Design of Marketplaces for Smart Manufacturing Services

被引:3
|
作者
Moghaddam, Mohsen [1 ]
Jones, Albert [2 ]
Wuest, Thorsten [3 ]
机构
[1] Northeastern Univ, Dept Mech & Ind Engn, 360 Huntington Ave, Boston, MA 02115 USA
[2] Natl Inst Stand & Technol, 100 Bureau Dr, Gaithersburg, MD 20899 USA
[3] West Virginia Univ, Dept Ind & Management Syst Engn, Morgantown, WV 26506 USA
关键词
Service-oriented manufacturing; Cyber-physical systems; Matching markets; Multi-agent reinforcement learning; Industry; 4.0; COMPLEX ADAPTIVE SYSTEMS; CYBER-PHYSICAL SYSTEMS; NETWORKS;
D O I
10.1016/j.promfg.2020.01.312
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The future of manufacturing depends on the successful implementation of two terms: cyber-physical systems (CPS) and microservices. CPS technologies are transforming the way manufacturing components interact, just as the Internet has transformed the interaction with information. A micro-service is an architectural style that structures an application as a collection of loosely-coupled, independent, and self-executable programs that can be composed to provide various capabilities of large, monolithic manufacturing applications such as ERP and MES. The transition to such a modular structure enables two important features. First, micro-services can be sourced on a pay-as-you-go fashion from a large, diverse, and growing number of independent providers, thus opening the manufacturing space to new players such as innovative and agile startups. Second, those micro-services can be viewed as building-blocks for creating on-demand, complex, custom processes that address the specific needs of users. Taken together, micro-services, the platforms on which they reside, and service vendors constitute a multi platform marketplace. This article first provides an overview of new manufacturing paradigms enabled by CPS and micro-services. It then formalizes the multiplat form marketplaces for micro-services as multi-layer networks of collaborating-competing agents with different interests, policies, capabilities, and technologies. It fmally formulates a research methodology for optimizing the policies and interactions through the theories of matching markets and multi-agent reinforcement learning. (C) 2019 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:194 / 201
页数:8
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