A modular hybrid simulation framework for complex manufacturing system design

Show simple item record

dc.contributor.author Farsi, Maryam
dc.contributor.author Erkoyuncu, John Ahmet
dc.contributor.author Daniel, Steenstra
dc.contributor.author Roy, Rajkumar
dc.date.accessioned 2019-06-26T08:27:53Z
dc.date.available 2019-06-26T08:27:53Z
dc.date.issued 2019-02-06
dc.identifier.citation Farsi M, Erkoyuncu JA, Steenstra D, Roy R. A modular hybrid simulation framework for complex manufacturing system design. Simulation Modelling Practice and Theory, Volume 94, July 2019, pp. 14-30 en_UK
dc.identifier.issn 1569-190X
dc.identifier.uri https://doi.org/10.1016/j.simpat.2019.02.002
dc.identifier.uri https://dspace.lib.cranfield.ac.uk/handle/1826/14265
dc.description.abstract For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated. en_UK
dc.language.iso en en_UK
dc.publisher Elsevier en_UK
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Complex manufacturing en_UK
dc.subject Modular hybrid simulation en_UK
dc.subject Multi-agent system en_UK
dc.subject Resource planning en_UK
dc.subject Agent-based modeling en_UK
dc.title A modular hybrid simulation framework for complex manufacturing system design en_UK
dc.type Article en_UK
dc.identifier.cris 22831478

Files in this item

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International

Search CERES


My Account