A three-tier programming model for service composition and optimal selection in cloud manufacturing

被引:37
|
作者
Lim, Ming K. [1 ,2 ]
Xiong, Weiqing [2 ,3 ]
Wang, Yankai [2 ,4 ]
机构
[1] Univ Glasgow, Adam Smith Business Sch, Glasgow, Lanark, Scotland
[2] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing, Peoples R China
[3] Natl Univ Singapore, Dept Mech Engn, Singapore, Singapore
[4] Natl Univ Singapore, Sch Comp, Singapore, Singapore
关键词
Cloud manufacturing; Service composition and optimal selection; Three-tier programming model; a-i-NSGA-II; GENETIC ALGORITHM; ALLOCATION;
D O I
10.1016/j.cie.2022.108006
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The process of service composition and optimal selection in cloud manufacturing (CMfg-SCOS) involves three types of users: service demanders, resource providers, and cloud platform operators. The interests of all users are a research focus of CMfg-SCOS, as their participation in the CMfg system directly affects the efficiency and longterm development of CMfg. However, the current research on CMfg-SCOS rarely considers the interests of all three types of users simultaneously, and the interest of resource providers is not clearly defined, which lags behind the reality of CMfg. Therefore, this study first proposes a three-tier programming model of CMfg-SCOS that considers the interests of service demanders, cloud platform operators, and resource providers. At the lower level of the model, service demanders are the decision makers, aiming to minimize time and cost and maximize service quality. At the middle level of the model, cloud platform operators are the decision makers, aiming to maximize resource use and flexibility in the face of uncertain environments. At the upper level, resource providers are the decision makers, aiming to maximize enterprise surplus. Then, this study develops an improved fast nondominated sorting genetic algorithm with advancement and inheritance (namely, a-i-NSGA-II) to solve the three-tier model efficiently. Numerical experiments conducted in this study found that in comparison to the art of state algorithms, including original nondominated sorting genetic algorithm II (NSGA-II), multiobjective particle swarm optimization (MOPSO), and multiobjective spotted hyena optimizer (MOSHO), the proposed a-i-NSGA-II has better diversity and comprehensive performance at the middle level of the model and better solution quality at the upper level. Furthermore, a case study of the actual production of an automobile fuel tank assembly enterprise verifies the effectiveness of the proposed model and algorithm.
引用
收藏
页数:20
相关论文
共 50 条
  • [41] Multi-Cloud Provisioning and Load Distribution for Three-Tier Applications
    Grozev, Nikolay
    Buyya, Rajkumar
    ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS, 2014, 9 (03)
  • [42] Performance Modelling and Simulation of Three-Tier Applications in Cloud and Multi-Cloud Environments
    Grozev, Nikolay
    Buyya, Rajkumar
    COMPUTER JOURNAL, 2015, 58 (01): : 1 - 22
  • [43] Controlling Testing Using Three-Tier Model Architecture
    Kervinen, Antti
    Maunumaa, Mika
    Katara, Mika
    ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE, 2006, 164 (04) : 53 - 66
  • [44] A Three-Tier Model of Parent Education in Early Childhood
    McIntyre, Laura Lee
    Phaneuf, Leah K.
    TOPICS IN EARLY CHILDHOOD SPECIAL EDUCATION, 2007, 27 (04) : 214 - 222
  • [45] The Application of a Three-Tier Model of Intervention to Parent Training
    Phaneuf, Leah
    McIntyre, Laura Lee
    JOURNAL OF POSITIVE BEHAVIOR INTERVENTIONS, 2011, 13 (04) : 198 - 207
  • [46] A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system
    Huang, Biqing
    Li, Chenghai
    Tao, Fei
    ENTERPRISE INFORMATION SYSTEMS, 2014, 8 (04) : 445 - 463
  • [47] A preference-based multi-objective algorithm for optimal service composition selection in cloud manufacturing
    Bi, Xiaoxue
    Yu, Dong
    Liu, Jinsong
    Hu, Yi
    INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2020, 33 (08) : 751 - 768
  • [48] A three-tier model for structuring of scientific and technical information
    Artamonov, Alexey Anatolievich
    Ananieva, Anastasia Gennadievna
    Tretyakov, Evheniy Sergeyevich
    Kshnyakov, Dmitry Olegovich
    Onykiy, Boris Nikolaevich
    Pronicheva, Larisa Vladimirovna
    Journal of Digital Information Management, 2016, 14 (03): : 184 - 193
  • [49] Development of a three-tier assessment model: a case study
    Lau, Henry C.
    Ip, Andrew
    Lee, C. K. M.
    Ho, G. T. S.
    BENCHMARKING-AN INTERNATIONAL JOURNAL, 2018, 25 (07) : 2216 - 2229
  • [50] Three-tier neural model for service provisioning over collaborative flying ad hoc networks
    Sharma, Vishal
    Kumar, Rajesh
    NEURAL COMPUTING & APPLICATIONS, 2018, 29 (10): : 837 - 856