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

Solve production scheduling problems with constraint programming in Python.

A modular Python library for creating, solving, and visualizing job shop scheduling problems.

A repository with a data set including instances and results from literature for the Job Shop Scheduling Problem (JSSP). While the raw data is provided as text files, it is also compiled in an R package with an API around it.

An end to end reinforcement learning approach with a reinforcement learning environment modeled as a CP model

Constraint Satisfaction Problem (CSP) Solver

A Python library for implementing and testing algorithm for Job-Shop Scheduling problem.

Solving the Job-Shop Scheduling Problem (JSSP) with Graph Neural Networks (GNNs).

A Gymnasium Environment for the Job Shop Problem Using the Disjunctive Graph Approach.

ScheduleOpt benchmarks

Implementation of job-shop scheduling problem using C#.

⚙️ Effortless and efficient task scheduling tailored for production, built with numpy.

Research code for flow shop, job shop and open shop scheduling problems

Job scheduling with deadlines using quantum annealing

Job Shop Scheduling Problem benchmark instances from http://jobshop.jjvh.nl/ and some utility functions to work with JSP instances.

Metaheuristic- and RL-powered production scheduling & optimization toolkit in Python

A comparative study solving the Multi-Objective Flexible Job Shop Scheduling Problem (FJSSP) using Deep Reinforcement Learning (Masked PPO) and Tabu Search to optimize makespan and energy consumption.基于深度强化学习（掩码PPO）与禁忌搜索的多目标柔性作业车间调度问题对比研究，旨在优化最大完工时间与能耗。

A Matlab-based Hyper-Heuristic framework. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711022000413

Advanced Data Structures in C — Job Shop scheduling model with nested linked lists, B-trees and dynamic memory management