Discrete-Event Simulation Software for Modeling Flexibility-Driven Manufacturing Processes

2015 ◽  
Author(s):  
Nadia Galaske ◽  
Erdal Tantik ◽  
Reiner Anderl
Keyword(s):  
Modeling And Simulation ◽  
Discrete Event Simulation ◽  
Production Systems ◽  
Discrete Event ◽  
Value Added ◽  
Life Cycles ◽  
Simulation Software ◽  
Manufacturing Processes ◽  
Material Flows ◽  
Event Simulation

In times of globalized markets and rapidly advancing technologies, companies are demanded to produce highly individualized products in shorter life cycles. This requires a certain flexibility in production processes, which, in turn, leads to a higher process complexity. In order to face these challenges, companies need to rely increasingly on the application of software tools for modeling and simulation of production systems. One of the most commonly used tools in the field of digital production planning and control is the discrete-event simulation (DES). A discrete-event simulation software allows production planners to create digital models of production systems and simulate process and material flows. It can be used not only to improve the design of production systems in the early stage of planning, but also to analyze changes in the system’s behavior during operative processes. In this paper, an event-based modeling and simulation software for flexibility-driven manufacturing processes in value-added process chains is developed. The software presented in this paper is aimed particularly at small and medium enterprises (SMEs) with low degree of automation and high product variety. The goal of this approach is to enable the modeling and simulation of manufacturing systems where the required manufacturing operations depend on production workers and vary with each production order. Using the approach described in this paper, a high variety of manufacturing process sequences in a flexible manufacturing system with different layouts, where material flows, worker paths, and part routings are not determined in fixed order, can be modeled, analyzed, and optimized.

Anlauf soziotechnischer Produktionssysteme*/Ramp-up of socio-technical production systems - Ramp-up-oriented design of socio-technical production systems by use of discrete-event simulation

2016 ◽  
Vol 106 (06) ◽  
pp. 451-456
Author(s):  
F. Prof. Klocke ◽  
P. Prof. Letmathe ◽  
J. Stauder ◽  
P. Bußwolder
Keyword(s):  
Discrete Event Simulation ◽  
Machine Tools ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Discrete Event ◽  
Life Cycles ◽  
Manufacturing Companies ◽  
Production Engineering ◽  
Event Simulation ◽  
Technical Production

Kürzere Produktlebenszyklen stellen produzierende Unternehmen in Deutschland vor die Herausforderung, Anläufe häufiger und in immer kürzerer Zeit zu bewältigen. Um dieser Herausforderung zu begegnen, müssen Produktionssysteme im Hinblick auf die Anlaufphase optimiert werden. In Kooperation zwischen dem Werkzeugmaschinenlabor WZL und dem Lehrstuhl für Controlling der RWTH Aachen entstand ein erster Ansatz für soziotechnische Produktionssysteme.   Due to shorter product life cycles, manufacturing companies in Germany have to face the challenge of managing ramp-ups more often and in ever decreasing times. To overcome these challenges, manufacturing systems must be optimized with regard to the ramp-up stage. As a result of the collaboration of the Laboratory for Machine Tools and Production Engineering and the Chair for Management Accounting of the RWTH Aachen university, a first approach for socio-technical manufacturing was developed.

scholarly journals The Integration of Human Factors into Discrete Event Simulation and Technology Acceptance in Engineering Design

2021 ◽  
Author(s):  
Petrit Dode
Keyword(s):  
Human Factors ◽  
Technology Acceptance ◽  
Discrete Event Simulation ◽  
New Technology ◽  
Discrete Event ◽  
Value Added ◽  
Learning Curves ◽  
Event Simulation ◽  
Initial Cost ◽  
Human Factors Modeling

This action research thesis aimed to: 1) develop and test a viable Discrete Event Simulation and Human Factors Modeling approach for an Ontario based telecommunication company, and 2) identify the factors that affect the uptake and application of the approach in work system design. This approach, which was validated at the Company, incorporated fatigue dose and learning curves in a Discrete Event Simulation model. The barriers to uptake included: Time constraints, lack of technological knowledge and initial cost. The uptake facilitators were: High frequency products produced, clear value added to leadership, defects reduction and the Company being open to new technology. In addition to helping design a manual assembly line with fewer bottlenecks and reduce the human factors risks for the employee, the developed approach showed a 26% correlation with quality defects. Further research is recommended to identify additional human factors and their benefits.

Discrete Event Simulation Of The COVID-19 Sample Collection Point Operation

2021 ◽  
Author(s):  
Martina Kuncova ◽  
Katerina Svitkova ◽  
Alena Vackova ◽  
Milena Vankova
Keyword(s):  
Discrete Event Simulation ◽  
Waiting Times ◽  
Discrete Event ◽  
Real Data ◽  
Simulation Software ◽  
Sample Collection ◽  
The Real ◽  
Event Simulation ◽  
Collection Process ◽  
Collection Point

The year 2020 was very challenging for everyone due to the COVID-19 pandemic. Many people turn their lives upside down from day to day. Politicians had to impose completely unprecedented measures, and doctors immediately had to adapt to the huge influx of patients and the massive demand for testing. Of course, not all processes could be planned completely efficiently, given that the situation literally changes from minute to minute, but sometimes better planning could improve the real processes. This contribution deals with the application of simulation software SIMUL8 to the analysis of the COVID-19 sample collection process in a drive-in point in a hospital. The main aim is to create a model based on the real data and then to find out the suitable number of other staff (medics) helping a doctor during the process to decrease the number of unattended patients and their waiting times.

scholarly journals Optimization of tillage and sowing operations using discrete event simulation 

2018 ◽  
Vol 64 (No. 4) ◽  
pp. 187-194 ◽  
Author(s):  
Armaghan Kosari Moghaddam ◽  
Hassan Sadrnia ◽  
Hassan Aghel ◽  
Mohammad Bannayan
Keyword(s):  
Discrete Event Simulation ◽  
Discrete Event ◽  
Daily Basis ◽  
Simulation Software ◽  
Field Size ◽  
Daily Work ◽  
Event Simulation ◽  
Significant Difference ◽  
Average Work ◽  
Work Method

A simulation model was developed for secondary tillage and sowing operations in autumn, using discrete event simulation technique in Arena<sup>®</sup> simulation software (Version 14). Eight machinery sets were evaluated on a 50-hectare farm. Total costs including fixed-costs, variable costs and timeliness costs were calculated for each machinery set. Timeliness costs were estimated for 21-years period on daily basis (Daily Work method) and compared with another method (Average Work method) based on the equation proposed by ASAE Standards, EP 496.3FEB2006. The Inputs of the model were machinery sets, field size, machines performances and daily soil workability state. The optimization criteria were the lowest costs and lowest standard deviation in daily work method plus the lowest costs based on average work method. The validity of the model was evaluated by comparing the output of the model with field observed data collected from various farms. Results revealed that there was no significant difference (P &gt; 0.01) between the observed and predicted finish day. 

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