Useful Links
Engineering
Industrial Engineering
Operations Research and Optimization
1. Introduction to Operations Research
2. Mathematical Foundations for Optimization
3. Linear Programming
4. Network Optimization
5. Integer Programming
6. Nonlinear Programming
7. Dynamic Programming
8. Stochastic Processes and Queuing Theory
9. Simulation Modeling
10. Decision Analysis
11. Heuristics and Metaheuristics
12. Advanced Optimization Topics
Linear Programming
Fundamental Concepts
Decision Variables
Variable Definition
Variable Types
Variable Bounds
Objective Function
Maximization Problems
Minimization Problems
Linear Objective Functions
Constraints
Equality Constraints
Inequality Constraints
Non-negativity Constraints
Constraint Types
Feasible Region
Feasible Solutions
Bounded Regions
Unbounded Regions
Empty Feasible Regions
Optimal Solutions
Existence of Optimal Solutions
Uniqueness of Optimal Solutions
Multiple Optimal Solutions
Corner Point Solutions
Linear Programming Assumptions
Proportionality Assumption
Additivity Assumption
Divisibility Assumption
Certainty Assumption
Linearity Assumption
Implications of Violations
Model Formulation
Problem Translation Process
Resource Allocation Problems
Production Planning
Capital Budgeting
Workforce Allocation
Blending Problems
Diet Problems
Product Mix Problems
Chemical Blending
Transportation Problems
Supply and Demand Constraints
Cost Minimization
Balanced Transportation
Assignment Problems
One-to-One Assignments
Cost Minimization
Hungarian Method Setup
Network Flow Problems
Node Balance Constraints
Arc Capacity Constraints
Graphical Solution Method
Two-Variable Problems
Plotting Constraints
Boundary Lines
Feasible Half-Planes
Identifying Feasible Region
Intersection of Half-Planes
Corner Points
Objective Function Lines
Iso-profit Lines
Iso-cost Lines
Locating Optimal Solutions
Corner Point Theorem
Optimal Point Identification
The Simplex Method
Standard Form Conversion
Slack Variables
Surplus Variables
Artificial Variables
Non-negativity Requirements
Initial Basic Feasible Solution
Basic Variables
Non-basic Variables
Basic Feasible Solutions
Simplex Tableau Construction
Tableau Format
Coefficient Matrix
Right-Hand Side Vector
Objective Row
Simplex Algorithm Steps
Optimality Test
Entering Variable Selection
Leaving Variable Selection
Pivot Operations
Tableau Updates
Special Cases in Simplex
Unbounded Solutions
Infeasible Solutions
Alternative Optimal Solutions
Degeneracy
Cycling Prevention
Two-Phase Method
Phase I Objective
Artificial Variable Elimination
Phase II Optimization
Transition Between Phases
Big M Method
Large Penalty Parameter
Artificial Variable Treatment
Solution Interpretation
Duality Theory
Primal Problem Formulation
Dual Problem Construction
Dual Variables
Dual Constraints
Dual Objective Function
Primal-Dual Relationships
Weak Duality Theorem
Strong Duality Theorem
Complementary Slackness
Economic Interpretation
Shadow Prices
Marginal Values
Resource Valuation
Dual Simplex Method
Dual Feasibility
Primal Infeasibility
Algorithm Steps
Sensitivity Analysis
Objective Function Coefficient Changes
Allowable Ranges
Optimality Preservation
Right-Hand Side Changes
Shadow Price Interpretation
Feasibility Ranges
Dual Price Validity
Constraint Coefficient Changes
Structural Changes
Feasibility Impact
Adding New Variables
Reduced Cost Analysis
Profitability Assessment
Adding New Constraints
Constraint Violation Check
Dual Variable Introduction
Parametric Programming
Systematic Parameter Changes
Solution Path Analysis
Previous
2. Mathematical Foundations for Optimization
Go to top
Next
4. Network Optimization