This project involves using Genetic Algorithm to solve the dynamic scheduling problem of flexible Job Shop production.

python-lekin: Flexible Supply Chain Planning and Scheduler

Solving scheduling problems with constraint programming in Python.

Using Heuristic Rules, Meta-Heuristic and Hyper-Heuristic approach to solve Job Shop Scheduling Problem.

SEAGE (Search Agents) is a hyper-heuristic framework for metaheuristic collaboration.

Job Shop Scheduling metaheuristics

A MRP application for Job Shops, Machine Shops and Fabrication shops.

A JobShop scheduling using Genetic Algorithm

Jobshop using OR tools and Flask

Provide code and algorithms in scheduling following in the student textbook

Job Shop Scheduling Problem using Simulated Annealing in Python

A minimal jobshop planner

Particle Swarm Optimization to solve the FJSP problem

Genetic algorithm with a giffler thompson algorithm for JSSP

Job-Shop Scheduling Problem with Mixed Integer Optimization. Formulation and implementation in Julia Gurobi.

Learning how to implement GA and NSGA-II for job shop scheduling problem in python

In the context of optimizing the production of a fully connected "smart" 3d printers factory, machine learning methods like Genetic algorithms, Deep Neural Networks as well as more traditional algorithms like Job-shop were used in a simulation environment (Robotic Operating System).

Code used in 2015 paper "Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search". Code using old version of Mistral solver (https://homepages.laas.fr/ehebrard/mistral.html), and old version of IBM ILOG CP Optimizer (https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-cp-optimizer).

A web application utilizing Particle Swarm Optimization (PSO) to optimize job shop scheduling and monitoring in the construction industry.