Decision making is one of the important tasks of every manager. The process of taking decisions has to be based on knowledge. For optimizing this process some software solutions has been created. In this article we tried to summarize some of the features which exists in some software applications.decision making, risk solver

Abrudan Maria Madela

Demian Horia

Perez Bernabeu Elena

English

Research Papers in Economics

315 
TOOLS USED IN DECISION MAKING 
Demian Horia 
Str. Universitatii nr 1, Faculty of Economics, University of Oradea, Romania 
Bernabeu Elena Perez 
Universitat Politecnica de Valencia, Spain 
Abrudan Maria Madela 
Str. Universitatii nr 1, Faculty of Economics, University of Oradea, Romania 
 
Abstract: Decision making is one of the important tasks of every manager. The process of taking 
decisions has to be based on knowledge. For optimizing this process some software solutions has 
been  created.  In  this  article  we  tried  to  summarize  some  of  the  features  which  exists  in  some 
software applications. 
 
Keywords: decision making, risk solver 
 
Introduction 
Knowledge is the main source of competitive 
advantage  in  the  knowledge  economy  and 
innovation  in  organizations  involved  in  the 
services  sector.[1]  It’s  really  about  the 
transformation of information into useful data 
management process. We must keep in mind, 
however,  that  the  speed  with  which 
information  circulates,  accessibility,  search 
and how their storage are critical factors to be 
taken into account in streamlining decision-
making. 
Taking  into  account  the  likelihood  of 
achieving  results,  several  models  are 
developed  to  optimize  decisions,  starting 
from the classification of decisions: decisions 
in  conditions  of  certainty,  risk  and 
uncertainty.  The  elements  that  distinguish 
these types of decisions is very important for 
all  - managers and software developers. 
While  the  development  of  computer 
applications  has  raised  no  problems  using 
established  models  in  case  of  economic 
consequences  known  with  certainty  - 
ELECTRE, for example - the real challenge 
for  software  developers  is  to  optimize  the 
decisions of category risk and uncertainty. 
In the service sector, as in any other field of 
economics,  all  computer  application  are 
based on the distinction between definitions 
of risk and uncertainty:   
-risk  is  present  when  future  events 
occur with measurable probability 
-uncertainty  is  present  when  the 
likelihood  of  future  events  is 
indefinite  or  incalculable  (Frank  H. 
Knight, 1921) 
 
Between different algorithms used inside the 
software  “Monte  Carlo  has  become  a 
standard tool of risk management” [PRO02] 
According to Michele Gambera, the accuracy 
of  the  results  is  limited  by  the  number  of 
simulated histories. The process of simulation 
will be longer when we have more data. So, 
an  important  issue  will  be  how  fast  a 
computer program can do all the calculations. 
New technologies which were developed will 
improve the speed of the calculations. Cloud 
computing is one of this technology. If we are 
taking  into  consideration  cloud  computing, 
we have to be aware that the algorithm has to 
be  done  in  parallel  and  also  if  there  are  a 
mechanism of sending and receiving subsets 
of data between these processes. Monte Carlo 
algorithm  is  one  which  can  be  done  in 
parallel computing. 
In Monte Carlo simulation an important fact 
is  the  distribution  which  we  used  for  our 
model.  There  is  a  lot  of  distribution  which 
can be used like normal distribution, uniform 
distribution, triangular distribution and so on.  316 
According  to  an  article  of  S.  Savage,  S. 
Scholtes,  and  D.  Zweidler  “simulations 
without acceptable input distributions are like 
light  bulbs  without  electricity”,  and  they 
observe  very  well  that  “Only  a  few  people 
within an organization have the expertise to 
estimate  probability  distributions,  and  even 
fewer  have  the  managerial  authority  to  get 
their estimates accepted on an enterprise-wide 
basis”.  One  of  their  ideas  was  to  manage 
probability  distributions  centrally  and  to 
replace the classical probability distributions 
with  stochastic  libraries  a  pre-generated 
random  trials  that  approximate”  stochastic 
inputs or can be the results of simulation and 
optimization models.  
ProbabilityManagement.org has presented the 
DIST™ Distribution  String  which  brings  to 
Monte Carlo simulation, a data structure that 
contains  thousands  of  Monte  Carlo  trials. 
These trials are compressed through XML, so 
that structure can be store in the single cell of 
a spreadsheet or in a field of a data base. The 
main  advantages  of  DIST  is  that  it  can  be 
generated  by  experts,  to  represent  virtually 
any type of probability distribution, and then 
it  can  be  distributed  to  others  in  a 
standardized format. 
Other important concept is to use interactive 
simulation in your model. 
Risk  Solver  Platform  is  developed  by 
FrontlineSystems and is fully integrated with 
Microsoft Excel. This software can be used 
for  linear  and  non-linear  model,  for 
conventional  optimization,  for  decision  tree 
model,  for  risk  analysis  and  Monte  Carlo 
simulation. 
 “This package was also the first, and as of 
this date, the most powerful interactive Monte 
Carlo  simulation  package  for  Microsoft 
Excel. For simple simulations involving a few 
variables,  it  can  perform  100,000  trials 
essentially  instantly,  as  the  user  changes 
parameters  in  the  model.” 
[http://probabilitymanagement.org/Software.h
tm] 
Immediate  after  installation  the  menu  of 
Excel  will  be  improved  with  a  ribbon  with 
specific  option  for  optimization  and 
simulation,  like  it  can  be  seen  in  the 
following figure.   
 
 
 
 
Also  a  Model  Pane  which  consists  of  four 
tabs will be displayed in the right part of the 
screen.  Model  tab  is  used  for  seeing  the 
description for the current model which can 
be  one  of  Optimization,  Simulation  or 
Decision  tree.  Platform  tab  is  used  for 
defining parameters for Optimization Model, 
parameters  for  Simulation  Model  or 
parameters for decision Tree Model.  
Engine  tab  is  used  for  specifying  engine 
which  will  be  used  in  solving  the  problem. 
We can select from Standard GRG Nonlinear 
Engine,  Standard  LP/Quadratic  Engine, 
Standard  Evolutionary  Engine,  Standard 
Interval  Global  Engine,  Standard  SOCP 
Barrier  Engine,  Risk  Solver  Engine,  or  we 
can let it choose automatically which engine 
to use in solving the problem. Which engine 
should we use? Their recommendation is to 
try  them all, and use the one that performs 
best on your model.[RISK01] 
For every of these engine we can define some 
parameters  which  will  be  used,  like  Monte 
Carlo number of trials, the algorithm used for 
random  number  generations,  number  of 
iterations, Integer tolerance, Multi Start and 317 
so on. Integer tolerance is used to stop finding 
a  solution  in  Branch  and  Bound  algorithm, 
when the relative difference between the best 
found solution and the best possible solution 
is less than this value. If this value is set to 
zero, the algorithm will be stopped with the 
true optimal integer solution. 
 
When Multistart option is set to true, methods 
for finding global solution to the problem will 
be used. 
Output tab is used for displaying the results. 
Nonlinear  problems  are  more  difficult  to 
solve  than  linear  problems.  In  risk  solver 
platform  an  important  advantage  is  that  of 
using   (Analyze without solving) button, 
which will help us in finding the model. If the 
nonlinear  problem  is  convex,  the  global 
optimal solution will be found. 
For using this platform we have to be familiar 
with  Microsoft  Excel.  The  platform  came 
with  a  lot  of  useful  examples  for 
optimization,  simulation    and  simulation  & 
optimization  like  Simple  Business  Plan 
Forecast Model with Uncertainty, an example 
illustrating  modeling  an  investment  fund's 
growth  given  uncertain  return  rates,  an 
example  illustrating  modeling  multi-period 
Inventory  problems  with  uncertain  demand, 
an  example  illustrating  the  use  of  Monte-
Carlo Simulation to estimate workforce levels 
required  to  meet  demand,  an  example 
illustrating use of Monte-Carlo Simulation in 
Airline Yield Management; this example also 
shows the use of a parameterized simulation 
model  to  arrive  at  good  decision  policies 
under uncertainty. 
It is very important to mention that a useful 
package  named  Risk  Solver  Engine  can  be 
used  to  develop  and  deploy  application  to 
users. 
Risk  Solver  Platform  came  with  a  set  of 
functions  which  can  be  used  for  defining 
optimization  models  and  Monte  Carlo 
simulation  models.  According  to  their 
documentation  most  of  these  functions  will 
be used for Monte Carlo simulation models, 
or  for  the  uncertain  elements  of  stochastic 
optimization models. 
We have tried to solve a problem of making 
reservation  to  a  hotel,  in  the  following 
conditions. Our hotels have 50 rooms, and the 
price for a room is 39 EURO. In case a client 
will  cancel  the  reservation,  hotel  will  not 
refund  the  price  of  the  first  night  of 
accommodation. In case the tourist will show 
up  and  no  room  will  be  available  a 
compensation of 25% of the price of a room 
will  be  paid  by  the  hotel  for  tourist 
accommodation in another hotel.  
In less than 5 minutes we can create a model 
of our problem based on the model which is 
defined  in  YieldManagementModel2.XLS 
file. The distribution of cancelation will be a 
LogNormal  distribution  of  5%  with  a 
standard deviation of 2%. For this  we used 
PSILogNormal  function  which  generates 
random  number  of  tourist  cancelations.  We 
prefer this model because we can see exactly 
the evolution of simulation for each particular 
case,  and  we  can  make  an  idea  about  the 
algoritm. 318 
 
 
 
For  number  of  booking  we  used 
=PsiSimParam(C19:BB19)  which  tell  to 
Solver engine that a different simulation will 
be made for each value of  a cell from the 
domain  C19  to  BB19.  We  also  used 
=PsiMean($C$15,C18)  do  define  the 
expected revenue in each case of simulations. 
We  also  set  the  number  of  trials  per 
simulation  to  10000  for  more  accuracy.  In 
less  than  3  seconds  on  our  computer  we 
obtain  the  results  to  our  problems  which 
indicate us to use 53 rooms in the process of 
making reservations. 
Another very useful thing is the displaying of 
the results which can be viewed and analyzed 
in a different window like the above on. We 
can  combine  multiple  simulations  in  our 
analyzing or we can see the results for only 
one. 
 
 
 
To solve this problem solver was simulate all 
50  cases    with  10000  of  situations  in  0.25 
seconds. 
 
The same problem can be solved if we define 
a  simulation  &optimization  model.  For  this 
model  we  used  only  PSILogNormal  and 319 
PSIMean functions as it can be seen in the following figure. 
   
 
We define an maximum objective of C16, we 
put a condition that number of booking has to 
be an integer (we cannot book 5.34 rooms), 
and we also set that C11 is a variable, like it 
can  be  seen  in  the  model  above  in  the 
optimization  section.  Another  parameters  of 
the engine is 1 simulation and 1000 of trials 
per  simulation.  We  start  with    button. 
Solver has found a solution in our case in 6.8 
seconds  after  informing  us  that  our  model 
contains  uncertainity,  and  Risk  solver 
platform  will  analyze  our  formula  to 
determine  the  best  methods  for  solving  it. 
Also  it  determine  that  our  models  is  non 
linear  in  decision  variables  and  cannot  be 
solved by stochastic programming or robust 
optimization methods. At the end inform us 
that Branch &Bound algorithm has found the 
best  of  the  locally  optimal  solutions.  The 
results are 53 bookings like in the preceding 
solutions. 
 
Another interesting part of Risk Solver Platform are Decision Tree. 
   

TOOLS USED IN DECISION MAKING

http://anale.steconomiceuoradea.ro/volume/2011/special/027.pdf

TOOLS USED IN DECISION MAKING

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