Service composition model and method in cloud manufacturing

Service Composition and Optimal Selection in Cloud Manufacturing: State-of-the-Art and Research Challenges

“…Case study [52] Magnetic bearing assembly [20] Manufacturing of magnetic bearing [22] Molding industry [61] Customized automobile parts [62] Alloy materials processing to customize car doors and painting [25] High-performance mechanical seals provider in challenging work conditions [57] Large-scale verification of proposed algorithm with artificial bee colony and cuckoo search strategies to identify the correlation impact [58] Performance of proposed case study based on searching ability and composite service dynamic trust QoS [60] Composite CMfg service optimal selection (CCSOS) on large-scale (Customized motorcycle production) [67] Service-oriented collaborative manufacturing system support 10 SMEs for the motorcycle production industry [64] Numerical case study based on original data from CMfg service scheduling simulation experiment [23] Simulated data set to evaluate algorithms for service composition and optimal selection problems of various scales [68] Variable candidate, arrival times and weight combinations [4] Cloud environment for the customized car design and production [71] Numerical example considering available services, and service performance similarity [70] Parameters setup with random seed for repeatable results [75] MC type wheeled cleaning robot manufacturing [14] Consists of the raw material life cycle based on representative components from two hundred thousand bill of materials (BOM) data [72] Molding industry cloud manufacturing prototype [78] Numerical case study for large scale problem on CMfg logistic service sharing requirements [81] Demand-driven system simulation to satisfy users' needs considering the real-world service provider capacity restrictions [80] Mold manufacturing service composition containing the entire life cycle from mold design to testing and packing. in service composition and optimal selection in the field of CMfg has received increasing attention since 2013, so it is essential to identify the studies with promising solutions to relay a foundation for future research.…”

An Optimal Composition Strategy for Knowledge Service Component Based on Flexible Tracking Particle Swarm Algorithm

“…Besides the swarm intelligence optimization algorithm, the deep reinforcement learning [15], and mining strategy of association rules [16] are also exploited to optimize the service composition. In the latest work, some studies optimized service composition on the basis of task requirements to solve the problem of multitask corresponding multiservice selection [17]. On the other hand, it is noted that the vast majority of knowledge service methods assume that knowledge resource and business process are separated or business process oriented knowledge resource retrieval mainly depend on keywords and semantics matching so that repeated retrieval, analysis, and evaluation must be used to acquire accurate knowledge resources.…”

“…(2) In the process of component composition, the weight of each subobjective including composition performance P C (X), composition efficiency E C (X), E R (X), E D (X), and composition cost C C (X), C R (X), C D (X) may be different, and then weight R λ corresponding to these objectives form a weight set as follows: No. Name C 11 Maintenance of common sense C 12 Assembly scheduled maintenance C 13 Assembly regular inspection C 14 Maintenance records C 15 Fault form C 16 Maintenance case C 17 Product structure tree C 18 BOM table C 19 Inventory information C 110 Supplier information C 111 ree-dimensional model of product C 112 Exploded view C 113 Assembly animation C 114 Motion simulation C 115 e parts catalogue C 116 Process knowledge of products C 21 General maintenance inquiries C 22 Maintaining common sense queries C 23 Fault shape query C 24 Maintenance record query C 25 Maintenance case analysis C 26 Troubleshooting program query C 27 Query by structure tree C 28 Number by name C 29 Single-level BOM check C 211 Single-level countercheck…”

Research on Cloud Enterprise Resource Integration and Scheduling Technology Based on Mixed Set Programming

“…Aiming at the problem of collaborative scheduling of resources between enterprises under the cloud manufacturing model, based on metadata and ontology modeling methods, a mechanism for collaborative scheduling of resources between enterprises is proposed. On the basis of proposing resource capacity model and order demand task model [12,13], it comprehensively considers multiple constraints based on the hybrid set planning method. It can provide supply and demand companies in the supply chain with a collaborative production plan with small order delays and low costs.…”