Resource Management Techniques [CSE Department]

Resource Management Techniques [CSE Department]

Resource Management Techniques

LINEAR PROGRAMMING

1. What Is Operations Research?
2. Operations Research Models
3. Solving The OR Model
4. Queuing and Simulation Models
5. Art of Modeling
6. More than Just Mathematics
7. Phases of an OR Study
8. Modeling with Linear Programming
9. Two-Variable LP Model
10. Graphical LP Solution: Solution of a Maximization Model
11. Graphical LP Solution: Solution of a Minimization Model
12. Selected LP Applications: Urban planning
13. Selected LP Applications: Currency Arbitrage
14. Selected LP Applications: Investment
15. Selected LP Applications: Production Planning and Inventory Control
16. Selected LP Applications: Blending and Refining
17. Selected LP Applications: Manpower Planning
18. Selected LP Applications: Additional Applications
19. Computer Solution With Solver and AMPL
20. The Simplex Method and Sensitivity Analysis
21. LP Model in Equation Form
22. Transition from Graphical to Algebraic Solution
23. The Simplex Method
24. Artificial Starting Solution: M-Method and Two-Phase Method
25. Special Cases in the Simplex Method
26. Graphical Sensitivity Analysis
27. Algebraic Sensitivity Analysis-Changes in the Right-Hand Side
28. Algebraic Sensitivity Analysis-objective Function
29. Sensitivity Analysis with TORA, Solver, and AMPL

DUALITY AND NETWORKS

1. Duality and Post-Optimal Analysis
2. Definition of the Dual Problem
3. Primal-Dual Relationships
4. Economic Interpretation of Duality
5. Additional Simplex Algorithms: Dual Simplex Method and Generalized Simplex Algorithm
6. Post-Optimal Analysis
7. Transportation Model and its Variants
8. Definition of the Transportation Model
9. Nontraditional Transportation Models
10. The Transportation Algorithm
11. The Assignment Model and The Hungarian Method
12. Transshipment Model
13. Network Models
14. Scope and Definition of Network Models
15. Minimal Spanning Tree Algorithm
16. Examples of the Shortest-Route Applications or Problem
17. Shortest-Route Algorithms
18. Linear Programming Formulation of the Shortest-Route Problem
19. Maximal flow model
20. CPM (Critical Path Method) and PERT (Program Evaluation and Review Technique)
21. CPM AND PERT: Network Representation
22. CPM AND PERT: Critical Path (CPM) Computations
23. CPM AND PERT: Construction of the Time Schedule
24. CPM AND PERT: linear Programming Formulation of CPM
25. CPM AND PERT: PERT Networks

INTEGER PROGRAMMING

1. Integer Linear Programming
2. Capital Budgeting- Integer Linear Programming Illustrative Applications
3. Set Covering Problem- Integer Linear Programming Illustrative Applications
4. Fixed Charge Problem- Integer Linear Programming Illustrative Applications
5. Either Or and If Then Constraints- Integer Linear Programming Illustrative Applications
6. Integer Programming Algorithms
7. Branch-and-Bound (B&B) Algorithm
8. Cutting-Plane Algorithm
9. Computational Considerations in ILP
10. Traveling Salesperson Problem (TSP)
11. Heuristic Algorithms: nearest neighbor and subtour reversal algorithms - Traveling Salesperson Problem (TSP)
12. B&B Solution Algorithm - Traveling Salesperson Problem (TSP)
13. Cutting Plane Algorithm - Traveling Salesperson Problem (TSP)
14. Deterministic Dynamic Programming
15. Recursive Nature of Computations in DP(Dynamic Programming)
16. Forward and Backward Recursion- Dynamic Programming
17. Selected Dynamic Programming(DP) Applications
18. Knapsack/Fly-Away/Cargo Loading Model- Dynamic Programming(DP) Applications
19. Work Force Size Model- Dynamic Programming(DP) Applications
20. Equipment Replacement Model- Dynamic Programming(DP) Applications
21. Investment Model- Dynamic Programming(DP) Applications
22. Problem of Dimensionality- Dynamic Programming

CLASSICAL OPTIMISATION THEORY

1. Classical Optimization Theory
2. Unconstrained Problems -Classical Optimization Theory
3. Necessary and Sufficient Conditions -Unconstrained Problems
4. Newton Raphson Method -Unconstrained Problems
5. Constrained Problems: Equality Constraints
6. Inequality Constraints-Karush-Kuhn-Tucker (KKT) Conditions

OBJECT SCHEDULING

Operations Research An Introduction by Hamdy A Taha

Chapter 1 : What Is Operations Research

1. What Is Operations Research?
2. Operations Research Models
3. Solving The OR Model
4. Queuing and Simulation Models
5. Art of Modeling
6. More than Just Mathematics
7. Phases of an OR Study

Chapter 2 : Modeling with Linear Programming

1. Modeling with Linear Programming
2. Two-Variable LP Model
3. Graphical LP Solution: Solution of a Maximization Model
4. Graphical LP Solution: Solution of a Minimization Model
5. Selected LP Applications: Urban planning
6. Selected LP Applications: Currency Arbitrage
7. Selected LP Applications: Investment
8. Selected LP Applications: Production Planning and Inventory Control
9. Selected LP Applications: Blending and Refining
10. Selected LP Applications: Manpower Planning
11. Selected LP Applications: Additional Applications
12. Computer Solution With Solver and AMPL

Chapter 3 : The Simplex Method and Sensitivity Analysis

1. The Simplex Method and Sensitivity Analysis
2. LP Model in Equation Form
3. Transition from Graphical to Algebraic Solution
4. The Simplex Method
5. Artificial Starting Solution: M-Method and Two-Phase Method
6. Special Cases in the Simplex Method
7. Graphical Sensitivity Analysis
8. Algebraic Sensitivity Analysis-Changes in the Right-Hand Side
9. Algebraic Sensitivity Analysis-objective Function
10. Sensitivity Analysis with TORA, Solver, and AMPL

Chapter 4 : Duality and Post Optimal Analysis

1. Duality and Post-Optimal Analysis
2. Definition of the Dual Problem
3. Primal-Dual Relationships
4. Economic Interpretation of Duality
5. Additional Simplex Algorithms: Dual Simplex Method and Generalized Simplex Algorithm
6. Post-Optimal Analysis

Chapter 5 : Transportation Model and Its Variants

1. Transportation Model and its Variants
2. Definition of the Transportation Model
3. Nontraditional Transportation Models
4. The Transportation Algorithm
5. The Assignment Model and The Hungarian Method
6. Transshipment Model

Chapter 6 : Network Models

1. Network Models
2. Scope and Definition of Network Models
3. Minimal Spanning Tree Algorithm
4. Examples of the Shortest-Route Applications or Problem
5. Shortest-Route Algorithms
6. Linear Programming Formulation of the Shortest-Route Problem
7. Maximal flow model
8. CPM (Critical Path Method) and PERT (Program Evaluation and Review Technique)
9. CPM AND PERT: Network Representation
10. CPM AND PERT: Critical Path (CPM) Computations
11. CPM AND PERT: Construction of the Time Schedule
12. CPM AND PERT: linear Programming Formulation of CPM
13. CPM AND PERT: PERT Networks

Chapter 9 : Integer Linear Programming

1. Integer Linear Programming
2. Capital Budgeting- Integer Linear Programming Illustrative Applications
3. Set Covering Problem- Integer Linear Programming Illustrative Applications
4. Fixed Charge Problem- Integer Linear Programming Illustrative Applications
5. Either Or and If Then Constraints- Integer Linear Programming Illustrative Applications
6. Integer Programming Algorithms
7. Branch-and-Bound (B&B) Algorithm
8. Cutting-Plane Algorithm
9. Computational Considerations in ILP
10. Traveling Salesperson Problem (TSP)
11. Heuristic Algorithms: nearest neighbor and subtour reversal algorithms - Traveling Salesperson Problem (TSP)
12. B&B Solution Algorithm - Traveling Salesperson Problem (TSP)
13. Cutting Plane Algorithm - Traveling Salesperson Problem (TSP)

Chapter 10 : Deterministic Dynamic Programming

1. Deterministic Dynamic Programming
2. Recursive Nature of Computations in DP(Dynamic Programming)
3. Forward and Backward Recursion- Dynamic Programming
4. Selected Dynamic Programming(DP) Applications
5. Knapsack/Fly-Away/Cargo Loading Model- Dynamic Programming(DP) Applications
6. Work Force Size Model- Dynamic Programming(DP) Applications
7. Equipment Replacement Model- Dynamic Programming(DP) Applications
8. Investment Model- Dynamic Programming(DP) Applications
9. Problem of Dimensionality- Dynamic Programming

Chapter 18 : Classical Optimization Theory

1. Classical Optimization Theory
2. Unconstrained Problems -Classical Optimization Theory
3. Necessary and Sufficient Conditions -Unconstrained Problems
4. Newton Raphson Method -Unconstrained Problems
5. Constrained Problems: Equality Constraints
6. Inequality Constraints-Karush-Kuhn-Tucker (KKT) Conditions