#### Event Title

Comparison of Optimization Techniques in Large-scale Transportation Problems

#### Location

CSU 255

#### Start Date

13-4-2004 10:30 AM

#### End Date

13-4-2004 12:15 PM

#### Student's Major

Computer Information Science

#### Student's College

Science, Engineering and Technology

#### Mentor's Name

Susan Schilling

#### Mentor's Department

Computer Information Science

#### Mentor's College

Science, Engineering and Technology

#### Description

The transportation problem is a classic Operations Research problem where the objective is to determine the schedule for transporting goods from source to destination in a way that minimizes the shipping cost while satisfying supply and demand constraints. Although it can be solved as a regular Linear Programming problem, other methods exist. Linear Programming makes use of the "Simplex Method," an algorithm invented to solve a linear program by progressing from one extreme point of the feasible polyhedron to an adjacent one. The algorithm contains tactics like pricing and pivoting. For a transportation problem, a simplified version of the regular Simplex Method can be used, known as the "Transportation Simplex Method." This paper will discuss the functionality of both of these algorithms, and compare their run-time and optimized values with a heuristic method called the "Genetic Algorithm." Genetic Algorithms, pioneered by John Holland, are algorithms that use mechanisms similar to those of natural evolution to encourage the survival of the best intermediate solutions. The objective of the study was to find out how these algorithms behave in terms of accuracy and speed when solving a large-scale problem.

Comparison of Optimization Techniques in Large-scale Transportation Problems

CSU 255

The transportation problem is a classic Operations Research problem where the objective is to determine the schedule for transporting goods from source to destination in a way that minimizes the shipping cost while satisfying supply and demand constraints. Although it can be solved as a regular Linear Programming problem, other methods exist. Linear Programming makes use of the "Simplex Method," an algorithm invented to solve a linear program by progressing from one extreme point of the feasible polyhedron to an adjacent one. The algorithm contains tactics like pricing and pivoting. For a transportation problem, a simplified version of the regular Simplex Method can be used, known as the "Transportation Simplex Method." This paper will discuss the functionality of both of these algorithms, and compare their run-time and optimized values with a heuristic method called the "Genetic Algorithm." Genetic Algorithms, pioneered by John Holland, are algorithms that use mechanisms similar to those of natural evolution to encourage the survival of the best intermediate solutions. The objective of the study was to find out how these algorithms behave in terms of accuracy and speed when solving a large-scale problem.