Queueless: Agent-Based Manufacturing for Workshop Production

Gopsill, James; Obi, Martins; Giunta, Lorenzo; Goudswaard, Mark

doi:10.1007/978-981-19-3359-2_3

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…No precedence exists between Machine and Job agents, making the system "queueless" with jobs representing a pool of work (Gopsill et al, 2022). The determination of which job will be manufactured by which machine is based on the minimal intelligences and communication strategy employed.…”

Section: A Minimally Intelligent Makerspace Manufacturing Systemmentioning

confidence: 99%

“…A concept that fits the job management requirements of Makerspaces is agent-based manufacturing and, in particular, Minimally Intelligent agents (Gopsill et al, 2022). As the name suggests, Minimally Intelligent agents feature the minimal intelligence required to assess their own parameters, negotiate with other agents, and make decisions to satisfy their goals (Cliff, 1997;Lomas and Cliff, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Optimal configurations of Minimally Intelligent additive manufacturing machines for Makerspace production environments

Gopsill,

Goudswaard,

Snider

et al. 2024

AIEDAM

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Additive manufacturing (AM) has transformed job shop production and catalysed the growth of Makerspaces, FabLabs, Hackspaces, and Repair Cafés. AM has enabled the handling and manufacturing of a wide variety of components, and its accessibility has enabled more individuals to make. While smaller than their production-scale counterparts, the objectives of minimizing technician overhead, capital expenditure, and job response time remain the same. The typical First-Come First-Serve (FCFS) operating model, while functional, is not necessarily the most efficient and makes responding to a-typical or urgent demand profiles difficult. This article reports a study that investigated how AM machines configured with Minimally Intelligent agents can support production in these environments. An agent-based model that simulated 5, 10, 15, and 20 AM machines operating a 9 am−5 pm pattern and experiencing a diverse non-repeating demand profile was developed. Machines were configured with minimal intelligence – FCFS, First-Response First-Serve (FRFS), Longest Print Time (LPT), Shortest Print Time (SPT), and Random Selection logics – that governed the selection of jobs from the job pool. A full factorial simulation totaling 15,629 configurations was run until convergence to a ranked list of production performance – min Job Time-in-System. Performance changed as much as 200%. Performant configurations featured a variety of logics, while the least performant were dominated by FCFS and LPT. All FCFS (a proxy for today’s operations) was one of the least performant configurations. The results provide an optimal set of logics and performance bands that can be used to justify capital expenditure and AM operations in Makerspaces.

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Section: A Minimally Intelligent Makerspace Manufacturing Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%