Investigating the effect of scale and scheduling strategies on the productivity of 3D managed print services

Gopsill, James; Hicks, Ben

doi:10.1177/0954405417708217

Cited by 16 publications

(9 citation statements)

References 18 publications

(29 reference statements)

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“…Thus, the first column identifies the author and year of publication of the article in the chronological order. The second column outlines the solution strategies for solving the AM scheduling problem implemented by the authors, with most studies developing their own mathematical models because only two studies adopted models from other authors (Gopsill & Hicks, 2018;Araujo et al, 2019). Among the fast algorithms (column 2), heuristics were developed in approximately 45% studies; however, two studies failed to present algorithm proposals for solving the problem and they developed only mathematical models (Luzon & Khmelnitsky, 2019;Kucukkoc, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…In the study by Gopsill & Hicks (2018), the integrated adoption of nesting and scheduling problems increases the complexity of the model. They authors assessed the influence of the scale effect and of four different production scheduling strategies using first-fit decreasing height (FFDH) as a nesting algorithm to improve the productivity of 3DP machines with ME technology; further, they presented the results of its combination with a genetic algorithm (GA).…”

Section: Additive Manufacturing Schedulingmentioning

confidence: 99%

See 1 more Smart Citation

Scheduling for Additive Manufacturing: a literature review

2022

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Advancements in production technologies and materials have facilitated the use of additive manufacturing (AM) (i.e., 3D printing) in the large-scale production of finished products with high level of customization, simplification of the factory floor, and fast delivery. Production sequencing is a well-established topic in this research area; however, its application to an AM environment suffers from specific issues that are yet to be explored. This paper presents a systematic literature review for mapping the state-of-the-art production sequencing methods in AM and for discussing the content of 26 articles published in magazines between 2017–2020. The main mathematical models, algorithms adopted for their solution, and main characteristics of computational experiments performed in these articles are identified; the results indicate that some characteristics of the problem can still be included in these models, such as the possibility of outsourcing and technology restrictions, which are yet to be explored in the literature. Further, authors observed the need for more robust computational experiments to better evaluate the proposed solutions.

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Section: Discussionmentioning

confidence: 99%

Section: Additive Manufacturing Schedulingmentioning

confidence: 99%

Scheduling for Additive Manufacturing: a literature review

2022

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“…Job management has typically followed First-Come First-Serve (FCFS) principles (Gopsill and Hicks, 2018). Jobs are either queued up based on submission or individuals coming into the space to identify and check the availability of the machine(s) they wish to use.…”

Section: Introductionmentioning

confidence: 99%

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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“…The prospect of the 3D printing industry is promising and the 3D printing service market is growing fast. 3 In the meantime, with the rapid development and application of cloud manufacturing technology, [4][5][6][7] numerous 3DPCSs, 8,9 as well as 3DPCS platforms 10,11 are becoming available gradually. This allows both professional users and customers with little domain knowledge to enjoy the pervasiveness and convenience of 3DPCSs.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic-based extendable quantitative evaluation method for 3D printing service in cloud manufacturing

Cui

Zhang

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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By enabling consumer products to be produced on demand and eliminating waste caused by excessive production and transportation, 3D Printing Cloud Services (3DPCSs) are increasingly welcomed by non-professional customers. With more and more 3D printers becoming available on various 3DPCS platforms, the evaluation and selection problem of 3DPCS has attracted much attention for both novices and experienced users. In this paper, we propose a probabilistic-based extendable quantitative evaluation method for 3DPCS evaluation. This method combines the advantages of the information transformation technique, the multinomial distribution probabilistic model, and the uncertainty based weighting method. Evaluation factors, the major attributes that significantly affect the performance of a 3DPCS, are modeled using probabilistic models. At the same time, historical service data is introduced to dynamically identify and update the evaluation factors. Based on these parameters, the proposed quantitative evaluation method can support the evaluation and comparison of 3DPCSs. Numerical simulation experiments are designed and implemented. The corresponding results verify the effectiveness of the proposed evaluation model. The presented evaluation method can serve as the basis of service evaluation and selection on a 3DPCS platform. Although the focus of this work is on 3DPCS, the idea can apply to other types of cloud manufacturing services.

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Investigating the effect of scale and scheduling strategies on the productivity of 3D managed print services

Cited by 16 publications

References 18 publications

Scheduling for Additive Manufacturing: a literature review

Scheduling for Additive Manufacturing: a literature review

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

A probabilistic-based extendable quantitative evaluation method for 3D printing service in cloud manufacturing

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