With the help of the simulated process, it could be realized all kind of experiments on a model. It could be tested lots of ideas, a returning in time to make possible remaking of manipulation scripts of some key variables with the purpose of observation based on experiments the effect above some important indicators of performances.production system, simulation, indicators, optimization

Constantin Marta

Daniel Vela

Guillaume Cavory

Olga Ioana Amariei

English

Research Papers in Economics

  19 
 
 
 
Olga Ioana Amariei, Guillaume Cavory, Constantin Marta, Daniel Vela  
The Simulated Process of a Production System using 
the Extend Program 
With the help of the simulated process, it could be realized all kind of 
experiments on a model. It could be tested lots of ideas, a returning in 
time to make possible remaking of manipulation scripts of some key 
variables with the purpose of observation based on experiments the ef 
fect above some important indicators of performances. 
1. Presenting the problem  
In figure  1 is presented a production workshop made of four machines, in 
which are processed and assembled two components X and Y. Before they are as 
sembled on machine 3, both components X and Y are submitted to the processing 
on machine 1 and machine 2. After the assembling on machine 3 of a component 
X with a component Y we obtain a new piece which is submitted to some process 
ing on machine 4. If one of the machines is busy, then the components or the 
pieces must wait to be processed making intermediary stocks between different 
points of processing. 
  We know the following: 
-  Processing time of component X on machine 1 is a probable dimension 
with normal distribution having a average of 0.12 hour/piece and standard 
deviation of 0.04 hour/piece. 
-  Processing time of component Y on machine 2 is a probable dimension 
with normal distribution having an average of 0.18 hour/piece and stan 
dard deviation of 0.07 hour/piece. 
-  Machine 3 assembles one piece X with one piece Y. Assembling time is 
constant 0.36 hours for one piece X and 0.36 hours for piece Y 
-  Machine 4 processes each new piece obtained through assembling. Proc 
essing time of assembled piece is a probable dimension having a average 
of 0.7 hour/piece and standard deviation of 0.2 hour/piece. 
 
ANALELE UNIVERSITĂłII  
 
“EFTIMIE MURGU” RE IłA 
 
ANUL XIII, NR. 1, 2006, ISSN 1453 - 7394 
   20 
 
Figure 1. The production section. 
 
For each waiting line it will be considered that the pieces are processed by the 
order that they arrive, so that serving discipline will be FIFO. 
Storing space for pieces that wait to be processed is limited, that’s why it will 
be specified the maximum capacity for each waiting line. 
-  Queue FIFO 1 capacity : 60 pieces 
-  Queue FIFO 2 capacity : 60 pieces 
-  Queue FIFO 3 capacity : 120 pieces 
-  Queue FIFO 4 capacity : 120 pieces 
-  Queue FIFO 5 capacity : 300 pieces 
 
2. The objectives of simulated process.  
-  To analyze and understand the behavior of the production system 
-  Showing out the thin places in the production system which generate long 
waiting lines and crowded machines 
-  Exploitation of growing politics of the production.  
 
3. Building a simulated process model.  
The presented production system in figure 1 may be considered a waiting sys 
tem. Simulated model will be a simulated model of discreet events. System’s com 
ponents are: two kinds of pieces which are processing, five intermediary stocks 
X 
Y 
Machine 1 
Machine 3 
Machining X 
Out 
Queue 1  Queue 3 
Machine 2 
Machining Y 
Assembling 
Machining XY 
Machine 
Queue 2  Queue 4 
Queue 5 
Work post 2 
Work post 1 
Work post 4   21 
which representing products waiting line who wait to be processed and four ma 
chines which representing the serving stations. That means that the system has 
eleven components. Time unit we will be using will be the hour. In figure 2 is rep 
resented the model of simulation in a working week with five days, in two shifts.  
 
 
Figure 2. The model with 4 working stations. 
 
After the beginning of simulated process, it will open a window for graphic 
representation of the fallowing process results, at the using rank of the serving 
stations and of waiting time of pieces in stocks. It could be observed that the using 
rank of serving stations presents big variations at the beginning of simulated proc 
ess, but after 30 simulated hours, this is stabilizing. Explaining this evolution con 
sists in the fact that simulated process begins without stocks. Totally has been re 
alized 109 pieces in one week of simulated process.  
Average number of finished pieces in one hour is approximate 0.73.  
With the help of “Stats Activities” block, represented in figure 3, we could ob 
serve that machine 3 had the biggest using rank of 99.7 %, fallowed by the ma 
chine 4 with 99.3 5, as we could observe that on machine 1 and machine 2 has 
been processed 344 pieces, which from, only 221 have been assembled on ma 
chine 3 and 109 processed on machine 4.    22 
 
 
Figure 3. The dialog window of Stats Activities block. 
 
Block “Stats Queues” of “Discrete” library furnished us some data regarding 
the five intermediary stocks of the analyzed system.  
If the experiments are repeated for one week many times, we could obtain 
other results. For the determinations of trust spell (interval) for average waiting 
time of pieces in lines, it will repeat the experiments many times (more the 30). At 
the same time it will be simulate 100 hours, 160 hours and 1000 working hours, 
repeated up to 30 times, and the important data are presented in table 1, 2 and 3. 
  
Table 1. 
Simulation time 
[hours] 
Number of finite products  
[pieces] 
Productivity 
80  109  1.36 
100  139  1.39 
160  227  1.41 
1000  1430  1.43 
 
We could observe from that tables that, when we increase simulation time, 
the production time of one piece decrease, because the stocks grow. It’s recom 
mendable to use rank of machine 1, 2 and 3 decrease and at machine 4 increases.    23 
Table 2. 
Simulation time 
[hours] 
Medium lenght 
in Queue FIFO 3 
[pieces] 
Medium lenght 
in Queue FIFO 4 
[pieces] 
Medium lenght 
in Queue FIFO 5 
[pieces] 
80  103  86  54 
100  107  89  66 
160  111  104  105 
1000  118  117  267 
 
Table 3. 
Simulation 
time 
[hours] 
Functioning 
machine 1 
[%] 
Functioning 
machine 2 
[%] 
Functioning 
machine 3 
[%] 
Functioning 
machine 4 
[%] 
80  50  75  99.7  99.3 
100  47  73  99.7  99.4 
160  42  62  99.9  99.7 
1000  22  33  62  99 
 
By the point of view of the analyzed production system, machine 4 could be 
considered “thin place” because it has the biggest process time: 0.70 hours aver 
age, and 1.35 hours maximum.  
 
 
Figure 4. The model with 5 working stations.   24 
 
If we are introducing the machine 4b (figure 4), who needs to work parallel 
with machine 4a, we could reduce intermediary stock number 5 and increase the 
using rank of all the machines and productivity. In one week of simulated work, 
the productivity has increased from 1.36 to 2.72 pieces/hour, for simulated spell of 
100 hours the productivity has increased from 1.39 to 2.74 pieces/hour, and for 
two weeks, from 1.41 to 2.75 pieces/hour.  
At the same time it could be observed in figure 4, in right up corner that the 
using  rank  of  all  machines  has  increased.  In  cloned  table  in  dialog  window  of 
“Stats Queues” block, it could be observed that average length of stock 5 has been 
decreased from 2.67 to 1.22 pieces. 
4. Conclusion  
The functioning in parallel at post 4 of work, of two machines, has leaded to 
the increasing of stock 5; at the considerable grow of using rank of all the ma 
chines from working post 1, 2 and 3, which leads a doubling of the production. 
References  
[1]  *****  Manuele d’utilisation d’Extend. IUT Bethune, 2003. 
 [2]   Habchi G. Modelling and Simulation of Complex Production Systems. Edito 
rial, Special Issue of SIMPRA, vol. 8, n° 5, December 2000. Guest Editor – Se 
lected Papers from the 2nd French Conference on Modelling and SIMulation 
(MOSIM'99). 
[3]    Habchi G.,Berchet C. Le Pilotage Industriel : Concepts de Base pour une Ap 
proche Intégrée. RFGI, Revue Française de Gestion Industrielle, Vol. 18, N° 2, 
1999, pp. 55 72. 
 
Addresses: 
Please insert here the addresses according to the example below. 
·  Asist. Drd. Eng. Olga Ioana Amariei, “Eftimie Murgu” University of Re 
 iŃa, PiaŃa Traian Vuia, nr. 1 4, 320085, Re iŃa, lepsiolga@uem.ro 
·  Conf. Dr. Guillaume Cavory, Universite d’Artois, IUT Bethune, 1230 Rue 
de l’Universite ,62408 Bethune Cedex, cavory@univ artois.fr 
·  Conf. Dr. Eng. Constantin Marta, “Eftimie Murgu” University of Re iŃa, 
PiaŃa Traian Vuia, nr. 1 4, 320085, Re iŃa, maco@uem.ro 
·  Prep. Drd. Eng. Daniel Vela, Eftimie Murgu” University of Re iŃa, PiaŃa 
Traian Vuia, nr. 1 4, 320085, Re iŃa, d.vela@uem.ro 

d’utilisation d’Extend.

Le Pilotage Industriel : Concepts de Base pour une Ap proche Intégrée. RFGI, Revue Française de Gestion Industrielle,

Modelling and Simulation of Complex Production Systems. Edito rial,

The Simulated Process of a Production System using the Extend Program

http://www.anale-ing.uem.ro/2006/2006_a3.pdf

The Simulated Process of a Production System using the Extend Program

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