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.

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

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

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

High-perf engine for MO-FJSP-SDST-ARC. Engineered for 50,000+ operations via RHC & ALNS. Unlike black-box NNs, SynAPS uses 12 deterministic solvers (CP-SAT, LBBD-HD) for full traceability and provable optimality. No neural nets, zero vendor lock-in, MIT. Transparent AI for complex production.