Using Discrete Event Simulation Software in a MET Program

2001 ◽  
Author(s):  
Alok K. Verma
Keyword(s):  
Discrete Event Simulation ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Real Life ◽  
Simulation Software ◽  
Computer Integrated Manufacturing ◽  
Student Interest ◽  
Technology Program ◽  
Event Simulation ◽  
Life Problems

Abstract Discrete event simulation software can be an effective tool for teaching comparative analysis of manufacturing systems for improving performance. ProModel software is used in a computer integrated manufacturing course in the Mechanical Engineering Technology Program for this purpose. Use of this tool allows an instructor to demonstrate the pros and cons of various manufacturing scenarios and recommend a solution. Student interest is enhanced by assigning simulation of real life problems from local industries.

Evaluation of postponement strategy in the production of table wine in Brazil: a discrete event simulation approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carolina Reis Gualberto ◽  
Lásara Fabrícia Rodrigues ◽  
Karine Araújo Ferreira
Keyword(s):  
Discrete Event Simulation ◽  
Discrete Event ◽  
Real Life ◽  
Minas Gerais ◽  
Future Research ◽  
Lost Sales ◽  
Wine Production ◽  
Content Type ◽  
Event Simulation ◽  
Postponement Strategy

Purpose The purpose of this paper is to develop an approach to evaluate the partial postponement strategy and compare it with postponement and make-to-stock (MTS) strategies in the production of table wine in wineries in the state of Minas Gerais (south-eastern Brazil). Design/methodology/approach An approach based on discrete event simulation was developed to support decision-making in the wine sector. Simulation models were used to analyse partial postponement, postponement and MTS strategies in wine production. These models were inspired by a typical table wine producer selected from an exploratory study conducted in 12 wineries of Minas Gerais state in Brazil. Findings Hybrid strategies, such as partial postponement, favour the advantages of postponement and MTS depending on the portion of semi-finished and finished goods adopted. Wine production characteristics favour postponement and partial postponement with high semi-finished product levels (customer order-driven product) because this allows companies to reduce their inventory of bottles, despite possible increases in lost sales and costs. MTS and partial postponement with high finished product levels (forecast-driven product) present higher costs with bottled wine storage; however, these strategies reduce lost sales and improve agility and reliability in deliveries. Research limitations/implications Future research should analyse the production of table wines in other regions of the country and the production of fine wines. Practical implications The findings suggest promising perspectives for real-life applications in wineries in Brazil and other countries. Originality/value Simulation techniques allow the analysis of production strategies in little-known industries, such as table wine production in Brazil. The approach developed is flexible enough to support decisions and to be adapted to companies’ and markets’ characteristics and to test specific strategies.

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. 

scholarly journals CONWIP versus POLCA: A comparative analysis in a high-mix, low-volume (HMLV) manufacturing environment

2016 ◽  
Vol 9 (2) ◽  
pp. 432 ◽  
Author(s):  
Todd Frazee ◽  
Charles Standridge
Keyword(s):  
Discrete Event Simulation ◽  
Lead Time ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Model Verification ◽  
Manufacturing Environment ◽  
Event Simulation ◽  
Work In Process ◽  
Low Volume ◽  
Average Lead

Purpose: Few studies comparing manufacturing control systems as they relate to high-mix, low-volume applications have been reported. This paper compares two strategies, constant work in process (CONWIP) and Paired-cell Overlapping Loops of Cards with Authorization (POLCA), for controlling work in process (WIP) in such a manufacturing environment. Characteristics of each control method are explained in regards to lead time impact and thus, why one may be advantageous over the other.Design/methodology/approach: An industrial system in the Photonics industry is studied. Discrete event simulation is used as the primary tool to compare performance of CONWIP and POLCA controls for the same WIP level with respect to lead time. Model verification and validation are accomplished by comparing historic data to simulation generated data including utilizations. Both deterministic and Poisson distributed order arrivals are considered. Findings: For the system considered in this case study, including order arrival patterns, a POLCA control can outperform a CONWIP parameter in terms of average lead time for a given level of WIP. At higher levels of WIP, the performance of POLCA and CONWIP is equivalent. Practical Implications: The POLCA control helps limit WIP in specific áreas of the system where the CONWIP control only limits the overall WIP in the system. Thus, POLCA can generate acceptably low lead times at lower levels of WIP for conditions equivalent to the HMLV manufacturing systems studied.Originality/value: The study compliments and extends previous studies of  CONWIP and POLCA performance to a HMLV manufacturing environment. It demonstrates the utility of discrete event simulation in that regard. It shows that proper inventory controls in bottleneck áreas of a system can reduce average lead time.

Sign in / Sign up

Export Citation Format

Share Document