WGU D605 Task 3 Performance
Assessment Report
Amazon Air Fleet Optimization:
Mixed Integer Linear Programming Solution
Student Name: Shanikwa Haynes
Course: D605 - Optimization
Date: July 18, 2025
, Executive Summary
This report presents a comprehensive Mixed Integer Linear Programming (MILP) solution
for optimizing Amazon Air fleet operations. The solution implements the mathematical
model developed in Task 2 using Python and the PuLP optimization library (Mitchell et al.,
2023). The system successfully minimizes total operational costs while satisfying all
operational constraints, demonstrating the practical application of mathematical
optimization techniques in real-world logistics scenarios (Hillier & Lieberman, 2021).
The optimization solution achieves a total operational cost of $112,060.00, with 80% fleet
utilization and 100% demand satisfaction. All constraints are satisfied, validating the
correctness of both the mathematical formulation and the computational implementation.
The COIN-OR CBC solver (Forrest & Lougee-Heimer, 2005) provides optimal solutions
within acceptable computational time limits.
Table of Contents
1. Introduction
2. A. Optimization Program Implementation
3. B. Solution Analysis
4. C. Development Process Reflection
5. D. References
6. Appendices
7. Conclusion
1. Introduction
The Amazon Air Fleet Optimization problem requires the efficient allocation of aircraft to
routes while minimizing operational costs and satisfying customer demand. This report
documents the computational implementation of the Mixed Integer Linear Programming
(MILP) model developed in Task 2, demonstrating how mathematical optimization
techniques can be applied to solve complex logistics problems.
The solution is implemented in Python using the PuLP optimization library (Mitchell et al.,
2023), providing a robust and scalable framework for fleet optimization. Operations
research methodologies (Winston, 2022) guide the systematic approach to problem
formulation, solution implementation, and result interpretation. The system includes
comprehensive analysis tools, visualization capabilities, and constraint verification
mechanisms to ensure solution quality and practical applicability.
Assessment Report
Amazon Air Fleet Optimization:
Mixed Integer Linear Programming Solution
Student Name: Shanikwa Haynes
Course: D605 - Optimization
Date: July 18, 2025
, Executive Summary
This report presents a comprehensive Mixed Integer Linear Programming (MILP) solution
for optimizing Amazon Air fleet operations. The solution implements the mathematical
model developed in Task 2 using Python and the PuLP optimization library (Mitchell et al.,
2023). The system successfully minimizes total operational costs while satisfying all
operational constraints, demonstrating the practical application of mathematical
optimization techniques in real-world logistics scenarios (Hillier & Lieberman, 2021).
The optimization solution achieves a total operational cost of $112,060.00, with 80% fleet
utilization and 100% demand satisfaction. All constraints are satisfied, validating the
correctness of both the mathematical formulation and the computational implementation.
The COIN-OR CBC solver (Forrest & Lougee-Heimer, 2005) provides optimal solutions
within acceptable computational time limits.
Table of Contents
1. Introduction
2. A. Optimization Program Implementation
3. B. Solution Analysis
4. C. Development Process Reflection
5. D. References
6. Appendices
7. Conclusion
1. Introduction
The Amazon Air Fleet Optimization problem requires the efficient allocation of aircraft to
routes while minimizing operational costs and satisfying customer demand. This report
documents the computational implementation of the Mixed Integer Linear Programming
(MILP) model developed in Task 2, demonstrating how mathematical optimization
techniques can be applied to solve complex logistics problems.
The solution is implemented in Python using the PuLP optimization library (Mitchell et al.,
2023), providing a robust and scalable framework for fleet optimization. Operations
research methodologies (Winston, 2022) guide the systematic approach to problem
formulation, solution implementation, and result interpretation. The system includes
comprehensive analysis tools, visualization capabilities, and constraint verification
mechanisms to ensure solution quality and practical applicability.
