Assessing the performances of a novel decentralised scheduling approach in Industry 4.0 and cloud manufacturing contexts

Grassi, Andrea; Guizzi, Guido; Santillo, Liberatina Carmela; Vespoli, Silvestro

doi:10.1080/00207543.2020.1799105

Cited by 30 publications

(14 citation statements)

References 28 publications

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“…The developed simulation tool statistically proved to produce the same T H and CT mean values of the one calculated with the PWC law proposed by Spearman et al (1990) in a two-year simulation time run, with a confidence level of 95%. More detail about the validation phase can be found in Grassi et al (2020b), due to the similarities of the methodology and simulation tools involved.…”

Section: Experimental Methodologymentioning

confidence: 99%

“…2). In order to facilitate understanding of the proposed control scheme, we first briefly detail the effects of the four different control knobs at the HLC level, as already analysed in Grassi et al (2020b): 1. JRQ size This is the number of orders in the JRQ that have been admitted and are waiting for actual release into production.…”

Section: Problem Statementmentioning

confidence: 99%

“…The size of the JRQ influences the effectiveness of the Dispatcher. A small JRQ size would result in a dispatcher having limited choices, while conversely, a large JRQ would leave the Dispatcher a wider range of choices but would also increase the total time required for a job to cross the entire system (Grassi et al 2020b). .…”

Section: Problem Statementmentioning

confidence: 99%

“…In the following, we will consider two different dispatching rules for the Dispatcher, as proposed in Vespoli et al (2019) and deeply analysed in Grassi et al (2020b), so only briefly discussed here:…”

Section: Problem Statementmentioning

confidence: 99%

“…Jobs passing this time are forced to be released by the Dispatcher. This can be a fixed value (statically chosen) or dynamically adjusted by an appropriate computation mechanism, updated on the basis of the contingent situation of the production system (Grassi et al 2020b).…”

Section: Problem Statementmentioning

confidence: 99%

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A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

et al. 2021

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Modern market scenarios are imposing a radical change in the production concept, driving companies’ attention to customer satisfaction through increased product customization and quick response strategies to maintain competitiveness. At the same time, the growing development of Industry 4.0 technologies made possible the creation of new manufacturing paradigms in which an increased level of autonomy is one of the key concepts to consider. Taking the advantage from the recent development around the semi-heterarchical architecture, this work proposes a first model for the throughput control of a production system managed by such an architecture. A cascade control algorithm is proposed considering work-in-progress (WIP) as the primary control lever for achieving a specific throughput target. It is composed of an optimal control law based on an analytical model of the considered production system, and of a secondary proportional-integral-derivative controller capable of performing an additional control action that addresses the error raised by the theoretical model’s. The proposed throughput control algorithm has been tested in different simulated scenarios, and the results showed that the combination of the control actions made it possible to have continuous adjustment of the WIP of the controlled production system, maintaining it at the minimum value required to achieve the requested throughput with nearly zero errors.

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Section: Experimental Methodologymentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

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A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

et al. 2021

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Dynamic Scheduling in a Flow Shop Using Deep Reinforcement Learning

Marchesano

Guizzi

Santillo

et al. 2021

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

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Machine Learning (ML) techniques and algorithms, which are emerging technologies in Industry 4.0, present new possibilities for complex scheduling methods. Since different rules can be applied to different circumstances, it can be difficult for the decision-maker to choose the right rule at any given time. The purpose of the paper is to build an "intelligent" tool that adapts its choices in response to changes in the state of the production line. A Deep Q-Network (DQN), a typical Deep Reinforcement Learning (DRL) method, is proposed for creating a self-optimizing scheduling policy. The system has a set of known dispatching rules for each machine's queue, from which the best one is dynamically chosen, according to the system state. The novelty of the paper is how the reward function, state, and action space are modelled. A series of experiments were conducted to determine the best DQN network size and the most influential hyperparameters for training.

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One-of-a-Kind Productions in Industry 4.0 Environments

Carlavan

Rossit

2023

Lecture Notes in Production Engineering

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Assessing the performances of a novel decentralised scheduling approach in Industry 4.0 and cloud manufacturing contexts

Cited by 30 publications

References 28 publications

A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

Dynamic Scheduling in a Flow Shop Using Deep Reinforcement Learning

One-of-a-Kind Productions in Industry 4.0 Environments

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