AI constraint solver in Java to optimize the vehicle routing problem, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.

OptaPlanner quick starts for AI optimization: many use cases shown in many different technologies.

OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

YiShape-Math is a Java math library that provides NumPy-like functionalities including vector & matrix operations, data visualization, statistics, optimization, time series, signal processing, audio analysis, image processing and machine learning models.

A comprehensive collection of implementations and resources related to the SIMPLEX algorithm.

Plan-A is a tool for turning real-world challenges into solvable optimization problems. Define your data, rules, and goals — and let the solver find the best plan, whether it’s for scheduling, routing, or even chip design!

This repository contains the Java implementation of a decomposition algorithm to solve the integrated Timetabling and Electric Vehicle Scheduling Problem.

This repository contains a Java implementation of a Benders Decomposition algorithm to solve the Directed Robust Perfect b-Matching Problem (DRPbM). The results are part of my PhD-thesis and will be published in the near future.