fjsp

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

Solve production scheduling problems with constraint programming in Python.

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

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

智能制造课程项目：结合遗传算法、强化学习、ALNS 与禁忌搜索求解柔性作业车间调度。

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

FJSP 排产：CP-SAT 精确求解 + 换型两阶段建模｜8/10 算例证明最优，较 SPT 派工平均降低 34.3%；16.2× 换型强度下最忙机器 83.3% 机时用于换型

Deterministic-first scheduling engine for MO-FJSP-SDST-ARC. 25 named solver configs, independent feasibility checker, benchmark harness.