The paper brings up many explanations regarding the understanding of connections between causes and effects, through simulated models. It wishes to establish and simulate some scripts witch concur to the development of a business.business, simulation, mathematical models

Constantin Marta

Guillaume Cavory

Olga Ioana Amariei

Raul Malos

English

Research Papers in Economics

  25 
 
 
 
Olga Ioana Amariei, Guillaume Cavory, Constantin Marta, Raul Malo  
The using of Simulated Models in Management 
 
The paper brings up many explanations  regarding   the understanding of 
connections  between  causes  and  effects,  through  simulated  models.  It 
wishes to establish and simulate some scripts witch concur to the develop 
ment of a business. 
1. The simulated process. Introductive elements. 
The simulate term referees to a specific class of dynamic models who need a 
detailed observation in time, of occasional dimensions witch intervene in a complex 
system. Time moments that make an event to be activated are occasional. The 
events are discreet, so that, between two events it’s considered that nothing hap 
pens. 
 The simulating process of discreet events is applied, generally, for the study 
ing of systems in which it is necessary the fallowing of the way that different ob 
jects are going, and in which in different time moments, events happens, events 
who makes suddenly and irregular states of system. 
Through simulated models based on discreet events, it could be analyzed eas 
ily the behavior of a system in case of some changes, or it could be seen the be 
havior of a new system even before the prototype is made. The building and easily 
made simulated experiments, combined with the generating of statistics and the 
seen of the results is close connected with software simulating systems. 
 
2. The simulated business with Extend 6 program 
A company has a new affair for renting boats to the fishermen that arrive in 
Delta. The manager of the company hopes that, through low prices and quality 
services can defeat the competition and attract the clients. The clients who wish to 
rent a boat, are sitting in front of the rental office, and wait to be served by the 
order that they arrived. The service that they are getting, consist in completing a 
rental demand of a boat, choosing one of the rental options and receiving some 
ANALELE UNIVERSITĂłII  
 
“EFTIMIE MURGU” RE IłA 
 
ANUL XIII, NR. 1, 2006, ISSN 1453 - 7394 
   26 
recommendation. At the end, the client leaves the office, get the boat and goes in 
Delta. 
The manager of the company wishes to use a simulated program with Extend 
6, to investigate some scripts. 
In case of, for rental, is available one type of boat, the objective of the simu 
lated process consist in determining the number of working points with the clients 
for renting contracts , so that, the waiting time of clients and the length of the 
waiting line, to be smaller. 
In case of offering different types of boats, the objectives of simulated process 
consist in determining the number and types of boats necessary for rental, so that, 
to be evaded the possibility of missing boats and insatisfaction of clients. 
Continuing, working points with clients, will be named as “ serving stations”. 
The manager of the company has proposed to himself to make the fallowing 
models: 
Model 1. To obtain the data necessary for the simulating process, it has been 
chosen a normal working day, and it has been recorded the number of fishermen 
which  arrived  in  group  at  the  rental  office  every  10  minutes.  Through  statistic 
grouping method it has been obtained Table 1, with the distribution of relative fre 
quency of client number. 
 
Table 1. 
Number of fisherman from a group  Relative frequency  
0  0.65 
1  0.14 
2  0.18 
3  0.03 
 
For the beginning, we’ll assume that the serving duration of a client is exactly 
10 minutes. The manager of the company decided to simulate one week, and that 
means 7*24*60=10.080 hours. 
The objectives of simulated process: determination of the number of working 
points for signing the rental contracts, so that, the waiting time for clients and the 
length of the waiting line to be smaller. 
In figure 1 it’s showed the simulated model associated to the first script, but 
with a single serving station, proposed by the manager. 
Totally have been signed 607 contracts in one week of simulated work. 
The  average  number  of  clients  in  rental  office  is  0.73,  and  the  maximum 
length of waiting line is 7 clients. Average waiting time of a client in serving station 
is 12.15 minutes, and the worst case scenario it could be waiting one hour. 
It can be seen that, the using rank of serving station is only 60 %, and the 
maximum length of the waiting line are too big. That’s why, we are introducing an 
additionally serving station.   27 
 
Figure 1. Model 1 with one serving station 
 
In figure 2 we can see that the using rank for each of two serving stations is 
approximate 32 %. 
 
 
Figure 2. Dialog window for the dialog « Activite Multiple» block   28 
 
In  the  rental  office  entered  639  clients  and  has  been  served  637,  during 
10.080 minutes, and the average number of clients in each serving stations are 
approximate 0.63. Maximum length of waiting line dropped down from 7 to 4, and 
the maximum waiting time dropped down from 60 minutes to 10 minutes (figure 
3). 
 
 
Figure 3. Dialog window for the dialog « File FIFO » block 
 
Model 2 . With probable spell of between two arrivals and serving time that 
is probable, represents correctly the real situation. The groups aren’t arriving ex 
actly at 10 minutes, and the time of signing a contract isn’t exactly 10 minutes. 
It’s considered the spell (interval) between two arrivals as being a probable 
dimension with triangular distribution, and the time of signing a contract is a prob 
able dimension with exponential distribution. 
To simulate the new model is necessary to be modified only at  “Group fisher 
men” block and “Rental office” the distributions. At the “Activite delai” block we put 
again a single serving station. 
After the simulated process, the results are : 
  average waiting time of a client is approximate 13.66 minutes, comparing to 
12.55  minutes  in  constant  dimension  model,  and  the  worst  case  scenario,  the 
maximum waiting time is 102.87 minutes comparing to 60 minutes. 
  Average length of waiting line is 0.79, and maximum is 7 entities. 
  The using rank of serving stations is approximate 56 %. 
In serving station average are 0.56 entities, and average waiting time for sign 
ing a rental contract is 9.69 minutes. 
It’s introduced another serving station ant the results dropped down at aver 
age waiting time from 13.66 minutes to 2.14 minutes, and the average length of   29 
waiting line has dropped down from 0.79 to 0.14 entities. The using rank of a serv 
ing station is 31 %, and average clients number present in the office is 0.62. The 
average time of signing a rental contract is approximate 9.76 minutes. 
Model 3. In this model it increases the number of serving stations (maximum 
four), connected to the dimension of waiting line. It can be seen, that using a vari 
able number, between 1 and 4 serving stations, the performances of boat rental 
office, has been worsen, comparing with the case of using a constant number of 
two serving stations. These results can be explained by the using rank of a serving 
station (55 %) is approximate equal with the average number of entities from a 
station (0.57), which means that the most frequent used is one serving station. 
The  manager  of  the  company  has  decided  to  continue  the  experiments  of 
simulated process based on two serving stations model. The objective of the fol 
lowing simulated process consist in determination of the needs of renting boats, to 
evade the probability of missing boats and loosing clients. 
 
 
Figure 4. Simulation model with various variable number of serving stations 
 
Model 4. The manager of the company observed that approximate 20 % of 
fishermen want to rent an engine boat. Initially, the company has 70 boats, from 
which only 20 have engine. The clients for engine boats, rent the boats for 2 days,   30 
plus or minus one day. The renting time of boats is constant distributed between 
three and four days. 
The simulated process is presented in figure5, and in figure 6 we can see the 
dialog windows of  “State files” and “Stats activites” blocks. 
 
 
Figure 5.  
 
 
 
Figure 6.    31 
 
For engine boats, with the probability of 95 %, average length of waiting line 
for receiving the boat is 40.7+/ 2.21, and the maximum length is. 91.7+/ 4.06. 
For no engine boats, with the probability of 95 %, average it rents 138.9+/ 
1.732 no engine boats, from which it returns 75.80+/ 1.627 boats on week and 
33.63+/ 1.788 engine boats, from which it returns 26.77+/ 1.604 boats on week. 
The big waiting line and time of waiting for receiving a boat  are caused by the 
missing of boats from the stock. 
It is tried the modification of boats stock at the beginning of the week. It is 
added 5 engine boats and the results are presented in figure 7. 
 
 
 
Figure 7.  
3. Conclusion  
The simulating of an affair in Extend 6 pointed out a base principle of modeling 
and simulating, by that meaning that it started from a simple model of the issue 
that need to be solved and it’s developing the model till it reaches out a better un 
derstanding of the connections between causes and effects.   32 
References  
[1]  *****  Manuel d’utilisation d’Extend. IUT Bethune, 2003. 
[2]  Habchi G. Modelling and Simulation of Complex Production Systems. 
Editorial, Special Issue of SIMPRA, vol. 8, n° 5, December 2000. Guest 
Editor – Selected 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 Approche Intégrée. RFGI, Revue Française de Gestion Industrielle, 
Vol. 18, N° 2, 1999, pp. 55 72. 
 
Addresses: 
·  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. Ec. Raul Malo , Eftimie Murgu” University of Re iŃa, PiaŃa 
Traian Vuia, nr. 1 4, 320085, Re iŃa, raul_malos@yahoo.com 
 

d’utilisation d’Extend.

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

Modelling and Simulation of Complex Production Systems.

The using of Simulated Models in Management

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The using of Simulated Models in Management

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