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Optimal Pivot Point Selection for the
Simplex Method
Jason Bradley
Spring 2012
In Partial Fulfillment of
Stat 4395-Senior Project
Department of Computer and Mathematical Sciences
Faculty Advisor:
Dr. Timothy Redl: _________________________________
Committee Member:
Dr. Vasilis G. Zafiris: _________________________________
Committee Member:
Dr. Erin M. Hodgess: _________________________________
Department Chairman:
Dr. Dennis Rodriguez _________________________________

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Table of Contents
I. Abstract 3
II. Introduction 4
III. History of the Simplex Method 5
IV. How to Perform the Simple Method 6
V. Fixing What Isn’t Broken 11
a. Identifying the Flaw 16
b. Creating a Solution 17
VI. Results 19
a. Minimization? 24
VII. Restrictions 26
VIII. Future Studies 28
IX. Conclusions 28
X. Bibliography 30

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Abstract
The objective of this project is to take the existing standard optimization model
and improve upon its structure by modifying its pivoting algorithm. While there are
many different methods for solving optimization problems, the most well-known and
most used algorithm is called the Simplex Method. The original method is over seventy
years old and has proven itself to be very robust. It has undergone only minor changes
over the course of its lifetime and currently relies on a rule modification that was
created in the 1970’s. This particular piece of the puzzle will be the focus of this project.
Changing the course of the algorithm will increase the overall efficiency and
effectiveness of the Simplex Method. If the hypothesis holds true, it has the ability to
have far reaching effects on many different levels of industry. This project is built upon
the amazing work of some brilliant mathematicians and is intended to add to previously
made discoveries. Innovation thrives on both original ideas and on the refinement of old
discoveries. This new algorithm falls into the later of those two categories.

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Introduction
Throughout industry and business, the optimal use of resources is not only
necessary for profitability but also necessary for the overall survival of a company.
Organizations commit large portions of manpower and funds toward improving the
decision making within a company. These choices are usually guided by specific
restrictions on manufacturing, manpower or other limitations. This type of optimization
often relies on the principles of Linear Programming (LP) to ensure the most efficient
use of limited resources. While this may sound like an economics topic, it actually falls
into the discipline of Operations Research (OR). It can also be referred to as Industrial
Engineering, Managerial Science, Mathematical Programming, Decision Science or host
of other monikers. In the field of Operations Research, choices are not made
haphazardly or based upon guesses. Instead, scenarios are laid out in a linear
mathematical model and the restrictions of that model are then analyzed in order to
find an optimal solution to the given scenario. Often, Linear Programming problems
arrive at solutions that are not obvious upon initial inspection. With larger problems,
more constraints and more variables, Linear Programming shows its true power.
Problems that would otherwise be nearly impossible to solve are done with ease by
following a simple, repetitive algorithm. Logistics, industry, business, manufacturing,
design, indeed any situation where resource management is crucial all benefit from this
modeling method.

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History of the Simplex Method
The first step in approaching this problem is to both study and give credit to the
works that it is built upon. The Simplex Method is one of the fundamental tools used in
Linear Programming. It was originally based on the input-output model developed
during the early 1940’s. This new method, created by George Dantzig, was officially
published in 1947 after being declassified by the Department of Defense. The
conceptualization of the simplex method was done without the relying on similar work
being developed in Russia by Leonid Kantorovich (1939). Dr. Dantzig designed this
approach while heading up the Air Force’s Statistical Control Combat Analysis Branch.
While working at the pentagon during World War II, Dantzig was tasked with improving
the logistics programming for the military. Keep in mind, programming as we now know
it today did not exist. The closest correlation to computer programming in today’s terms
was done by coding punch cards. Programming was more akin to planning and logistics.
After searching for methods to solve his input-output matrices, he decided to create his
own algorithm. Dantzig used linear inequalities and a set of rules that determined how
to manipulate those inequalities to arrive at an optimal solution to the problems with
which he was faced. The first real world problem that he tested the new algorithm on
was “Stigler’s Diet Problem” (Dantzig, 1990, pg. 43-47). It contained 77 variables and
only 9 constraints. “At the time, this was considered a very large problem.” It took 9
people 3 months to come to a solution using the newly developed Simplex Method.
Fortunately, with today’s computing power, the Diet Problem is considered relatively
small and can be solved very quickly. There is a great quote from Dr. Dantzig concerning

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the power of Simplex. He refers to an assignment problem concerning tasking 70 men to
70 different jobs. That’s 70! Different combinations! Dantzig says,
“Now 70! is a big number, greater than 10100. Suppose we had an IBM
370-168 available at the time of the big bang 15 billion years ago. Would
it have been able to look at all the 70! combinations by the year 1981?
No! Suppose instead it could examine 1 billion assignments per second?
The answer is still no. Even if the earth were filled with such computers
all working in parallel, the answer would still be no. If, however, there
were 1050 earths or 1044 suns all filled with nano-second speed computers
all programmed in parallel from the time of the big bang until sun grows
cold, then perhaps the answer is yes.” (Dantzig,1984, pg. 106)
With the creation of the Simplex Method, this assignment problem could be
transformed into a manageable and solvable scenario. This amazing mathematical
discover has not only shown the ability to stand the test of time, it is still the standard
for optimization, just as it was 65 years ago.
How to perform the Simplex Method
Is the problem maximization or minimization? This is the first question that
needs to be answered when constructing a Linear Programming problem. Following the
identification of the type of model, each constraint and the objective function must be
identified and set as a linear equation. In standard form, the constraints are represented
by “less than or equal to” inequalities. Each inequality is converted into an equation by
adding slack/surplus variables that take up the difference of the inequality. Also, it
should be noted that whether or not the variables are restricted by non-negativity
constraints. Once these elements have been identified, they are set up in a matrix
(tableau) form. This tableau has one row represented by each constraint and the

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objective (function) row is presented as the upper or lower row depending on the
preference of the user. The function row is set to zero and the negative values of the
original coefficients are inserted into the tableau. This finishes the initial set up of the LP
model. Only after these steps are completed can the algorithm begin.
Step 1. Maximize Z= c1 x1+c2 x2+... +cn xn
subject to: a11 x1+a12 x2+...+a1n xn <= b1
a21 x1+a22 x2+...+a2n xn <= b2
. . .
. . . .
. . . .
am1 x1+am2 x2+...+amn xn <= bm
and x1,x2,...xn >= 0
Step 2. Maximize Z= c1 x1+c2 x2+... +cn xn
subject to: a11 x1+a12 x2+...+a1n xn + s1 <= b1
a21 x1+a22 x2+...+a2n xn + s2 <=b2
. . .
. . . .
. . . .
am1 x1+am2 x2+...+amn xn + sm <= bm
and x1,x2,...xn >= 0
Step 3.
The first part of the algorithm identifies the “pivot element”. The current
standard for finding a pivot element is based off the work of Robert Bland (Bland, 1997).
This is accomplished in two steps. First, the pivot column must be identified by locating
x1 x2 … xn s1 s2 sm Z
- c1 - c2 … - cn 0 0 0 0
a11 a12 … a1n 1 0 0 b1
a21 a22 … a2n 0 1 0 b2
. . .
. . . .
. . . .
am1 am2 … amn 0 0 1 bm

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the most negative coefficient in the function row. If there is a tie, the upper left most
(North West) value is chosen. Once the column is identified, the pivot element has to be
determined. This is accomplished by a process called a minimum ratio test. The right
hand side of each constraint is divided by its corresponding pivot column element of the
pivot column. The lowest, non-negative, value becomes the pivot element. The row
associated with the pivot element is divided by the value of that element. After scaling
down the pivot row, the other elements in that column are eliminated using Gauss-
Jordan. The resulting Z-value is the amount of increase in the overall function. This
process is repeated until there are no remaining negative values in the function row.
The final Z-value represents an optimal solution to the linear problem. There may exist
none, one or many optimal solutions to a Linear Programming problem depending on
the area defined by the constraints.
On a side note, if the only eligible pivot column contains only negative values, it
is an indication that the feasible region is unbounded and there is no optimal feasible
solution to the problem.
Also, for the sake of this project, each problem is set up in standard form.

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This is a flowchart that reflects the decision making for the Simplex Method:
Here is an example of the current pivot method of the Simplex Method:
Iteration 1: Identify the Pivot Column (-5 is the most negative obj. row coefficient)
x1 x2 s1 Z
z-row -3 -5 0 0
s1 .5 1 1 3

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Conduct Minimum Ration Test (MRT) on selected column (3/1=3, min non-neg)
x1 x2 s1 Z
z-row -3 -5 0 0
x2 .5 1 1 3
Pivot the Tableau (Gauss-Jordan, Pivot element to 1 and zero out other rows)
x1 x2 s1 Z
z-row -.5 0 5 15
'x2 .5 1 1 3
Iteration 2: Identify the Pivot Column (-.5 is the most negative obj. row coefficient)
x1 x2 s1 Z
z-row -.5 0 5 15
x2 .5 1 1 3
Conduct Minimum Ration Test (MRT) on selected column (3/.5=6, min non-neg)
x1 x2 s1 Z
z-row -.5 0 5 15
x2 .5 1 1 3
Pivot the Tableau (Gauss-Jordan, Pivot element to 1 and zero out other rows)
x1 x2 s1 Z
z-row 0 1 1 18
x1 1 2 2 6
Iteration 3: Identify the Pivot Column (All columns have Positive coefficients in z-row)
With no negative coefficients in the z-row, maximization is complete.

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Sol: Objective Function Z=18
Path Traveled (0,0), (0,3), (6,0)
Iterations = 3
Distance traveled = 9.71
As you can see, the algorithm works very well. It traverses the corner points
(Feasible Solutions) of the feasible region until if finds an acceptable solution to the
problem. The results tell the story of how the algorithm progressed and what the final
outcome was. For this LP, the algorithm travelled from origin (0,0) to point (0,3) where it
arrived at a value of 15. From there it travelled to point (6,0) where it reached its
optimal of Z = 18. The total unit distance traveled was 9.71 and it found a solution on
the third iteration.
While this seems straight forward, please remember that as Linear Programming
goes, this problem is about as simple as it gets. It is a 2-dimensional problem with only
one constraint. Real world LP’s are far larger for both constraints and variables
(dimensions). Accordingly, the complexity of the method increases when these
parameters are larger.
Fixing what isn’t broken
While the Simplex Method is large part of the foundation of Operations

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Research, it is not without its issues. It has been shown that under certain scenarios that
a problem called cycling occurs. Cycling happens when the values associated with a
linear optimization problem fail to converge on an optimal solution. In other words; the
objective value does not improve after completing an iteration. This usually occurs when
one or more constraints occupy the same basic feasible solution or corner point. With
the most extreme examples of cycling, the algorithm will never find a solution and will
get stuck in an infinite loop.
In order to address this issue of cycling, Robert Bland crafted new guidelines for
determining the process that the simplex method should follow. His changes are specific
to the pivot point selection. Those changes are now the standard for the Simplex
Method. His changes to the basic algorithm curtail cycling but do not eliminate it. It can
be demonstrated that, under certain conditions, cycling still occurs while using his
algorithm. Part of the goal of this project is to eliminate cycling, especially infinite
cycling. This is one of the fundamental weak points with the algorithm.
Infinite Cycle:
x1 x2 x3 x4 s1 s2 s3 Z
z-row -3/4 150 -1/50 6 0 0 0 0
s1 ¼ -60 -1/25 9 1 0 0 0
s2 ½ -90 -1/50 3 0 1 0 0
s3 0 0 1 0 0 0 1 1
x1 x2 x3 x4 s1 s2 s3 Z
z-row 0 -30 -7/50 33 3 0 0 0
x1 1 -240 -4/25 36 4 0 0 0
s2 0 30 3/50 -15 -2 1 0 0
s3 0 0 1 0 0 0 1 1

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x1 x2 x3 x4 s1 s2 s3 Z
z-row 0 0 -2/25 18 1 1 0 0
x1 1 0 8/25 -84 -12 8 0 0
x2 0 1 1/500 -1/2 -1/15 1/30 0 0
s3 0 0 1 0 0 0 1 1
x1 x2 x3 x4 s1 s2 s3 Z
z-row ¼ 0 0 -3 -2 3 0 0
x3 25/8 0 1 -525/2 -75/2 25 0 0
x2 -1/160 1 0 1/40 1/120 -1/60 0 0
s3 -25/8 0 0 525/2 75/2 -25 1 1
x1 x2 x3 x4 s1 s2 s3 Z
z-row -1/2 120 0 0 -1 1 0 0
x3 -125/2 10500 1 0 50 -150 0 0
x4 -1/4 40 0 1 1/3 -2/3 0 0
s3 125/2 -10500 0 0 -50 150 1 1
x1 x2 x3 x4 s1 s2 s3 Z
z-row -7/4 330 1/50 0 0 -2 0 0
s1 -5/4 210 1/50 0 1 -3 0 0
x4 1/6 -30 -1/150 1 0 1/3 0 0
s3 0 0 1 0 0 0 1 1
x1 x2 x3 x4 s1 s2 s3 Z
z-row -3/4 150 -1/50 6 0 0 0 0
s1 ¼ -60 -1/25 9 1 0 0 0
s2 ½ -90 -1/50 3 0 1 0 0
s3 0 0 1 0 0 0 1 1
This is the same place we started. We have entered an infinite cycle.
Another issue within the Simplex Method is the efficiency of the path it takes to
acquire an optimal solution. Assuming that the cycling dilemma is not an issue, Simplex
will absolutely arrive at an optimal solution if it exists. However, the direction it chooses

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to take in order to arrive at that optimal solution is not always… optimal. This is
particularly true in concave linear models. Simplex does not account for the overall
increase in the objective function when choosing a pivot. The algorithm simply picks the
column with the most negative coefficient with complete disregard for the per unit
increase along that vector. Due to that shortcoming, Simplex may actually take a longer
route than necessary to arrive at its solution.
It has been common practice that the efficiency of this method be measured in
iterations of the algorithm. This way of measuring can be misleading. For instance, if we
take our previous problem with the triangular feasible region, we could add additional
constraints to the one of the extreme corners and thereby increase the number of
iterations in that area. This creates the illusion that more was done while in reality, the
unit distance may not have increased much due to the close proximity of corner points
to one another.
To address this, we can use the unit distance for the path from our starting
solution to the optimal solution to measure the efficiency of the algorithm. By
measuring by distance instead of iterations, we are able to avoid situations where
“loading a corner” affects the overall efficiency of the algorithm.
For clarification, “loading a corner” is referring to the respective number of
vertices located in one particular corner of the feasible region. Since the Simplex
method iterates based on the number of vertices between its origin and its final
destination, loading up a corner with multiple vertices will increase the number of
iterations if Simplex decides to travel in that particular direction. If we instead use the

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unit distance travelled to measure the overall efficiency, we arrive at a measurement
based upon the distance of the path traveled to arrive at the optimal solution. This
accounts for the shorter distances between tightly packed vertices. This distance
measurement is a far better gauge of efficiency because, unlike the iterations
measurement, it cannot be artificially skewed. Thus, you get a more reliable means of
determining how optimal the process is.
This LP is loaded up near the
extreme feasible solution on
the y-axis.
Work on these issues is a promising venture due to the proven effectiveness of
the Simplex Method. If this already powerful algorithm can be improved in any
significant way, it becomes an even more valuable tool. Small changes to existing work
can often have far reaching implications. The cycling issues and the paths chosen by the
Simplex Method present a unique opportunity to reexamine a masterful piece of work.
If a more efficient (distance) method that does a better job avoiding cycling can be
gleaned from tinkering with Dr. Dantzig’s masterpiece, I would be proud to be a small
part of it.
“Linear programming can be viewed as part of a great
revolutionary development which has given mankind
the ability to state general goals and to lay out a path
of detailed decisions to take in order to ``best’’ achieve its

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goals when faced with practical situations of great complexity.”
(Dantzig, 1991)
Identifying the Flaw
Vs.
Originally, my desire was to decrease the overall number of iterations of the
Simplex Method because it seemed as though they were an accurate measure of
effectiveness. Only after changing the algorithm and being happy with the results, I
discovered that I could indeed alter the constraints to prove my algorithm wrong.
Disappointed, but not deterred, I sought to understand what changes would actually
constitute an improvement in the process.
The realization that the number of pivots required to find the optimal does not
correlate to the actual distance from the starting point of the feasible region was the
inspiration I needed to try and find a better model. So the focus changed from iterations
to distance, which gave a more consistent result. The new algorithm would need to take
into account the actual distance between the current point and the distance(s) to the

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other feasible solutions. The thought seemed very close to graph theory and I found it
curious why the concept would not apply to optimization the same way it applies to
directed graphs.
Creating a Solution
From there designing the basic algorithm seemed simple; check the distance
between two or more points, scale that distance by the unit value of that vector, choose
the path with the most negative value. Fortunately, the backbone of the Simplex
Method could stay intact and the only changes would be made to the pivot
identification portion of the algorithm. After inspection, I decided to keep the first part
of the rule concerning column identification. Each column’s z row value still represented
the coefficient associated with original objective equation. Also, the minimum ratio test
represents the unit value along each vector. With those two elements already identified
in the standard Simplex algorithm, using them for the modified algorithm seemed like a
natural fit.
All of the parts needed for the modified algorithm were already there. The next
step was to fit those pieces to the idea for the new algorithm. The initialization is the
same as the original method; convert the problem into a system and convert the system
into a tableau. Next, where Simplex identifies only the most negative z-row value, the
new algorithm requires the identification of all eligible columns. In other words, identify
all columns that the z-row coefficient is less than zero. Once those columns were
identified, perform the minimum ratio test on each eligible column. Following that test,

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choose the lowest, non-negative, value in each eligible column. Multiply that value by
the corresponding objective row coefficient. Whichever value is most negative is used to
identify the pivot column and the pivot element. Once that process is complete; pivot
the tableau in the same manner as the original Simplex algorithm. Repeat the algorithm
until there are no negatives in the objective row or until the only eligible columns have
all negative values in their constraint rows (this would violate the minimum ratio test
requirements). The resulting z-row value will be the optimal value of the linear
programming problem.
This is the modified Algorithm:

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Results
In order to test the results I needed a large number of sample linear
programming problems. The set of LPs needed to include unremarkable maximization
and minimization problems. The new method was evaluated with 2nd, 3rd and nth degree
LP’s in order to identify the limits of the algorithm. Also, I tested examples where the
Simplex Method showed temporary stalling and infinite cycling. After verifying that the
Simplex method takes a longer path than necessary or that the algorithm cycles, the
same LP needed to be tested in the modified algorithm.
Simulated real world example:
The first example is a maximization problem. In our scenario, a fledgling
computer retailer wants to begin building its own personal computers. The retailer
offers two models. The first is a base level pc. It takes 3 hours to install the mother
board and associated hardware, 3 hours to install the operating system; it does not
require any updating following the software installation. For the more advanced
computer, the build requires 3 hours to install the board and hardware, 10 hours to
install the OS and additional software and 2 hours to update the system. Due to the
other requirements placed on the employees, the company can only dedicate as much
as 60 man hours a week for hardware instillation, 70 hours on operating systems, and
no more than 12 hours on updates. The profit for each computer is $300.00 on the basic
model and a profit of $500.00 on the advanced model. Due to having lower prices than
their competitors, demand is high and the company cannot keep enough computers on

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the shelves. The owner of the computer company wants to know about how many of
each machine he should dedicate his employees to build per week in order to maximize
his profit.
This is a fairly simple maximization word problem. Our first step is to identify the
objective function and the constraints.
Objective Function: # Comp 1 x Price Comp 1 + # Comp 2 x Price Comp 2 = Profit
Constraints: Hardware hours for Comp 1 + hours for Comp 2 <= 60 hours
Oper System hours for Comp 1 + hours for Comp 2 <= 70 hours
Updates hours for Comp 1 + hours for Comp 2 < = 12 hours
So after identifying the objective function and the constraints it is time to
convert the information into a system.
Maximize Z = 300 x1 + 500 x2
subject to: 3 x1 + 3 x2 <= 60
3 x1 + 10 x2 <= 70
0 x1 + 2 x2 <= 12
and x1, x2 >= 0
Next, we convert the system into a tableau and add slack variables.
x1 x2 s1 s2 s3 Z
-300 -500 0 0 0 0
3 10 1 0 0 70
3 3 0 1 0 60
0 2 0 0 1 12

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Each pivot point is highlighted
Here are the results represented by the tableau

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So our company owner would choose to build roughly 19 of computer 1 and only one of
computer 2, resulting in a profit of about $6300.00.
If you notice, the path to the optimal point could have taken a shorter route. If it
did take the alternate path, not only would the distance travelled be less but it would
have used less iterations as well.
So the next step was to test the modified algorithm.
Each pivot is highlighted

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Upon inspection, it is easy to see that the solutions were the same yet the new
algorithm took a much shorter route and in this situation, less iterations. If you
compare the two models, the path to the optimal solution using the Simplex Method
traveled a unit distance of 25.24 compared to the modified method whose path was a
unit distance of 22.02. The modified method outperformed the standard Simplex
algorithm by a unit value of 3.22.
The comparison, while compelling, is not enough to claim an improvement over
the traditional model. The next step was to address problems where Simplex cycles. This
was the infinite cycle we looked at earlier:
x1 x2 x3 x4 s1 s2 s3 Z
z-row -3/4 150 -1/50 6 0 0 0 0
s1 ¼ -60 -1/25 9 1 0 0 0
s2 ½ -90 -1/50 3 0 0 0 0
s3 0 0 1 0 0 0 1 1
In this LP, the Simplex Method cycles forever. However, if you run the same LP
through the modified algorithm, cycling does not occur. Surprisingly, a solution presents

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itself after only three iteration of the new method. This result by itself demonstrates
one of the limitations of the Simplex Method and the potential promise of the new pivot
identification rules. These are the results:
Minimization?
The modified algorithm also works on minimization problems. However, it does
require some work on the front end. Since the minimization standard form is not the
same as the maximization standard form, it has to be converted. The system is

25. 25
converted using the Theory of Duality discovered by John Von Neumann. The theory
suggests that the optimal a minimization problem (primal) is the same as the optimal of
dual of that system. Once the system is converted into the dual and put into standard
form, it can then be solved using the modified algorithm. The final value will be optimal
for both systems. Also, the slack values in the final tableau represent the basic solution
to the minimization problem.
Example of a minimization problem using the modified method to solve the dual:

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The values of the slack variables correspond to the solution to the original minimization
problem.
Restrictions
While the modified algorithm will work with any maximization system in
standard form, there are some restrictions that need to be addressed. The first is
temporary degeneracy or temporary cycling. Like Simplex, it can be shown that the
modified algorithm can still be made to cycle temporarily. The other issue is a limitation
on the distance claim. The modified algorithm can guarantee shortest distance only on
cases where the constraints have a negative slope including zero and undefined. While
both of these issues are substantial, they do not take away from the overall goal of the
algorithm.
The temporary degeneracy is a particularly frustrating problem. Stalling or
temporary cycling, as mentioned before, happens when more than one constraint
crosses a feasible solution on the path to the optimal solution. I believe that like the
selection of the pivot element, it can be addressed by simply designing an algorithm to

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affect order of the constraints. Since the North West Method or lowest index method is
the default choice for the next column or row, it too should be evaluated for
improvement. However, that’s a topic for another paper.
The more serious restriction is that of the negative slope issue. Because the new
algorithm detects the distance between feasible solutions, it cannot predict the
following point until it becomes the focus. This means that if the first constraint was
along the y-axis and the second constraint had a steep positive slope that intersected
the y-axis, the algorithm must evaluate that intersection point first before moving on.
Since, with two dimensional scenarios, the first to vectors to be evaluate intersect the
origin it can be misleading about the upper limits of each axis. Under negative slope
conditions, this problem is nonexistent due to the accurate representation of the upper
bounds of each axis.
Both of these issues are significant enough to warrant further examination. The
good news is; both of these problems already exist in the standard Simplex Method. The
stalling problem is not only prevalent in the Simplex Method; it is easy to create a
scenario where a linear problem cycles infinitely instead of just stalling. It is only when
you compare the Simplex algorithm with the modified algorithm that you realize the
blindness associated with identifying the upper bounds along each base vector. Still, it is
a solid claim that under the negative sloped contraint conditions, the modified
algorithm will reach the optimal solution in a distance less than or equal to the Simplex
Method.

28. 28
Future Studies
There are several unanswered questions concerning the benefits of this modified
algorithm. One of the next steps is obviously the mathematical proof for the new
method. Also, the problem of stalling is still a nuisance that requires some attention.
Before either of those two concerns can be addressed, I believe that more sample LP’s
need to be evaluated. If the modification survives that further evaluation, then I believe
it would be a very worthwhile topic for further work.
Conclusion
My goal at the start of this project was to modify the algorithm behind the
Simplex Method so that it would avoid unnecessary iterations. After initially believing
that I had somehow stumbled upon a simple way to accomplish this, I found ways to
prove that solution wrong. This was frustrating but I was still convinced that there had
to be a way to improve the algorithm. After reexamining the problem and the ways I
found to disprove my original attempt, I realized that strictly using iterations as a
measure of efficiency was the wrong metric for this project. Distance of the path
traveled seemed a far better measurement of success or failure. Once I changed the
focus to improving the distance traveled, the algorithm came together. The next
objective was testing. I decided that I needed to design a program that not only
computed each tableau but that could also display it in a meaningful manner. Through
sheer luck or happenstance I was introduced to Mathematica. This piece of software
had not only a high-level programming language but also several powerful graphics

29. 29
functions. After designing code to run the original Simplex Method, I then went back
and modified that code to run the new algorithm as well. Once I verified that the new
algorithm provided reliably correct results, I began testing it with problems that Simplex
struggled with or failed to solve all together. The results were compelling. The modified
algorithm not only had the ability to identify the shortest path, it could also solve LP’s
where the Simplex Method would cycle indefinitely. The new method does come with
some caveats though. It does not guarantee the shortest path if there are constraints
with a positive slope and it does not solve temporary stalling. Also, there is a trade-off
with some additional simple calculations on the front end of the algorithm in exchange
for no infinite cycling. The changes made to this algorithm have the potential to affect a
wide range of industries, especially economics. I still have a long way to go before being
able to prove this concept mathematically but that is the next logical step for this
project. Also, finding a way to minimize or prevent temporary degeneracy (stalling)
seems to be a natural next step. Often, we do not feel the need to fix something that is
not broken. We simply become satisfied that the problem has been solved and we move
on to the next. However, when we do decide to apply new methods and technologies to
old solutions, we often end up with tools that are amplified and more effective. Given
my relative newness to the field of Operations Research, my intuition says that this must
have been tried before. That being said, the results of this project speak for themselves.
If it has been done before, I do not know why it is not the standard.

30. 30
Bibliography
Dantzig, GB (1984). Reminiscences about the origin of linear programming. In
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