Operations Research

Name: Operations Research
Rating: 4.0 (38 reviews)

For Engineers, Data analysts, and Business Leaders

Created byDr. Rauf Tailony

Last updated 3/2020

English

English [Auto],

What you'll learn

Linear programming
Mathematical Modeling
Business Cost Optimization
Methods for Maximizing Profit and Minimizing Cost
Cost Sensitivity Analysis
Network and Transportation optimization

Course content

4 sections • 13 lectures • 2h 41m total length

Introduction to Model Building15:18
Learn to build operations research optimization models for real systems like production lines, defining objective functions, decision variables, and constraints to maximize profit and quality.
Model Types15:01
Explore how operations research uses static and dynamic, linear and non-linear, integer and non-integer, and deterministic and stochastic models, and follow seven steps to build effective mathematical models.
Introduction to Linear Programming7:20
Learn the basics of linear programming (LP) as a tool for maximizing or minimizing linear objective functions under linear constraints, with the simplex method and real-world applications across industries.

A linear Programming Problem20:39
Formulate and solve a linear programming model to maximize weekly profit from soldiers and trains, using x1 and x2 with finishing, carpentry, and demand constraints.
Feasible Region and Optimal Solution [Graphical Solution]9:38
Explore the graphical solution to linear programming by defining the feasible region under constraints, visualizing X1 and X2 in the Cartesian plane, and identifying the optimal solution.
Maximize/Minimize Feasible region9:14
Identify the feasible region from x1 ≤ 4, 2x2 ≤ 12, and 3x1+2x2 ≤ 18, then maximize Z = 3x1+5x2 by graphically lifting lines to touch at the optimal point.
Graphical linear programming problem10:33
Explore the graphical method for linear programming by building feasible region, plotting constraint lines like x1+x2 ≤ 80 and x1 ≤ 40, and using iso-cost lines to locate the optimum.
Optimal Solution and Corner Points13:27
Identify the optimal solution at corner points of the feasible region for a linear programming problem. Evaluate the objective function at intersections of binding constraints to maximize z.
Using Tools to Quickly Draw Feasible Region14:16
Use free tools like one note and online graphing calculators to quickly draw the linear programming feasible region in 2d.
Linear programming Optimization Using Excel Solver7:06
Use Excel Solver to minimize z = 6x + 7y subject to 2x+6y ≥ 10, 5x+3y ≥ 10, x,y ≥ 0, yielding x=1.25, y=1.25, z=16.25.
Simplex Method16:47
Explore the simplex method for solving linear programming problems by standardizing constraints with dummy variables, building the matrix, and performing pivots to maximize z.

Requirements

Basic Linear Algebra

Description

The course is focused on the application of linear programming techniques. Most of the mathematical models presented in the course are The course includes discussions of the simplex algorithm and other methods to derive solutions for the above models. The Excel Solver software is also used in the course to solve linear programming problems. Discussions (Sensitivity Analysis) are included as to how changes or variations in a linear programming’s parameters affect the optimal solution.

Who this course is for:

Students of Mathematics, Engineering, and Business
Operations Engineers
Production leaders
Business Analysts
Business Office Executives

Operations Research

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 38min

Linear Programming8 lectures • 1hr 42min

Sensitivity Analysis1 lecture • 12min

Network Models1 lecture • 10min

Requirements

Description

Who this course is for: