Transportation, Assignment and Advanced Programming
Aspen University: MGT520
November 30, 2014
Abstract
The options selected for the assignment are numbers 1, 2, 4, 6, 7 and 8. Self-tests are answered in tables with incorrect answers in red. Excel tables are attached showing the work of the math problems.
1. Self-tests
Chapter 10
1. B
2. C
3. B
4. B
5. B
6. A
7. B
8. B
9. B
10. A
11. B
12. A
Chapter 11
1. A
2. B
3. A
4. A
5. A
6. B
7. B
8. B
9. D
10. B
11. E
2. Dummy Sources and Dummy Destinations
Why have dummy sources and dummy destinations? Dummy sources and dummy destinations are used to balance problems which are unbalanced, meaning that either demand or supply is greater than the other value. Balancing the problem makes it possible to still find optimal solutions by assigning a shipping cost of zero to the dummy value and using the value to calculate excess demand or capacity within the dummy.
4. Balanced Transportation Problem
What is a balanced transportation problem? Describe the approach you would use to solve an unbalanced problem. A balanced transportation problem is one in which total supply is equal to total demand. As this is unlikely to occur, there must be a way to artificially balance a problem to allow for finding an optimal solution. To solve an unbalanced problem, a dummy source or dummy destination must be added to create the balance. When demand is less than supply, a dummy column should be added to represent a warehouse where the excess supply will be sent. If the supply is less than demand, a dummy row is added to represent a factory where additional supply is produced. The shipping cost are always zero for dummy sources and destinations. This allows for finding an optimal solution to an unbalanced problem.
6. Goal Ranking
What does it mean to rank goals in goal programming? How does this affect the problem's solution? The concept of ranking goals within a goal programming problem allows for specifying which goad hold the highest importance. Without goal ranking, all goals would be considered equally important and the model would attempt to achieve all goals equally.
By ranking goals with priority levels, the problem will treat the highest ranking goal as infinitely more important than the second and treat the second goal as infinitely more important than the next. (Render, Stair, & Hanna, 2009) This prioritization of goals continues and the optimal solution attempts to fulfill all goals based on importance.
By weighting goals within a particular priority, it is
