the mathematical models are developed to calculate the energy consumption of the production event in three process modes. Third, the energy consumption information of multiple manufacturing element dimensions can be derived from the mathematical models, and therefore, the energy consumption information on multiple dimensions is easily scaled. Finally, a case of refined energy cost accounting is studied to demonstrate the feasibility of the proposed models.
Chen Peng; Tao Peng; Yang Liu; Martin Geissdoerfer; Steve Evans; Renzhong Tang. Industrial Internet of Things enabled supply-side energy modelling for refined energy management in aluminium extrusions manufacturing. Journal of Cleaner Production 2021, 301, 126882 .
AMA StyleChen Peng, Tao Peng, Yang Liu, Martin Geissdoerfer, Steve Evans, Renzhong Tang. Industrial Internet of Things enabled supply-side energy modelling for refined energy management in aluminium extrusions manufacturing. Journal of Cleaner Production. 2021; 301 ():126882.
Chicago/Turabian StyleChen Peng; Tao Peng; Yang Liu; Martin Geissdoerfer; Steve Evans; Renzhong Tang. 2021. "Industrial Internet of Things enabled supply-side energy modelling for refined energy management in aluminium extrusions manufacturing." Journal of Cleaner Production 301, no. : 126882.
In the aftermath of large-scale natural disasters, supply shortage and inequitable distribution cause various losses, hindering humanitarian supply chains’ performance. The optimal decisions are difficult due to the complexity arising from the multi-period post-disaster consideration, uncertainty of supplies, hierarchal decision levels and conflicting objectives in sustainable humanitarian supply chains (SHSCs). This paper formulates the problem as a fuzzy tri-objective bi-level integer programming model to minimize the unmet demand rate, potential environmental risks, emergency costs on the upper level of decision hierarchy and maximize survivors’ perceived satisfaction on the lower level of decision hierarchy. A hybrid global criterion method is devised to incorporate a primal-dual algorithm, expected value and branch-and-bound approach in solving the model. A case study using data from the Wenchuan earthquake is presented to evaluate the proposed model. Study results indicate that the hybrid global criterion method guides an optimal strategy for such a complex problem within a reasonable computational time. More attention should be attached to the environmental and economic sustainability aspects in SHSCs after golden rescue stage. The proposed bi-level optimization model has the advantages of reducing the total unmet demand rate, total potential environmental risks and total emergency costs. If the decision-agents with higher authorities act as the leaders with dominant power in SHSCs, the optimal decisions, respectively taking hierarchical and horizontal relationships into account would result in equal performance.
Cejun Cao; Yang Liu; Ou Tang; Xuehong Gao. A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains. International Journal of Production Economics 2021, 235, 108081 .
AMA StyleCejun Cao, Yang Liu, Ou Tang, Xuehong Gao. A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains. International Journal of Production Economics. 2021; 235 ():108081.
Chicago/Turabian StyleCejun Cao; Yang Liu; Ou Tang; Xuehong Gao. 2021. "A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains." International Journal of Production Economics 235, no. : 108081.
In light of a circular economy, to encourage core returns, the remanufacturer charges a deposit and refund it to the customer based on quality inspection of cores. Generally, two types of classification errors exist and interact with each other during the inspection process: either low-quality cores are sorted as remanufacturable, or high-quality cores are sorted as non-remanufacturable. The remanufacturer needs to choose refund policies and determine a reasonable deposit value, considering customers’ potential responses. This paper firstly develops analytical solutions for these issues within a game theory framework. The effect of inspection information transparency is evaluated by comparing two settings: the information of inspection errors is available to customers or not. The study results show the advantage of inspection information transparency from the remanufacturer’s perspective. The analysis indicates the importance of avoiding overestimating customers’ payoff of products and the significance of inspection accuracy. The study also highlights that the salvage value of different cores significantly influences the remanufacturer’s profits, and the improvement of inspection accuracy does not necessarily reduce the customer’s return of low-quality cores.
Ou Tang; Yang Liu; Zhengang Guo; Shuoguo Wei. Refund policies and core classification errors in the presence of customers’ choice behaviour in remanufacturing. International Journal of Production Research 2021, 59, 3553 -3571.
AMA StyleOu Tang, Yang Liu, Zhengang Guo, Shuoguo Wei. Refund policies and core classification errors in the presence of customers’ choice behaviour in remanufacturing. International Journal of Production Research. 2021; 59 (12):3553-3571.
Chicago/Turabian StyleOu Tang; Yang Liu; Zhengang Guo; Shuoguo Wei. 2021. "Refund policies and core classification errors in the presence of customers’ choice behaviour in remanufacturing." International Journal of Production Research 59, no. 12: 3553-3571.
With the global energy crisis and environmental issues becoming severe, more attention has been paid to production scheduling considering energy consumption than ever before. However, in the context of intelligent manufacturing, most studies apply the industrial internet of things (IIoT) to improve energy efficiency. It may cause the real-time data in the workshop unable to be collected and treated timely, thus affecting the real-time decision-making of the scheduling system. Edge computing (EC) can make full use of embedded computing capabilities of field devices to process real-time data and reduce the response time of making production decisions. Therefore, in this study, an overall architecture of the EC-IIoT based distributed and flexible job shop real-time scheduling (DFJS-RS) is proposed to enhance the real-time decision-making capability of the scheduling system. The DFJS-RS method, which consists of the task assignment method of the shop floor layer and the RS method of the flexible manufacturing units (FMUs) layer, is designed and developed. An evolutionary game-based solver method is adopted to obtain the optimal allocation. Finally, a case study is employed to validate the DFJS-RS method. The results show that compared with the existing production scheduling method, the DFJS-RS method can improve energy efficiency by up to 26%. This improvement can further promote cleaner production (CP) and sustainable societal development.
Jin Wang; Yang Liu; Shan Ren; Chuang Wang; Wenbo Wang. Evolutionary game based real-time scheduling for energy-efficient distributed and flexible job shop. Journal of Cleaner Production 2021, 293, 126093 .
AMA StyleJin Wang, Yang Liu, Shan Ren, Chuang Wang, Wenbo Wang. Evolutionary game based real-time scheduling for energy-efficient distributed and flexible job shop. Journal of Cleaner Production. 2021; 293 ():126093.
Chicago/Turabian StyleJin Wang; Yang Liu; Shan Ren; Chuang Wang; Wenbo Wang. 2021. "Evolutionary game based real-time scheduling for energy-efficient distributed and flexible job shop." Journal of Cleaner Production 293, no. : 126093.
As a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert’ judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring.
Lianhui Li; Ting Qu; Yang Liu; Ray Y. Zhong; Guanying Xu; Hongxia Sun; Yang Gao; Bingbing Lei; Chunlei Mao; Yanghua Pan; Fuwei Wang; Cong Ma. Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin. IEEE Access 2020, 8, 174988 -175008.
AMA StyleLianhui Li, Ting Qu, Yang Liu, Ray Y. Zhong, Guanying Xu, Hongxia Sun, Yang Gao, Bingbing Lei, Chunlei Mao, Yanghua Pan, Fuwei Wang, Cong Ma. Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin. IEEE Access. 2020; 8 ():174988-175008.
Chicago/Turabian StyleLianhui Li; Ting Qu; Yang Liu; Ray Y. Zhong; Guanying Xu; Hongxia Sun; Yang Gao; Bingbing Lei; Chunlei Mao; Yanghua Pan; Fuwei Wang; Cong Ma. 2020. "Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin." IEEE Access 8, no. : 174988-175008.
Product-service system (PSS) solution selection is of great significance to better meet the personalised needs of customers and ensure the subsequent implementation. The problems of incomplete index system, difficulty to obtain the value of the qualitative index and unreasonable single index weighting have a significant impact on the decision-making of PSS solution selection. In response to these problems, a decision-making framework of PSS solution selection is constructed. A comprehensive index system is established from the perspectives of multiple stakeholders. Expert evaluating with the fuzzy number and multi-expert evaluation opinion combination is adopted for index value solving. Integration of objective and subjective weights is achieved based on the multi-weight information consistency model and the candidate PSS solutions are ranked by technique for order preference by similarity to an ideal solution finally. An application case of automobile PSS solution selection is given to verify the effectiveness and rationality of the constructed decision-making framework.
Lianhui Li; Chunlei Mao; Bingbing Lei; Yang Gao; Yang Liu; George Q. Huang. Decision‐making of product‐service system solution selection based on integrated weight and technique for order preference by similarity to an ideal solution. IET Collaborative Intelligent Manufacturing 2020, 2, 102 -108.
AMA StyleLianhui Li, Chunlei Mao, Bingbing Lei, Yang Gao, Yang Liu, George Q. Huang. Decision‐making of product‐service system solution selection based on integrated weight and technique for order preference by similarity to an ideal solution. IET Collaborative Intelligent Manufacturing. 2020; 2 (3):102-108.
Chicago/Turabian StyleLianhui Li; Chunlei Mao; Bingbing Lei; Yang Gao; Yang Liu; George Q. Huang. 2020. "Decision‐making of product‐service system solution selection based on integrated weight and technique for order preference by similarity to an ideal solution." IET Collaborative Intelligent Manufacturing 2, no. 3: 102-108.
In the uncertain entrepreneurial ecosystem, scholarly knowledge is bounded by the sustainable growth of entrepreneurial enterprises. Moreover, there is a lack of consensus in academic circles on the relationship between entrepreneurial experience and entrepreneurial performance. In adopting the meta-analysis method, we found a significant relationship between entrepreneurial experience and entrepreneurial performance based on an investigation of 45 independent samples (N = 18,752). We also examined theoretically derived moderators of this relationship referring to firm age, industry condition and experience type to test whether the moderating effects can explain the inconsistent research results on the relationship between entrepreneurial experience and entrepreneurial performance. The relationship was stronger for the high-tech industry than for low-tech industry, for the early business stage than for late business stage and for start-up experience compared to management experience, work experience and industry experience. Our research findings are meaningful for practitioners to achieve sustainable growth by better preserving and coordinating entrepreneurial experience in a dynamic environment. Further, these findings are also important for future research to analyze the factors triggering the heterogeneity of entrepreneurial experience and to investigate the extent to which the start-up experience is more capable of promoting entrepreneurial performance.
Huatao Peng; Chen Zhou; Yang Liu. Entrepreneurial Experience and Performance: From the Aspect of Sustainable Growth of Enterprises. Sustainability 2020, 12, 7351 .
AMA StyleHuatao Peng, Chen Zhou, Yang Liu. Entrepreneurial Experience and Performance: From the Aspect of Sustainable Growth of Enterprises. Sustainability. 2020; 12 (18):7351.
Chicago/Turabian StyleHuatao Peng; Chen Zhou; Yang Liu. 2020. "Entrepreneurial Experience and Performance: From the Aspect of Sustainable Growth of Enterprises." Sustainability 12, no. 18: 7351.
The research on ecotourism has attracted much attention in recent years with the increasing awareness of sustainable development and environmental protection. In the development and construction of ecotourism in ecologically fragile areas, however, conflicts of interest between stakeholders often negatively affect the efficiency and effectiveness of ecotourism construction. This paper applied evolutionary game theory to analyse the evolutionary stable strategies of local governments, tourism enterprises and residents in the development and construction of ecotourism in ecologically fragile areas, to explore the mechanism that influences the sustainable development policies. The evolutionary stable strategies of the game were calculated and the dynamic simulation of the model was also discussed by using a system dynamics method to analyse the stability of interaction among the stakeholders and determine an equilibrium solution. The simulation results showed that the strategic choices of the three stakeholders fluctuate repeatedly, which indicated that there was no evolutionary stability strategy in the interaction among the current stakeholders. Therefore, an optimized dynamic penalty-incentive control method was proposed to control the fluctuation, after which the simulation results showed that the optimized dynamic penalty-incentive control method can not only effectively suppress the fluctuation but also obtain an ideal evolutionary stable strategy. Then, the cooperation mode can be changed from any original status into the desired target. This research provides valuable information for design appropriate policies and business modes to coordinate the interests of stakeholders and promote the development of ecotourism, as well as contributes to the environmental and sustainability research and practice.
Wenke Wang; Linyun Feng; Tao Zheng; Yang Liu. The sustainability of ecotourism stakeholders in ecologically fragile areas: Implications for cleaner production. Journal of Cleaner Production 2020, 279, 123606 .
AMA StyleWenke Wang, Linyun Feng, Tao Zheng, Yang Liu. The sustainability of ecotourism stakeholders in ecologically fragile areas: Implications for cleaner production. Journal of Cleaner Production. 2020; 279 ():123606.
Chicago/Turabian StyleWenke Wang; Linyun Feng; Tao Zheng; Yang Liu. 2020. "The sustainability of ecotourism stakeholders in ecologically fragile areas: Implications for cleaner production." Journal of Cleaner Production 279, no. : 123606.
The product-service system (PSS) business model has received increasing attention in equipment maintenance studies, as it has the potential to provide high value-added services for equipment users and construct ethical principles for equipment providers to support the implementation of circular economy. However, the PSS providers in equipment industry are facing many challenges when implementing Industry 4.0 technologies. One important challenge is how to fully collect and analyse the operational data of different equipment and diverse users in widely varied conditions to make the PSS providers create innovative equipment management services for their customers. To address this challenge, an active preventive maintenance approach for complex equipment is proposed. Firstly, a novel PSS operation mode was developed, where complex equipment is offered as a part of PSS and under exclusive control by the providers. Then, a solution of equipment preventive maintenance based on the operation mode was designed. A deep neural network was trained to predict the remaining effective life of the key components and thereby, it can pre-emptively assess the health status of equipment. Finally, a real-world industrial case of a leading CNC machine provider was developed to illustrate the feasibility and effectiveness of the proposed approach. Higher accuracy for predicting the remaining effective life was achieved, which resulted in predictive identification of the fault features, proactive implementation of the preventive maintenance, and reduction of the PSS providers’ maintenance costs and resource consumption. Consequently, the result shows that it can help PSS providers move towards more ethical and sustainable directions.
Ning Wang; Shan Ren; Yang Liu; Miying Yang; Jin Wang; Donald Huisingh. An active preventive maintenance approach of complex equipment based on a novel product-service system operation mode. Journal of Cleaner Production 2020, 277, 123365 .
AMA StyleNing Wang, Shan Ren, Yang Liu, Miying Yang, Jin Wang, Donald Huisingh. An active preventive maintenance approach of complex equipment based on a novel product-service system operation mode. Journal of Cleaner Production. 2020; 277 ():123365.
Chicago/Turabian StyleNing Wang; Shan Ren; Yang Liu; Miying Yang; Jin Wang; Donald Huisingh. 2020. "An active preventive maintenance approach of complex equipment based on a novel product-service system operation mode." Journal of Cleaner Production 277, no. : 123365.
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.
Shuaiyin Ma; Yingfeng Zhang; Yang Liu; Haidong Yang; Jingxiang Lv; Shan Ren. Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries. Journal of Cleaner Production 2020, 274, 123155 .
AMA StyleShuaiyin Ma, Yingfeng Zhang, Yang Liu, Haidong Yang, Jingxiang Lv, Shan Ren. Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries. Journal of Cleaner Production. 2020; 274 ():123155.
Chicago/Turabian StyleShuaiyin Ma; Yingfeng Zhang; Yang Liu; Haidong Yang; Jingxiang Lv; Shan Ren. 2020. "Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries." Journal of Cleaner Production 274, no. : 123155.
We study a new supply chain configuration problem to optimize the amount of carbon emission in the context of a service guarantee modelling framework, called supply chain configuration problem with low carbon emission (SCCP-LCE). A novel feature of our addressed problem is the explicit consideration of carbon emission cap and trading price in the supply chain configuration setting with operating capacity. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model, and optimally solved by a custom designed dynamic programming algorithm. A case study and computational experiment are performed to examine the behaviour of optimal SCCP-LCE configurations, and the effects of key input parameters: carbon emission cap, trading price, and operating capacity. Our results suggest that government-imposed carbon emission policies, in terms of emission cap and trading price, have significant impacts and interactive effects on the optimal supply chain configuration and performance, including the safety stock cost and carbon emission cost. Our model and methodology offer a new analytical framework to prescribe data-driven decision support for both firms and governmental/environmental agencies to control carbon emission, while achieving optimal business and social benefits.
Duxian Nie; Haitao Li; Ting Qu; Yang Liu; Congdong Li. Optimizing supply chain configuration with low carbon emission. Journal of Cleaner Production 2020, 271, 122539 .
AMA StyleDuxian Nie, Haitao Li, Ting Qu, Yang Liu, Congdong Li. Optimizing supply chain configuration with low carbon emission. Journal of Cleaner Production. 2020; 271 ():122539.
Chicago/Turabian StyleDuxian Nie; Haitao Li; Ting Qu; Yang Liu; Congdong Li. 2020. "Optimizing supply chain configuration with low carbon emission." Journal of Cleaner Production 271, no. : 122539.
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.
Zhengang Guo; Yingfeng Zhang; Jingxiang Lv; Yang Liu; Ying Liu. An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization. IEEE Transactions on Intelligent Transportation Systems 2020, 1 -12.
AMA StyleZhengang Guo, Yingfeng Zhang, Jingxiang Lv, Yang Liu, Ying Liu. An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization. IEEE Transactions on Intelligent Transportation Systems. 2020; (99):1-12.
Chicago/Turabian StyleZhengang Guo; Yingfeng Zhang; Jingxiang Lv; Yang Liu; Ying Liu. 2020. "An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization." IEEE Transactions on Intelligent Transportation Systems , no. 99: 1-12.
Online co-creation allows companies to leverage external sources of knowledge to sustain product or service innovation. Users’ knowledge is regarded as such a potential source. Understanding user behaviors and innovation types is vital to improving a company’s sustainable innovation. Many prior studies mainly categorized online community members into core and peripheral members based on their posting frequencies. However, little research has gone beyond that categorization and examined whether there may be different types of core members who may contribute to product or service innovation differently, especially in the context of co-creation. The objectives of this study are three-fold: (1) to identify core members of a company-hosted online co-creation community automatically by considering several dimensions of individual members, including posting behavior, the generated content, and social network features; (2) to categorize and compare the contributions of different types of core members in the community, aiming to identify community members who may play leadership roles in sustainable innovation; and (3) to investigate the influence of those different types of core members on other community members. The data collected from a company-hosted online co-creation community in China were analyzed. Through analysis, we developed a novel innovation-oriented topology of core community members consisting of eight types. Based on Practice Theory, we also explored how those different types of core community members may influence other members’ behavior. Finally, based on the findings, we propose strategies and guidelines for practitioners to keep different types of community members actively engaged in online co-creation and to manage sustainable innovation practice better.
Yu Wang; Cheng Li; Dongsong Zhang; Jiacong Wu; Yang Liu; Wu Jiacong. A deeper investigation of different types of core users and their contributions for sustainable innovation in a company-hosted online co-creation community. Journal of Cleaner Production 2020, 256, 120397 .
AMA StyleYu Wang, Cheng Li, Dongsong Zhang, Jiacong Wu, Yang Liu, Wu Jiacong. A deeper investigation of different types of core users and their contributions for sustainable innovation in a company-hosted online co-creation community. Journal of Cleaner Production. 2020; 256 ():120397.
Chicago/Turabian StyleYu Wang; Cheng Li; Dongsong Zhang; Jiacong Wu; Yang Liu; Wu Jiacong. 2020. "A deeper investigation of different types of core users and their contributions for sustainable innovation in a company-hosted online co-creation community." Journal of Cleaner Production 256, no. : 120397.
Waste electrical and electronic equipment utilization is increasingly regulated in China, through the implementation of fund policy, to promote sustainable development. This research discusses the economic and social impacts of the fund policy by employing a game-theory-based model and simulating a new fund reduction policy to achieve multi-objective optimization. The results suggest that (i) the fund policy may create trade-offs between remanufacturing profits and economic burdens onto primary manufacturers; (ii) The manufacturers are not as favorable to eco-design as expected, resulting to remanufacturers’ overdependence on governmental subsidies. Strategically, a fund reduction policy is devised for addressing the negative effects of the fund policy. This reduction policy can provide better incentives for manufacturers to carry out eco-design. It may also improve the profits of both manufacturers and remanufacturers, social welfare and greatly reduce the environmental costs. The findings would provide insights into e-waste management and recycling policies in China and the developing world as in general.
Guangfu Liu; Yi Xu; Tingting Tian; Tao Wang; Yang Liu. The impacts of China’s fund policy on waste electrical and electronic equipment utilization. Journal of Cleaner Production 2019, 251, 119582 .
AMA StyleGuangfu Liu, Yi Xu, Tingting Tian, Tao Wang, Yang Liu. The impacts of China’s fund policy on waste electrical and electronic equipment utilization. Journal of Cleaner Production. 2019; 251 ():119582.
Chicago/Turabian StyleGuangfu Liu; Yi Xu; Tingting Tian; Tao Wang; Yang Liu. 2019. "The impacts of China’s fund policy on waste electrical and electronic equipment utilization." Journal of Cleaner Production 251, no. : 119582.
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.
Yang Liu; Yingfeng Zhang; Shan Ren; Miying Yang; Yutao Wang; Donald Huisingh. How can smart technologies contribute to sustainable product lifecycle management? Journal of Cleaner Production 2019, 249, 119423 .
AMA StyleYang Liu, Yingfeng Zhang, Shan Ren, Miying Yang, Yutao Wang, Donald Huisingh. How can smart technologies contribute to sustainable product lifecycle management? Journal of Cleaner Production. 2019; 249 ():119423.
Chicago/Turabian StyleYang Liu; Yingfeng Zhang; Shan Ren; Miying Yang; Yutao Wang; Donald Huisingh. 2019. "How can smart technologies contribute to sustainable product lifecycle management?" Journal of Cleaner Production 249, no. : 119423.
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 IoT-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.
Jin Wang; Jiahao Yang; Yingfeng Zhang; Shan Ren; Yang Liu. Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods. Journal of Cleaner Production 2019, 247, 119093 .
AMA StyleJin Wang, Jiahao Yang, Yingfeng Zhang, Shan Ren, Yang Liu. Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods. Journal of Cleaner Production. 2019; 247 ():119093.
Chicago/Turabian StyleJin Wang; Jiahao Yang; Yingfeng Zhang; Shan Ren; Yang Liu. 2019. "Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods." Journal of Cleaner Production 247, no. : 119093.
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.
Gunjan Yadav; Sunil Luthra; Donald Huisingh; Sachin Kumar Mangla; Balkrishna Eknath Narkhede; Yang Liu. Development of a lean manufacturing framework to enhance its adoption within manufacturing companies in developing economies. Journal of Cleaner Production 2019, 245, 118726 .
AMA StyleGunjan Yadav, Sunil Luthra, Donald Huisingh, Sachin Kumar Mangla, Balkrishna Eknath Narkhede, Yang Liu. Development of a lean manufacturing framework to enhance its adoption within manufacturing companies in developing economies. Journal of Cleaner Production. 2019; 245 ():118726.
Chicago/Turabian StyleGunjan Yadav; Sunil Luthra; Donald Huisingh; Sachin Kumar Mangla; Balkrishna Eknath Narkhede; Yang Liu. 2019. "Development of a lean manufacturing framework to enhance its adoption within manufacturing companies in developing economies." Journal of Cleaner Production 245, no. : 118726.
As a critical part of machining, cutting tools are of great importance to sustainability enhancement. Normally, they are underused, resulting in huge waste. However, the lack of reliable support leads to a high risk on improving the cutting tool utilization. Aiming at this problem, this paper proposes an approach to enhance the cutting tool sustainability. A non-linear cutting tool remaining useful life prediction model is developed based on tool wear historical data. Probability distribution function and cumulative distribution function are used to quantize the uncertainty of the prediction. Under a constant machining condition, a cutting tool life is extended according to its specific remaining useful life prediction, rather than a unified one. Under various machining conditions, machining parameters are optimized to improve efficiency or capability. Cutting tool sustainability is assessed in economic, environmental and social dimensions. Experimental study verifies that both material removal rate and material removal volume are improved. Carbon emission and cutting tool cost are also reduced. The balance between benefit and risk is achieved by assigning a reasonable confidence level. Cutting tool sustainability can be enhanced by improving cutting tool utilization at controllable risk.
Huibin Sun; Yang Liu; Junlin Pan; Jiduo Zhang; Wei Ji. Enhancing cutting tool sustainability based on remaining useful life prediction. Journal of Cleaner Production 2019, 244, 118794 .
AMA StyleHuibin Sun, Yang Liu, Junlin Pan, Jiduo Zhang, Wei Ji. Enhancing cutting tool sustainability based on remaining useful life prediction. Journal of Cleaner Production. 2019; 244 ():118794.
Chicago/Turabian StyleHuibin Sun; Yang Liu; Junlin Pan; Jiduo Zhang; Wei Ji. 2019. "Enhancing cutting tool sustainability based on remaining useful life prediction." Journal of Cleaner Production 244, no. : 118794.