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Zheng X, Zhang X. Robustness of Cloud Manufacturing System Based on Complex Network and Multi-Agent Simulation. ENTROPY (BASEL, SWITZERLAND) 2022; 25:45. [PMID: 36673186 PMCID: PMC9857848 DOI: 10.3390/e25010045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/20/2022] [Accepted: 12/24/2022] [Indexed: 06/17/2023]
Abstract
Cloud manufacturing systems (CMSs) are networked, distributed and loosely coupled, so they face great uncertainty and risk. This paper combines the complex network model with multi-agent simulation in a novel approach to the robustness analysis of CMSs. Different evaluation metrics are chosen for the two models, and three different robustness attack strategies are proposed. To verify the effectiveness of the proposed method, a case study is then conducted on a cloud manufacturing project of a new energy vehicle. The results show that both the structural and process-based robustness of the system are lowest under the betweenness-based failure mode, indicating that resource nodes with large betweenness are most important to the robustness of the project. Therefore, the cloud manufacturing platform should focus on monitoring and managing these resources so that they can provide stable services. Under the individual server failure mode, system robustness varies greatly depending on the failure behavior of the service provider: Among the five service providers (S1-S5) given in the experimental group, the failure of Server 1 leads to a sharp decline in robustness, while the failure of Server 2 has little impact. This indicates that the CMS can protect its robustness by identifying key servers and strengthening its supervision of them to prevent them from exiting the platform.
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Bibliometric Method for Manufacturing Servitization: A Review and Future Research Directions. SUSTAINABILITY 2022. [DOI: 10.3390/su14148743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
To gain sustainable development, it is a trend that manufacturing companies are change the value chain from manufacturing-centric to service-centric. Therefore, the capability of the manufacturing service is as significant as the production ability of enterprises, which reflects the supply chain management (SCM), flexible production, production efficiency, and other indicators of the enterprises. It is the first paper to discuss the sustainability of service-oriented manufacturing using bibliometric analysis. It derives a detailed review and future outlook on the development of manufacturing servitization, indicating the research directions for future development, and provides a valuable reference for researchers in related directions. The bibliometric analysis discusses countries or regions, research areas, authors, keywords, institutions, and journals based on the literature data from the Web of Science (WoS). The results show that research on manufacturing services has gradually received attention since its inception and has become popular since 2008. The papers published from 2008 to 2021 account for 77.62%. The USA is the most studied country on this topic, followed by China and the UK. The International Journal of Production Research regarding the most quantity of articles, and Beihang University is the most influential institution in this field. The largest amount of articles published in the area of “business and economics”, amounting to 1565 articles. In recent years, the main research areas included “Industry 4.0”, “cloud manufacturing (CMfg)”, “Internet of Things (IoT)”, “big data” and “services innovation”. Finally, “digital and intelligent manufacturing” and “product-service systems” are potential research directions for the future.
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Namoun A, Abi Sen AA, Tufail A, Alshanqiti A, Nawaz W, BenRhouma O. A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities. SENSORS (BASEL, SWITZERLAND) 2022; 22:5142. [PMID: 35890820 PMCID: PMC9324550 DOI: 10.3390/s22145142] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 06/24/2022] [Accepted: 07/01/2022] [Indexed: 06/15/2023]
Abstract
The use of software and IoT services is increasing significantly among people with special needs, who constitute 15% of the world's population. However, selecting appropriate services to create a composite assistive service based on the evolving needs and context of disabled user groups remains a challenging research endeavor. Our research applies a scenario-based design technique to contribute (1) an inclusive disability ontology for assistive service selection, (2) semi-synthetic generated disability service datasets, and (3) a machine learning (ML) framework to choose services adaptively to suit the dynamic requirements of people with special needs. The ML-based selection framework is applied in two complementary phases. In the first phase, all available atomic tasks are assessed to determine their appropriateness to the user goal and profiles, whereas in the subsequent phase, the list of service providers is narrowed by matching their quality-of-service factors against the context and characteristics of the disabled person. Our methodology is centered around a myriad of user characteristics, including their disability profile, preferences, environment, and available IT resources. To this end, we extended the widely used QWS V2.0 and WS-DREAM web services datasets with a fusion of selected accessibility features. To ascertain the validity of our approach, we compared its performance against common multi-criteria decision making (MCDM) models, namely AHP, SAW, PROMETHEE, and TOPSIS. The findings demonstrate superior service selection accuracy in contrast to the other methods while ensuring accessibility requirements are satisfied.
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Affiliation(s)
- Abdallah Namoun
- Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; (A.A.A.S.); (A.A.); (W.N.); (O.B.)
| | - Adnan Ahmed Abi Sen
- Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; (A.A.A.S.); (A.A.); (W.N.); (O.B.)
| | - Ali Tufail
- School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei;
| | - Abdullah Alshanqiti
- Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; (A.A.A.S.); (A.A.); (W.N.); (O.B.)
| | - Waqas Nawaz
- Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; (A.A.A.S.); (A.A.); (W.N.); (O.B.)
| | - Oussama BenRhouma
- Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; (A.A.A.S.); (A.A.); (W.N.); (O.B.)
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Abosaif AN, Hamza HS. Quality of service-aware service selection algorithms for the internet of things environment: A review paper. ARRAY 2020. [DOI: 10.1016/j.array.2020.100041] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Sharma M, Sehrawat R. Quantifying SWOT analysis for cloud adoption using FAHP-DEMATEL approach: evidence from the manufacturing sector. JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT 2020. [DOI: 10.1108/jeim-09-2019-0276] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThis study aims to identify the critical factors (barriers and drivers) influencing the adoption of cloud computing (ACC) in the manufacturing sector in India.Design/methodology/approachIn this study, a mixed methodology approach is used. Interviews are conducted to investigate factors (drivers and barriers) influencing the ACC, which are further categorized as controllable determinants (weaknesses and strengths) and uncontrollable determinants (threats and opportunities) using a SWOT analysis. Fuzzy analytic hierarchy process (FAHP) has been utilized to highlight the most critical drivers as well as barriers. Finally, decision-making trial and evaluation laboratory (DEMATEL) has been used to find the cause-effect relationships among factors and their influence on the decision of adoption.FindingsThe manufacturing sector is in the digital and value change transformation phase with Industry 4.0, that is, the next industrial revolution. The 24 critical factors influencing ACC are subdivided into strengths, weaknesses, opportunities and threats. The FAHP analysis ranked time to market, competitive advantage, business agility, data confidentiality and lack of government policy standards as the most critical factors. The cause-effect relationships highlight that time to market is the most significant causal factor, and resistance to technology is the least significant effect factor. The results of the study elucidate that the strengths of ACC are appreciably more than its weaknesses.Research limitations/implicationsThis study couples the technology acceptance model (TAM) with technology-organization-environment (TOE) framework and adds an economic perspective to examine the significant influences of ACC in the Indian manufacturing sector. Further, it contributes to the knowledge of ACC in general and provides valuable insights into interrelationships among factors influencing the decision and strategies of adoption in particular.Originality/valueThis is the first scholarly work in the Indian manufacturing sector that uses the analysis from SWOT and FAHP approach as a base for identifying cause-effect relationships between the critical factors influencing ACC. Further, based on the extant literature and analysis of this work, an adoption framework has been proposed that justifies that ACC is not just a technological challenge but is also an environmental, economic and organizational challenge that includes organizational issues, costs and need for adequate government policies.
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Multi-Objective Cloud Manufacturing Service Selection and Scheduling with Different Objective Priorities. SUSTAINABILITY 2019. [DOI: 10.3390/su11174767] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In recent years, with the support of new information technology and national policies, cloud manufacturing (CMfg) has developed rapidly in China. About CMfg, scholars have conducted extensive and in-depth research, among which multi-objective service selection and scheduling (SSS) attracts increasing attention. Generally, the objectives of the SSS problem involve several aspects, such as time, cost, environment and quality. In order to select an optimal solution, the preference of a decision maker (DM) becomes key information. As one kind of typical preference information, objective priorities are less considered in current studies. So, in this paper, a multi-objective model is first constructed for the SSS with different objective priorities. Then, a two-phase method based on the order of priority satisfaction (TP-OPS) is designed to solve this problem. Finally, computational experiments are conducted for problems with different services and tasks/subtasks, as well as different preference information. The results show that the proposed TP-OPS method can achieve a balance between the maximum comprehensive satisfaction and satisfaction differences, which is conducive to the sustainable development of CMfg. In addition, the proposed method allows the preference information to be gradually clarified, which has the advantage of providing convenience to DM.
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Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing. SUSTAINABILITY 2019. [DOI: 10.3390/su11092619] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Service management in cloud manufacturing (CMfg), especially the service selection and scheduling (SSS) problem has aroused general attention due to its broad industrial application prospects. Due to the diversity of CMfg services, SSS usually need to take into account multiple objectives in terms of time, cost, quality, and environment. As one of the keys to solving multi-objective problems, the preference information of decision maker (DM) is less considered in current research. In this paper, linguistic preference is considered, and a novel two-phase model based on a desirable satisfying degree is proposed for solving the multi-objective SSS problem with linguistic preference. In the first phase, the maximum comprehensive satisfying degree is calculated. In the second phase, the satisfying solution is obtained by repeatedly solving the model and interaction with DM. Compared with the traditional model, the two-phase is more effective, which is verified in the calculation experiment. The proposed method could offer useful insights which help DM balance multiple objectives with linguistic preference and promote sustainable development of CMfg.
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Networked correlation-aware manufacturing service supply chain optimization using an extended artificial bee colony algorithm. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2018.12.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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9
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Akbaripour H, Houshmand M. Service composition and optimal selection in cloud manufacturing: landscape analysis and optimization by a hybrid imperialist competitive and local search algorithm. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3721-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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10
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Zhou J, Yao X, Lin Y, Chan FT, Li Y. An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.05.009] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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11
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Zhang S, Xu S, Zhang W, Yu D, Chen K. A hybrid approach combining an extended BBO algorithm with an intuitionistic fuzzy entropy weight method for QoS-aware manufacturing service supply chain optimization. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.07.011] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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12
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Zhang W, Yang Y, Zhang S, Yu D, Chen Y. A new three-dimensional manufacturing service composition method under various structures using improved Flower Pollination Algorithm. ENTERP INF SYST-UK 2017. [DOI: 10.1080/17517575.2017.1410895] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Wenyu Zhang
- School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Yushu Yang
- School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Shuai Zhang
- School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Dejian Yu
- School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Yong Chen
- School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
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Yin Y, Yu F, Xu Y, Yu L, Mu J. Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems. SENSORS 2017; 17:s17092059. [PMID: 28885602 PMCID: PMC5621120 DOI: 10.3390/s17092059] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Revised: 09/01/2017] [Accepted: 09/04/2017] [Indexed: 11/16/2022]
Abstract
Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable services in a huge number of available services in CPS. In traditional service recommendation, collaborative filtering (CF) has been studied in academia, and used in industry. However, there exist several defects that limit the application of CF-based methods in CPS. One is that under the case of high data sparsity, CF-based methods are likely to generate inaccurate prediction results. In this paper, we discover that mining the potential similarity relations among users or services in CPS is really helpful to improve the prediction accuracy. Besides, most of traditional CF-based methods are only capable of using the service invocation records, but ignore the context information, such as network location, which is a typical context in CPS. In this paper, we propose a novel service recommendation method for CPS, which utilizes network location as context information and contains three prediction models using random walking. We conduct sufficient experiments on two real-world datasets, and the results demonstrate the effectiveness of our proposed methods and verify that the network location is indeed useful in QoS prediction.
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Affiliation(s)
- Yuyu Yin
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310019, China.
- Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou 310019, China.
| | - Fangzheng Yu
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310019, China.
- Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou 310019, China.
| | - Yueshen Xu
- School of Software, Xidian University, Xi'an 710071, China.
| | - Lifeng Yu
- Hithink RoyalFlush Information Network Co., Ltd., Hangzhou 310023, China.
| | - Jinglong Mu
- Fushun Power Supply Branch, State Grid Liaoning Electric Power Supply Co., Ltd., Fushun 113008, China.
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Zhou L, Zhang L, Zhao C, Laili Y, Xu L. Diverse task scheduling for individualized requirements in cloud manufacturing. ENTERP INF SYST-UK 2017. [DOI: 10.1080/17517575.2017.1364428] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Longfei Zhou
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
- Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing, China
| | - Lin Zhang
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
- Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing, China
| | - Chun Zhao
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
- Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing, China
| | - Yuanjun Laili
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
- Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing, China
| | - Lida Xu
- Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA, USA
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Zhou J, Yao X. Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.03.017] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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Zhou J, Yao X. Multi-objective hybrid artificial bee colony algorithm enhanced with Lévy flight and self-adaption for cloud manufacturing service composition. APPL INTELL 2017. [DOI: 10.1007/s10489-017-0927-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Cheng Y, Tao F, Xu L, Zhao D. Advanced manufacturing systems: supply–demand matching of manufacturing resource based on complex networks and Internet of Things. ENTERP INF SYST-UK 2016. [DOI: 10.1080/17517575.2016.1183263] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Ying Cheng
- School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, P. R. China
- Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI, USA
| | - Fei Tao
- School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, P. R. China
| | - Lida Xu
- Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA, USA
| | - Dongming Zhao
- Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI, USA
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Zhang J, Wang C, Zhou M. Fast and Epsilon-Optimal Discretized Pursuit Learning Automata. IEEE TRANSACTIONS ON CYBERNETICS 2015; 45:2089-2099. [PMID: 25415995 DOI: 10.1109/tcyb.2014.2365463] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Learning automata (LA) are powerful tools for reinforcement learning. A discretized pursuit LA is the most popular one among them. During an iteration its operation consists of three basic phases: 1) selecting the next action; 2) finding the optimal estimated action; and 3) updating the state probability. However, when the number of actions is large, the learning becomes extremely slow because there are too many updates to be made at each iteration. The increased updates are mostly from phases 1 and 3. A new fast discretized pursuit LA with assured ε -optimality is proposed to perform both phases 1 and 3 with the computational complexity independent of the number of actions. Apart from its low computational complexity, it achieves faster convergence speed than the classical one when operating in stationary environments. This paper can promote the applications of LA toward the large-scale-action oriented area that requires efficient reinforcement learning tools with assured ε -optimality, fast convergence speed, and low computational complexity for each iteration.
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Jin C, Li F, Tsang EC, Bulysheva L, Kataev MY. A new compound arithmetic crossover-based genetic algorithm for constrained optimisation in enterprise systems. ENTERP INF SYST-UK 2015. [DOI: 10.1080/17517575.2015.1080302] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Chenxia Jin
- School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang 050018, China
| | - Fachao Li
- School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang 050018, China
| | - Eric C.C. Tsang
- Faculty of Information Technology, Macao university of science and technology, Macao, China
| | - Larissa Bulysheva
- Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA 23529, USA
| | - Mikhail Yu Kataev
- Department of Control systems, Tomsk State University of Control Systems and Radioelectronics, Tomsk, Russia
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