Dynamic assessment of sustainable manufacturing capability based on correlation relationship for industrial cloud robotics

The industrial cloud robotics (ICRs) integrates distributed industrial robot resources in various places to support complex task processing for multi-resource service requirements, and manufacturing capability assessment plays an important role in determining the optimal service composition to realize the value-added of ICRs resources. However, ignoring correlation relationship within the robot composition, the traditional evaluation method cannot reflect the impact of the robot individuals on the overall manufacturing capability of the ICRs composition. In addition, the problems of excessive resource consumption and environmental pollution in the manufacturing industry are becoming increasingly serious. The paper proposes a dynamic assessment method of sustainable manufacturing capability for ICRs based on the correlation relationship. Firstly, an extensible multi-dimensional indicator system of sustainable manufacturing capability is constructed. Then, multiple composition correlation relationships among ICRs are analyzed to establish the correlation assessment model. Furthermore, a set of dynamic evaluation methods is proposed, in which raw data of the evaluation indicators is processed based on the service correlation model, and the traditional network analytic network process method is improved based on the data correlation model. Finally, a case study is implemented to show the reasonability and effectiveness of the proposed method in the assessment of sustainable manufacturing capability for ICRs.

[1]  Yueyu Li,et al.  Study on Supplier Selection of Manufacturing in Lean Closed-Loop Supply Chain , 2015 .

[2]  Lihui Wang,et al.  Energy-efficient robot applications towards sustainable manufacturing , 2017, Int. J. Comput. Integr. Manuf..

[3]  Wenjun Xu,et al.  An improved discrete bees algorithm for correlation-aware service aggregation optimization in cloud manufacturing , 2016 .

[4]  Michael Luck,et al.  Efficient Correlation-Aware Service Selection , 2012, 2012 IEEE 19th International Conference on Web Services.

[5]  Michael Mitzenmacher,et al.  Detecting Novel Associations in Large Data Sets , 2011, Science.

[6]  Fei Tao,et al.  Correlation-aware resource service composition and optimal-selection in manufacturing grid , 2010, Eur. J. Oper. Res..

[7]  Duc Truong Pham,et al.  Knowledge Sharing and Evolution of Industrial Cloud Robotics , 2018 .

[8]  Qingsong Hua,et al.  Cloud robotics in Smart Manufacturing Environments: Challenges and countermeasures , 2017, Comput. Electr. Eng..

[9]  G. Sakthivel,et al.  Application of failure mode and effect analysis in manufacturing industry - an integrated approach with FAHP-fuzzy TOPSIS and FAHP-fuzzy VIKOR , 2018 .

[10]  Hu-Chen Liu,et al.  Robot Evaluation and Selection with Entropy-Based Combination Weighting and Cloud TODIM Approach , 2018, Entropy.

[11]  Ma Wen-lon.  Optimizing services composition in cloud manufacturing based on improved ant colony algorithm , 2016 .

[12]  Jörg Franke,et al.  Reducing the energy consumption of industrial robots in manufacturing systems , 2015 .

[13]  Fei Tao,et al.  Correlation-aware web services composition and QoS computation model in virtual enterprise , 2010 .

[14]  Qingsong Ai,et al.  Energy Condition Perception and Big Data Analysis for Industrial Cloud Robotics , 2017 .

[15]  Liu Jian,et al.  An approach for service composition optimisation considering service correlation via a parallel max–min ant system based on the case library , 2018, Int. J. Comput. Integr. Manuf..

[16]  Qiang He,et al.  Efficient Query of Quality Correlation for Service Composition , 2018, IEEE Transactions on Services Computing.

[17]  Shuliang Wang,et al.  Service Selection With QoS Correlations in Distributed Service-Based Systems , 2019, IEEE Access.

[18]  Takashi Irohara,et al.  Scheduling for sustainable manufacturing: A review , 2018, Journal of Cleaner Production.

[19]  Xuemei Jiang,et al.  Dynamic and unified modelling of sustainable manufacturing capability for industrial robots in cloud manufacturing , 2017 .

[20]  Xun Xu,et al.  From cloud computing to cloud manufacturing , 2012 .

[21]  Qin Wei,et al.  Dynamic Assessment of Sustainable Manufacturing Capability for CNC Machining Systems in Cloud Manufacturing , 2015, APMS.

[22]  Alexandr Klimchik,et al.  Efficiency evaluation of robots in machining applications using industrial performance measure , 2017 .

[23]  Damien Trentesaux,et al.  Sustainability in manufacturing operations scheduling: A state of the art review , 2015 .

[24]  Laura Porcu,et al.  Cloud manufacturing as a sustainable process manufacturing route , 2018 .

[25]  R. R. Lekurwale,et al.  Framework to evaluate manufacturing capability using analytical hierarchy process , 2015 .

[26]  Lei Chen,et al.  Selecting Training method of a rehabilitation robot Based on fuzzy comprehensive evaluation , 2016 .

[27]  Duc Truong Pham,et al.  Dynamic Manufacturing Capability Assessment of Industrial Robots Based on Feedback Information in Cloud Manufacturing , 2017 .

[28]  Duc Truong Pham,et al.  Industrial Cloud Robotics Towards Sustainable Manufacturing , 2016 .

[29]  Thomas L. Saaty,et al.  Fundamentals of the analytic network process — multiple networks with benefits, costs, opportunities and risks , 2004 .

[30]  Zeid Kootbally,et al.  Industrial robot capability models for agile manufacturing , 2016, Ind. Robot.

[31]  Zhengzhu Zhang,et al.  Modeling and dynamic assessment on sustainable development of drainage enterprise: Application of a coupled system dynamics- comprehensive assessment model , 2017 .

[33]  Tabasam Rashid,et al.  Robot selection by using generalized interval-valued fuzzy numbers with TOPSIS , 2014, Appl. Soft Comput..

[34]  Lei Ren,et al.  A modeling and description method of multidimensional information for manufacturing capability in cloud manufacturing system , 2013 .

[35]  Duc Truong Pham,et al.  The Bees Algorithm: Modelling foraging behaviour to solve continuous optimization problems , 2009 .

[36]  Xiaomei Zhang,et al.  Energy-efficient concurrent assessment of industrial robot operation based on association rules in manufacturing , 2018, 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC).

[37]  Qingsong Ai,et al.  Manufacturing Capability Assessment for Human-Robot Collaborative Disassembly Based on Multi-Data Fusion☆ , 2017 .

[38]  Yang Hu,et al.  Collaborative Optimization of Service Scheduling for Industrial Cloud Robotics Based on Knowledge Sharing , 2019 .

[39]  M. Taisch,et al.  Sustainable manufacturing: trends and research challenges , 2012 .

[40]  Dexin Huang.  A Supplier Selection Method Based on AHP , 2019 .

[41]  Yoshio Yokose.  Dynamically evolving algorithm for minimizing the energy consumption of a manipulator , 2018, Artificial Life and Robotics.

[42]  Xifan Yao,et al.  Correlation-aware QoS modeling and manufacturing cloud service composition , 2017, J. Intell. Manuf..

[43]  Chao Yang,et al.  A network quotation framework for customised parts through rough requests , 2018, Int. J. Comput. Integr. Manuf..