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 JobShop scheduling using Genetic Algorithm

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

Jobshop using OR tools and Flask

Job Shop Scheduling Problem using Simulated Annealing in Python

A minimal jobshop planner

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

Provide code and algorithms in scheduling following in the student textbook

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

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).

Job shop constraint optimization/scheduling using Google OR Tools