Journal
IEEE ACCESS
Volume 9, Issue -, Pages 24658-24668Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3055587
Keywords
Manufacturing; Games; Game theory; Learning automata; Cloud computing; Analytical models; Computer architecture; Cloud manufacturing; public good game; learning automaton; agent-based modeling and simulating
Categories
Funding
- National Natural Science Foundation of China [52075404]
- Application Foundation Frontier Special Project of Wuhan Science and Technology Bureau [2020010601012176]
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The emergence of service-oriented Cloud Manufacturing (CMfg) relies on technologies like cloud computing and Industrial Internet of Things to distribute and authorize various manufacturing resources. In CMfg, cooperation between multiple Manufacturing Services (MSs) is essential to complete Manufacturing Tasks (MTs) as individual and collective interests may conflict. By exploring individual decision-making behaviors and employing evolutionary game theory and agent-based modeling, efficient methods for maximizing interests through MSs' cooperation can be developed.
With the development of information and network technology, a new type of manufacturing paradigm, known as a service-oriented Cloud Manufacturing (CMfg), is emerged. In the CMfg paradigm, geographically distributed various manufacturing resources authorized by different companies are encapsulated as different manufacturing services (MSs) supported by the enabling technologies, such as cloud computing, Industrial Internet of Things, virtualization technologies, etc. MSs are provided according to the demands of manufacturing tasks (MTs) presented by various customers via the IoT (Internet of Things) in CMfg paradigm. Generally, one MS may complete one MT, but mostly one MT needs more MSs to complete it cooperatively. Furthermore, these MSs are perhaps controlled and managed by different enterprises. As an autonomous entity, each MS generally makes decision in light of its own interests, and their own interests generally have conflicts with the others' and the collective ones. To solve the conflicts through MSs' cooperation is an efficient and low-cost method to maximize both individual and collective interests. Hence, the influences of individual decision-making behaviors on cooperation are explored and discussed in CMfg paradigm. The evolutionary game theory and agent-based modeling and simulating method are employed to model and simulate individual behaviors of MSs during the process of forming the coalition for completing MTs. In addition, the learning mechanism of MSs is discussed in the decision-making process. The simulation results show that decision behaviors of MSs are positively influenced by the information transparency of the CMfg environment and the learning mechanism of MSs effects on the cooperative emergence.
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