
For some problems it useful and sometimes necessary to identify
specific combinations of the random variables that satisfy logical
conditions. For example, say we are uncertain about the righthandside
constants for the constraints of a linear programming model, so
we assume that each constant is a random variable. Using the expected
values of the random variables, we solve the LP model to obtain
"optimum" values of the decision variables. We enclose
the word optimum in quotes because the solution is only optimum
for the expected values. It is probably not optimum for other
realizations of the random variables. In fact, the solution is
probably not feasible for some realizations. We want to use probability
analysis to find the probability that the solution is feasible
and also the expected value for the objective function over the
solutions that are feasible. Feasibility is a logical state taking
the values TRUE and FALSE. A solution is feasible (TRUE) if all
the constraints are simultaneously satisfied. Otherwise the solution
is not feasible (FALSE).
The addin provides for logical conditions by including a cell
for logical expressions along with each cell for function evaluations.
On this page we illustrate this feature using the builtin function
examples. We will return to the LP situation described above
on another page.
To add logical expressions, we click the Include Feasibility
box on the dialog. We use the term feasibility here
to represent any logical expression because the concept of feasibility
is so common for Operations Research. 



The form shown below is the same as
that considered earlier, but two new rows are added called Fun.
Feas. (Function Feasible) and Prob. Feas. (Probability
Feasible). The problem has already been solved on this form. 



The function feasible
row (row 16 for the example) holds logical expressions that
can refer to the random variable values or other cells on the
worksheet. Each logical expression returns either TRUE or FALSE.
The examples shown above all relate to the values of the random
variables. These are the builtin examples obtained when the
Example Function button is clicked. Logical expressions
for Excel may have a variety of forms as illustrated by the
example. The three expressions used here have the mathematical
descriptions shown below.
Note that the second and third conditions include the previous
conditions, so the number of solutions that are feasible (the
logical expression evaluates to TRUE) decreases with the function
index. 
Enumeration 

When we choose the Moments
command from the menu, the entire sample space of the random variables
is enumerated. For every combination, the feasibility and function
value for each function is evaluated. These values are combined
to obtain the results in the green fields at the bottom of the
form. Row 17 shows the probabilities that the logical expressions
are satisfied. Row 19 shows the mean values of the functions for
those solutions that satisfy the logical expressions. Row 20 shows
the variances of the function values for those solutions that
satisfy the logical expressions. 



The formulas for calculating
these values are below.
When we enumerate all possible values of the random variables,
as for the example, these results are exact. 
Simulation 

The same problem can be addressed
with Monte Carlo simulation. The figure below shows the form constructed
for simulation with the results determined by a sample of 1000
observations. 



The simulation results are reported
in rows 12 through 18. The probability of feasibility is estimated
as the proportion of the simulation observations that satisfy
the feasibility conditions. Since the logical conditions for
each function are different, these probabilities are all different.
The moment values in rows 14 and 15 are computed only for feasible
combinations. Since not all the combinations are feasible, the
sample sizes used for the moment estimates are less than the
number of iterations (1000). The reduced sample sizes generally
increase the confidence intervals.
The results are obtained with the following formulas. Only
feasible observations are used when computing moments.


The provision for logical functions
described on this page will be useful in many contexts. Examples
are given on later pages of this section. 

