This article explains 0‑1 integer programming, its advantages, common solution methods, and demonstrates its application to an assignment problem using a relay‑team case study solved with Python's PuLP library, yielding the optimal swimmer‑stroke allocation.
This article explains the transshipment problem—a special case of transportation with intermediate warehouses—provides its mathematical formulation, defines indices, decision variables, parameters, objective function and constraints, and demonstrates a complete Python implementation using the PuLP library, including sample data and solution output.
This article explains the fundamentals of sensitivity analysis in linear programming, detailing how changes to objective coefficients, constraint bounds, and coefficients affect model outcomes, and demonstrates computing shadow prices and slack variables using Python's PuLP library with a glass manufacturing example.
This tutorial demonstrates how to model and solve a chocolate‑production profit maximization problem using Python's PuLP library, covering decision‑variable definition, objective formulation, constraints, and result extraction in a clear step‑by‑step guide for beginners.
Learn how to model and solve a relay‑team selection problem using Python’s PuLP library, defining decision variables, parameters, objective function, and constraints, then executing the solver to obtain the optimal swimmer‑stroke assignments and total time.
This article explains the fixed‑cost production planning problem, builds a mathematical model with binary and integer variables, and demonstrates its solution on a garment factory example using Python's PuLP library to maximize profit.
