Discover the power of realistic linear programming models with randomized constraint limits. Explore risk analysis and Monte Carlo simulation in business analytics. Perfect for graduate students.

Traditional linear program (LP) models are deterministic. The way that constraint limit uncertainty is handled is to compute the range of feasibility. After the optimal solution is obtained, typically ...

In the linear programming approach to approximate dynamic programming, one tries to solve a certain linear program - the ALP -, which has a relatively small number K of variables but an intractable ...

This repo contains linear programming examples of production scheduling and distribution using Excel and Python. These activities are largely from a Udemy course on Data Science and Supply Chain ...

Simple Linear Programming Repository This repository provides simple examples of solving linear programming problems using Python libraries such as NumPy, PuLP, SciPy, and Matplotlib. Linear ...

The hyperbolic (or fractional) linear programming problem with only one aggregate constraint is solved by a simple and extremely efficient algorithm. The core step in the iterative procedure is the ...

A continuing problem with inductive logic programming (ILP) has proved to be difficult to handle. Constraint inductive logic programming (CILP) aims to solve this problem with ILP. We propose a new ...