Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Trondheim, Norway, September 17-21, 2023, Proceedings, Part II
Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Trondheim, Norway, September 17-21, 2023, Proceedings, Part II
复制标题
生产管理系统的进步。
DOI：
10.1007/978-3-031-43666-6_36
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Peckham O
中科院分区：
文献类型：
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作者：
Peckham O
分布式分散增材制造（DDAM）网络被认为是大规模/批量生产模式的补充方法，提供了快速增长、稳健和响应迅速的全球-本地制造能力。一种实现是使用代表作业和机器的智能代理（AI），并代表它们进行代理。代理过程的基础是共享关于作业和机器的信息，使得机器能够选择它们有能力和资源来完成的适当作业。这里可能存在挑战，因为不同的作业和机器可能无法（即，由于部分信息）或不愿意（即，由于IP问题）共享，然后潜在地限制决策能力和系统性能。本文研究了信息共享的性质对代理DDAM网络性能的影响。为此，使用AnyLogic创建多代理模拟。该模拟对DDAM网络进行了建模，其中作业与机器共享的特征信息可以是不同的。仿真结果表明，一般来说，更多的信息共享提高了系统性能，但不同类型的信息共享的工作和机器创建不同的影响，实现的性能优势，并应优先考虑，以最大限度地提高系统吞吐量。
Distributed Decentralised Additive Manufacturing (DDAM) networks are considered a complementary method to mass/batch production paradigms providing ramp-up, robust and responsive global-local manufacturing capability. One implementation is to use Artificially Intelligent (AI) agents that represent jobs and machines, and broker on their behalf. Fundamental to the brokering process is the sharing of information about the jobs and machines such that machines are able to select appropriate jobs for which they have the capabilities and resources to complete. Here a challenge may exist, in that different jobs and machines may be unable (i.e. due to partial information) or unwilling (i.e. due to IP concerns) to share, then potentially limiting decision-making capability and system performance. This paper examines the nature of information sharing on the performance of a brokered DDAM network. To do so, AnyLogic was used to create a multi-agent simulation. The simulation modelled a DDAM network where the characteristic information shared to the machines by the jobs could be varied. The results of the simulation showed that in general more information sharing boosted system performance, but that different types of information shared by jobs and machines created a varying impact on the performance benefit that is realised and should be prioritised to maximise system throughput.