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

This repository provides an OpenAI Gym-compatible environment for production scheduling tasks, designed to benchmark reinforcement learning agents in job shop and flow shop settings.

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

Job Shop Scheduling Problem using Simulated Annealing in Python

Provide code and algorithms in scheduling following in the student textbook

Multi-objective job shop scheduling system using NSGA-II algorithm to optimize makespan, energy costs, and tardiness simultaneously. Features energy-aware scheduling with time-of-use tariffs and Pareto front visualization

A minimal jobshop planner

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

Genetic algorithm with a giffler thompson algorithm for JSSP

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

Particle Swarm Optimization to solve the FJSP problem

Job shop constraint optimization/scheduling using Google OR Tools

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).