MANUFACTURING SYSTEMS MODELLING USING SYSTEM DYNAMICS: FORMING A DEDICATED MODELLING TOOL

2003 ◽  
Vol 02 (01) ◽  
pp. 71-87 ◽  
Author(s):  
A. OYARBIDE ◽  
T. S. BAINES ◽  
J. M. KAY ◽  
J. LADBROOK
Keyword(s):  
System Dynamics ◽  
System Design ◽  
Discrete Event Simulation ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Computer Modelling ◽  
Discrete Event ◽  
Event Simulation ◽  
Manufacturing System Design ◽  
Computer Based

Discrete event simulation is a popular aid for manufacturing system design; however in application this technique can sometimes be unnecessarily complex. This paper is concerned with applying an alternative technique to manufacturing system design which may well provide an efficient form of rough-cut analysis. This technique is System Dynamics, and the work described in this paper has set about incorporating the principles of this technique into a computer based modelling tool that is tailored to manufacturing system design. This paper is structured to first explore the principles of System Dynamics and how they differ from Discrete Event Simulation. The opportunity for System Dynamics is then explored, and this leads to defining the capabilities that a suitable tool would need. This specification is then transformed into a computer modelling tool, which is then assessed by applying this tool to model an engine production facility.

A Blended System Dynamics-Discrete Event Physics-Based Model for Anomaly Detection in Cyber-Physical Manufacturing Systems

2020 ◽  
Author(s):  
Hong Yu ◽  
Ajay Raghavan ◽  
Saman Mostafavi ◽  
Deokwoo Jung ◽  
Yukinori Sasaki ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
System Dynamics ◽  
Discrete Event Simulation ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Continuous Variables ◽  
Event Simulation ◽  
Computational Overhead ◽  
Discrete Manufacturing System ◽  
Discrete Manufacturing

Abstract Being able to quickly detect anomalies and reason about their root causes in critical manufacturing systems can significantly reduce the analysis time to bring operations back online, thus reducing expensive unplanned downtime. Machine learning-based anomaly detection approaches often need significant amounts of labeled data for training and are challenging to scale for manufacturing deployments. A robust blended system dynamics and discrete event simulation physics-based modeling methodology is proposed for the task of automated anomaly detection. The blended model consists of discrete event simulation (DES) components for the discrete manufacturing process modeling, and system dynamics (SD) components for continuous variables. The methodology strikes a balance between the computational overhead for online monitoring and the level of details required to perform anomaly detection tasks. The implementation of models takes an object-oriented approach, allowing multiple components of a smart factory to be robustly described in a modular, extendable and reconfigurable manner. The proposed methodology is applied to and validated by data collected from a real commercial manufacturing plant. A production line is modeled with DES components and heat transfer is modeled with SD. The blended model is then utilized for anomaly detection. It is demonstrated that the model-based approach is effective not only for detecting but also explaining particular types of anomalies in a commercial discrete manufacturing system.

scholarly journals Incorporation of human factors into a Discrete Event Simulation Model for human centred assembly performance evaluation

2021 ◽  
Vol 40 (3) ◽  
pp. 437-448
Author(s):  
M.I. Abubakar ◽  
Q. Wang
Keyword(s):  
Human Factors ◽  
Discrete Event Simulation ◽  
Manufacturing Systems ◽  
Human Performance ◽  
Manufacturing System ◽  
Discrete Event ◽  
Integrated Approach ◽  
Design Stage ◽  
Early Design Stage ◽  
Event Simulation

Discrete Event Simulation (DES) tool is commonly used for the design, analysis, and evaluation of manufacturing systems. Human centred assembly systems offer better system flexibility and responsiveness due to inherent human intelligence and problem-solving abilities; human can deal with product variations and production volumes; and can always adapt themselves to multiple tasks after learning process. Nevertheless, human performance can be unpredictable, and may alter over time due to varying psychological and physiological states, these are often overlooked by researchers when designing, implementing, or evaluating a manufacturing system. In this paper a user-friendly integrated DES method was proposed to enable manufacturing system designers to investigate overall performance of human centred system considering effects of selected human factors. the method can permit manufacturing system designers to evaluate overall manufacturing system performance with considerations of parameters of human factors at early design stage. A case study was carried out using integrated approach; simulation results demonstrate the applicability of this approach.

scholarly journals Communication mechanism of the discrete event simulation and the mechanical project softwares for manufacturing systems

2018 ◽  
Vol 6 (1) ◽  
pp. 70-80 ◽  
Author(s):  
Quézia Manuela Gonçalves Laurindo ◽  
Túlio Almeida Peixoto ◽  
João José de Assis Rangel
Keyword(s):  
Control System ◽  
System Dynamics ◽  
Open Source ◽  
Discrete Event Simulation ◽  
Open Source Software ◽  
Manufacturing System ◽  
Discrete Event ◽  
Online Communication ◽  
Event Simulation ◽  
Des Model

Abstract This paper presents an integration mechanism for online communication between a discrete event simulation (DES) software and a system dynamics (SD) software. The integration between them allowed executing a hybrid and broader simulation, in which the complexity of the systems and their multi-faceted relationships may demand the combination of different simulation methods and the synergies between the techniques. The Ururau free and open-source software (FOSS) was applied to implement the DES model. In order to build the dynamic model, we used the software for mechanical design called CAD 3D Software Inventor®. Besides, we also employed the DES model in the test step of a control system in real time. The results of that mechanism implementation enabled the evaluation of different aspects of a typical manufacturing system. Furthermore, the integration between the control system and the DES model allowed validating the logic of the programmable logic controller (PLC). Highlights Mechanism for online communication between a discrete event simulation (DES) software and a system dynamics (SD) software. A free and open-source software (FOSS) was applied to implement the DES model. The results of that mechanism implementation enabled the evaluation of different aspects of a typical manufacturing system.

scholarly journals A Methodology to Adapt and Understand a Manufacturing System and Operations using Discrete-Event Simulatio

2019 ◽  
Vol 8 (6) ◽  
pp. 4998-5003
Keyword(s):  
Discrete Event Simulation ◽  
Manufacturing Industry ◽  
Manufacturing System ◽  
Discrete Event ◽  
The State ◽  
State Variables ◽  
Event Simulation ◽  
Potential Benefits ◽  
Short Hand

Discrete-Event Simulation (DES) is concerned with system and modeling of that system, where the state of the system is transformed at different discrete points from time to time, and several event occurs from time to time and the changes in state variables will transform then activities/attributes connected to these state variables changes according to the event. It is a robust methodology in the manufacturing industry for strategic, tactical, and operational applications for an organization, and yet organizations ignore to use simulation and do not rely on it. Moreover, companies that are using DES are not using the potential benefits but merely used as a short-hand basis for problems like bottlenecks, optimization, and in later stages of production like PLM, this paper aims to apply and analyze Discrete-Event Simulation through a Manufacturing System. The work describes here is to understand the concept of simulation for a system and to practice Discrete Event methodology

Multi-Methodology Approach

2021 ◽  
pp. 98-128
Keyword(s):  
System Dynamics ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Multiple Methods ◽  
Soft Systems ◽  
Agent Based ◽  
Event Simulation ◽  
Pluralistic Approach ◽  
The Difference

The pluralistic approach in today's world needs combining multiple methods, whether hard or soft, into a multi-methodology intervention. The methodologies can be combined, sometimes from several different paradigms, including hard and soft, in the form of a multi-methodology so that the hard paradigms are positivistic and see the organizational environment as objective, while the nature of soft paradigms is interpretive. In this chapter, the combination of methodologies has been examined using soft systems methodologies (SSM) and simulation methodologies including discrete event simulation (DES), system dynamics (SD), and agent-based modeling (ABM). Also, using the ontological, epistemological, and methodological assumptions underlying the respective paradigms, the difference between SD, ABM, SSM; a synthesis of SSM and SD generally known as soft system dynamics methodology (SSDM); and a promising integration of SSM and ABM referred to as soft systems agent-based methodology (SSABM) have been proven.

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