Linear Programming in Python | PuLP Tutorial: Max Profit Model, Constraints, Shadow Prices скачать в хорошем качестве

Linear Programming in Python | PuLP Tutorial: Max Profit Model, Constraints, Shadow Prices

In this hands-on tutorial, you will learn how to solve a full Linear Programming (LP) problem using Python’s PuLP library. The video walks through building a maximization model—producing chairs and tables to achieve the highest possible profit under resource constraints. You’ll learn how to install PuLP, define decision variables, build the objective function, add constraints, solve the model, and interpret the results. The tutorial also covers advanced concepts such as shadow prices and reduced costs, helping you understand sensitivity analysis and resource efficiency. 📌 What You Will Learn ✔ How to install and import PuLP in Python ✔ How to formulate an LP model (maximization) ✔ Defining decision variables ✔ Writing the objective function in PuLP ✔ Adding resource constraints ✔ Solving the LP model and checking solution status ✔ Extracting optimal values for decision variables ✔ Performing sensitivity analysis ✔ Understanding: • Shadow prices (marginal value of resources) • Reduced costs (value of introducing a variable) ✔ How Python PuLP outputs support OR decision-making 📌 Key Takeaways PuLP provides a simple and powerful interface for building LP and ILP models in Python. A maximization problem requires defining an objective function and constraints using linear expressions. The solver returns optimal values, objective value, and feasibility status. Shadow prices identify the improvement in objective value when a constraint’s RHS increases by one unit. Reduced costs tell whether adding or increasing production of a variable would improve the objective. LP modeling is widely used in manufacturing, supply chain, operations planning, scheduling, and capacity allocation. Code + Related files used in the playlist of Python PuLP https://github.com/hakeemrehman/Pytho... “How do I solve a linear programming problem in Python using PuLP?” “What is the easiest way to set up and solve optimization problems in Python?” “How can I perform sensitivity analysis with PuLP in Python?” “Which Python library is best for linear programming?” Learn how to solve a Linear Programming maximization model in Python using PuLP, including constraints, optimal value extraction, and sensitivity analysis. #PythonPuLP #LinearProgramming #OperationsResearch #Optimization #SensitivityAnalysis #ShadowPrice Thanks for watching! 🚀 Should I make a tutorial on Integer Programming, Transportation Problems, or Sensitivity Analysis in depth? Comment below! Python’s PuLP library allows users to easily formulate and solve Linear Programming models. This tutorial builds a maximization problem, adds constraints, solves using PuLP, and explains sensitivity measures like shadow prices and reduced costs.