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Linear Programming Graphical Method & Sensitivity Analysis

   Author: Baturi   |   20 August 2026   |   Comments icon: 0


Free Download Linear Programming Graphical Method & Sensitivity Analysis

Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With Linear Programming Graphical Method & Sensitivity Analysis, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Published 8/2026
Created by Salman Sagheer Warsi
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 46 Lectures ( 6h 54m ) | Size: 3 GB


Step-by-step graphical method, sensitivity analysis & Excel Solver — with worked examples, quizzes and worksheets

What you'll learn


⚡ Identify and formulate Linear Programming problems and understand where the graphical method fits as a solution approach
⚡ Solve LP maximization problems confidently using the graphical method — from plotting constraints to finding the optimal solution
⚡ Perform sensitivity analysis on objective function coefficients and RHS values — find sensitivity ranges graphically with confidence.
⚡ Interpret shadow price, reduced cost, and binding constraints correctly — then verify your graphical results using Excel Solver.
⚡ Spot and fix the mistakes that cost students marks in graphical LP problems
⚡ Apply the graphical method, sensitivity analysis, and Excel Solver to a complete real-world Product Mix Optimization problem
⚡ Solve LP minimization problems graphically and apply sensitivity analysis with the same confidence as maximization
⚡ Walk into your LP exam ready — with worked examples covering real exam-style word problems

Requirements


❗ No prior knowledge of Linear Programming or Operations Research required
❗ No specialist software needed — Excel (any version) is sufficient for the Excel sections; all graphical work is done by hand
❗ No prior Excel experience required — the course covers everything you need
❗ A willingness to practice — this course is example-driven, and working through problems alongside the lectures is strongly recommended
❗ Basic algebra helpful but not essential — the course walks through constraint plotting step by step from scratch

Description


You can follow the lecture. You understand the theory. But when exam questions appear — different numbers, different constraints — you freeze.
This course fixes that.
Built for university students in Operations Research, Management Science, Industrial Engineering, or any course that includes Linear Programming, this is a structured, exam-ready system — not a collection of scattered videos.
What Makes This Course Different
Most LP courses teach you the method. This one also teaches you where students go wrong — and why. A dedicated section covers the most common mistakes in graphical LP and sensitivity analysis. Each mistake is explained, shown on a graph, and corrected step by step. You will not find this level of error analysis in a textbook or a standard lecture series.
Every lecture is deliberately kept short — mostly 3 to 8 minutes, with only a handful exceeding 20 minutes where the concept genuinely demands more depth. Each one covers a single idea completely. You can watch one between classes, revisit a specific concept before an exam, or work through a section in a single sitting. No 2-hour lectures you abandon halfway through.
Some core lectures are also available on my YouTube channel, Optima Decisions Lab — where they have been watched by tens of thousands of students. This course goes further: every topic is accompanied by downloadable worked-example PDFs, practice quizzes, and a structured learning path. If you have already watched the YouTube content and found it useful, this course is the natural next step.
Every section ends with a practice quiz and downloadable worked-example PDF — so you test your understanding as you go, not just before the exam.

What You Will Learn


The course covers everything from LP fundamentals and graphical method to sensitivity analysis, common mistakes, and full exam preparation — in a deliberate sequence that builds understanding one concept at a time
What the Course Covers
The course is structured across 7 sections — from LP fundamentals through graphical method, sensitivity analysis, common mistakes, product mix examples, minimization, and exam preparation. Each section builds on the previous one in a deliberate sequence
What You Get
✨ Short, focused lectures — mostly 3 to 8 minutes, none exceeding 26— each covering one complete idea
✨ Worked-example PDFs for every major topic — downloadable and printable
✨ Section-wise practice quizzes to consolidate understanding
✨ A complete constraint plotting guide covering every case you will encounter
✨ LP vs NLP identification worksheet with corner cases
✨ Excel Solver walkthroughs alongside graphical solutions
✨ A certificate of completion

Who this course is for


⭐ University and college students taking Operations Research, Management Science, Quantitative Methods, or Industrial Engineering courses who need to master LP for exams
⭐ Students who have tried to learn LP from textbooks but find the graphical method or sensitivity analysis confusing
⭐ Self-taught learners and YouTube viewers who want a structured, practice-driven path with worked examples, quizzes, and a certificate
⭐ Professionals in supply chain, logistics, or business analytics who want a working understanding of LP fundamentals without heavy mathematical theory
⭐ Anyone preparing for a standardized exam or professional certification that includes Linear Programming topics

Homepage

https://www.udemy.com/course/linear-programming-graphical-method-and-sensitivity-analysis


Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me


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