This is the official code of the publised paper 'A Multi-action Deep Reinforcement Learning Framework for Flexible Job-shop Scheduling Problem'

A benchmarking repo with various solution methods to various machine scheduling problems

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

Solve production scheduling problems with constraint programming in Python.

This is the official code for the baseline methods of the publised paper 'A Multi-action Deep Reinforcement Learning Framework for Flexible Job-shop Scheduling Problem'

The public benchmark instances of flexible job shop scheduling problem

Multi-objective Flexible Job Shop Scheduling Problem with transportation constraint solved with NSGA-II, VNS and improved initialisation

A Python implementation of NSGA-II for multi-objective FJSP, optimizing makespan and energy consumption.

A Python implementation of NSGA-II for multi-objective flexible job shop scheduling with AGVs, optimizing makespan and energy consumption.

Solving flexible job-shop scheduling problem to minimize mean flow time using arithmetic optimization algorithm and rao-1 algorithm.

Particle Swarm Optimization to solve the FJSP problem

Deep Reinforcement Learning-assisted Memetic Algorithm for Distributed Flexible Job Shop Scheduling

White-box scheduling engine for MO-FJSP-SDST-ARC production planning. 22 solver configs (CP-SAT, LBBD, ALNS, RHC), optional Rust/PyO3 acceleration, deterministic routing, reproducible benchmarks. Solves multi-objective flexible job-shop with sequence-dependent setups, auxiliary resources, and explainable metadata. Python 3.12+, MIT.