Simulation Methods for Changeable Manufacturing

Seleim, A.; Azab, Ahmed; AlGeddawy, Tarek

doi:10.1016/j.procir.2012.07.032

Cited by 20 publications

(8 citation statements)

References 38 publications

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“…Step 2. System Boundary Once the system of Interest is chosen, its essential to consider the boundaries for the system these boundaries include are machines in that system and their capabilities, product data, number of workers, and Material Handling Systems (MHSs), Management system, Distribution of facilities in the system [3], [12].…”

Section: System Of Interestmentioning

confidence: 99%

A Methodology to Adapt and Understand a Manufacturing System and Operations using Discrete-Event Simulation

Kshatra,

Prasad*,

Akhil

et al. 2019

IJEAT

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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

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Section: System Of Interestmentioning

confidence: 99%

A Methodology to Adapt and Understand a Manufacturing System and Operations using Discrete-Event Simulation

Kshatra,

Prasad*,

Akhil

et al. 2019

IJEAT

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“…In today's competitive and unpredictable business world, modelling and simulation has been used as a tool to support decision making in different areas such as manufacturing, services, healthcare, public services and many more, being an essential element of daily process in enterprises (Jahangirian, Eldabi, Naseer, Stergioulas, & Young, 2010;Azab & AlGeddawy, 2012). Since the Monte Carlo method invented in 1947, many other simulation methods have emerged in order to determine the outcome of an experiment or event differentiating mainly between dynamic and static modelling (Mourtzis, Doukas, & Bernidaki, 2014;Perez et al, 2018).…”

Section: Modelling and Simulation In An Uncertain Worldmentioning

confidence: 99%

“…One of the main problems with modelling and simulation is that it losses its effectiveness when decision alternatives become too many or the problem to be analysed has an important degree of uncertainty or misleading information (Azab & AlGeddawy, 2012). In order to overcome these limitations the use of different fuzzy techniques and aggregation of information operators have become an important part in modelling and simulation (Blanco-Mesa, Merigó, & Gil-Lafuente, 2017;Cabrerizo et al, 2017; Cid-López, Hornos, Carrasco-González, & Herrera-Viedma, 2018).…”

Section: Modelling and Simulation In An Uncertain Worldmentioning

confidence: 99%

Modelling and Simulation in Business, Economics and Management

León‐Castro

Merigó

Avilés‐Ochoa³

et al. 2019

Technological and Economic Development of Economy

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“…The manufacturing system may experience structural changes during their operational life span resulting from adding new system components, replacing or retiring old equipment to react to the changes in products, technology or markets [43]. Because of the complexity and dynamic nature in the manufacturing systems the spreadsheet and flowcharts are almost impossible to capture the complicated process configuration and its complex constraints.…”

Section: Modelling and Simulation Techniquesmentioning

confidence: 99%

Methodology – A Review of Intelligent Manufacturing: Scope, Strategy and Simulation

Sun

2018

Communications in Computer and Information Science

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This paper presents a critical review of some existing modelling, control and optimization techniques for energy saving, carbon emission reduction in manufacturing processes. The study on various production issues reveals different levels of intelligent manufacturing approaches. Then methods and strategies to tackle the sustainability issues in manufacturing are summarized. Modelling tools such as discrete (dynamic) event system (DES/DEDS) and agent-based modelling/simulation (ABS) approaches are reviewed from the production planning and control prospective. These approaches will provide some guidelines for the development of advanced factory modelling, resource flow analysis and assisting the identification of improvement potentials, in order to achieve more sustainable manufacturing.

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Simulation Methods for Changeable Manufacturing

Cited by 20 publications

References 38 publications

A Methodology to Adapt and Understand a Manufacturing System and Operations using Discrete-Event Simulation

A Methodology to Adapt and Understand a Manufacturing System and Operations using Discrete-Event Simulation

Modelling and Simulation in Business, Economics and Management

Methodology – A Review of Intelligent Manufacturing: Scope, Strategy and Simulation

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