Environment to Simulate Distributed Agent Based Manufacturing Systems

Rocha, Andre Dionisio; Barroca, Pedro; Maso, Giovanni Dal; Barata, José

doi:10.1007/978-3-319-51100-9_36

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…An agent can influence other agents through direct or indirect interactions or general elements of the environment can trigger a new state. For instance, AB modelling was built in a manufacturing setting with multi-agents to illustrate how to minimize risks to meet the requirements for customized products (Rocha et al , 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of Six Sigma and simulations in real production factory to improve performance – a case study analysis

Ahmed

Olsen²,

Page³

2022

IJLSS

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Purpose The overarching objective of this research is to integrate the Lean Six Sigma (LSS) framework with computer simulation to improve the production efficiency of a light-emitting diode (LED) manufacturing factory. Design/methodology/approach Recently, the idea of taking advantage of the benefits of Six Sigma and simulation models together has led both industry and the academy towards further investigation and implementation of these methodologies. From this perspective, the present research will illustrate the effectiveness of using LSS methodology in a real factory environment by using the combination of three simulation methods which are system dynamics (SD), discrete-event simulation (DES) and agent-based (AB) modelling. Findings The hybrid simulation method applied in this research was found to accurately mimic and model the existing real factory environment. The define, measure, analyse, control and improve (DMAIC)-based improvements showed that the applied method is able to improve machine utilization rates while balancing the workload. Moreover, queue lengths for several stations were shortened, and the average processing time was decreased by around 50%. Also, a weekly production increase of 25% was achieved while lowering the cost per unit by around 8%. Research limitations/implications While the case study used was for a LED manufacturing system, the proposed framework could be implemented for any other existing production system. The research also meticulously presents the steps carried out for the development of the multi-method simulation model to allow readers to replicate the model and tailor it for their own case studies and projects. The hybrid model enables managers to navigate the trade-off decisions they often face when choosing advanced production output ahead of continuous improvement practices. The adoption of methodologies outlined in this paper would attain improvements in terms of queue lengths, utilization, reduced costs and improved quality and efficiency of a real, small factory. The findings suggest improvements and create awareness among practitioners for the utilization of quality tools that will provide direct benefits to their companies. Although the multi-method simulation is effective, a limitation of the current study is the lack of micro details within each station. Furthermore, the results are all based on one specific case study which is not enough to suggest and generalized findings. Originality/value This research combines the use of the three main hybrid simulation paradigms (SD, DES and AB) in a unified framework DMAIC methodology. Choosing the right models in DMAIC is important, challenging and urgently necessary. Also, this paper shows empirical evidence on its effectiveness.

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Section: Literature Reviewmentioning

confidence: 99%

Integration of Six Sigma and simulations in real production factory to improve performance – a case study analysis

Ahmed

Olsen²,

Page³

2022

IJLSS

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show abstract

“…These simulations are used in manufacturing, and a vast amount of research has been done using this methodology. For example, Rocha et al (2017) used AB modelling in a manufacturing setting with multi-agents to show how risks are reduced using this method and to meet requirements for customised products.…”

Section: Simulation Paradigmsmentioning

confidence: 99%

A comparison of three simulation methodologies for a Lean Six Sigma manufacturing project – a business case study

Ahmed

Page

Olsen

2019

IJLSS

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Purpose This paper aims to compare the prognostic and visualisation capabilities of all the three simulation paradigms to identify their suitability and rigor in eliminating weaknesses and bottlenecks in a Lean Six Sigma (LSS) project. Design/methodology/approach The paper uses an light-emitting diode (LED) factory as a business case to show the differences and advantages of using three different simulation techniques to solve a manufacturing problem. Findings Even though this is only one business case that shows how system dynamics (SD) can be effective in a Six Sigma manufacturing project, more examples are needed to validate this hypothesis within Six Sigma and Lean manufacturing fields. Even though, discrete-events (DE) and agent-based (AB) models are both equally well suited to develop the manufacturing processes and the choice of what to use may be arbitrarily dependent on the available software or the preference of the modeller, hybrid models seem to become extremely powerful. Therefore, more hybrid models need to be constructed within LSS (especially when a flowchart and a SIPOC ((Suppliers, Inputs, Process, Outputs and Customers) table are combined to develop a hybrid model) to achieve the most accurate results with accurate representation of reality. Originality/value There are three commonly used simulation techniques, DE, AB and SD, but choosing the right simulation methodology has often been a challenge.

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“…Zhou et al (2022) constructed a social learning evolution model based on agent, which can explore the evolution process of cloud manufacturing system. Rocha et al (2017) proposed a simulation framework integrating industrial simulator and MAS for verifying the effectiveness of new paradigms of manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Research on the evolution law of cloud manufacturing service ecosystem based on multi-agent behavior simulation

Dong,

Li,

Peng

et al. 2023

Concurrent Engineering

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Monitoring and evaluating the health of the cloud manufacturing service ecosystem (CMSE) is critical to ensuring the long-term development of the cloud manufacturing service platform. The behavior patterns of three types of market entities, service demander (SD), service provider (SP) and platform operator (PO), have an important impact on the evolution trend of CMSE. The formulation of platform transaction rules and development of operation and regulation strategies need clarified the evolution law of the CMSE. Therefore, the evolution framework of the CMSE is constructed, the behavior modes of market entities such as SP, SD, and PO are established respectively, and multi-agent behavior simulation experiments are carried out. Simulation analysis showed that the more sensitive the SP was to the transaction activity in the system, the earlier the ecosystem in which it was located enters the stable period. When the sensitivity k of SD to the number of SPs that are available for required services in the system was between 0.45 and 0.5, the evolution trend of CMSE would show an obvious turning point from rapid growth to shrinkage. PO could adopt flexible charging strategies at different stages of the evolution of the CMSE to maximize revenue.

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Environment to Simulate Distributed Agent Based Manufacturing Systems

Cited by 7 publications

References 13 publications

Integration of Six Sigma and simulations in real production factory to improve performance – a case study analysis

Integration of Six Sigma and simulations in real production factory to improve performance – a case study analysis

A comparison of three simulation methodologies for a Lean Six Sigma manufacturing project – a business case study

Research on the evolution law of cloud manufacturing service ecosystem based on multi-agent behavior simulation

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