Production Scheduling

Production scheduling optimizes the allocation of resources (machines, personnel, materials) to tasks over time, aiming to minimize costs and maximize efficiency. Current research heavily utilizes reinforcement learning (RL), particularly deep RL, often incorporating explainable AI techniques to improve transparency and incorporating curriculum learning to enhance training efficiency. These advancements, along with mathematical models like mixed-integer linear programming (MILP) and memetic algorithms, are improving the ability to handle complex real-world constraints, such as those found in flexible job shops and customized production environments, leading to more robust and practical scheduling solutions for various industries.

Papers

August 19, 2024

Demystifying Reinforcement Learning in Production Scheduling via Explainable AI
Daniel Fischer, Hannah M. Hüsener, Felix Grumbach, Lukas Vollenkemper, Arthur Müller, Pascal Reusch
Reinforcement Learning Deep Reinforcement Learning Explainable AI Scheduling Policy Production Scheduling

June 4, 2024

Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling
Arthur Müller, Felix Grumbach, Matthia Sabatelli
Reinforcement Learning Global Impact Mini Batch Big Gain Large Batch Batch Size Flow Shop Scheduling Production Scheduling

February 24, 2024

A mathematical model for simultaneous personnel shift planning and unrelated parallel machine scheduling
Maziyar Khadivi, Mostafa Abbasi, Todd Charter, Homayoun Najjaran
Task Scheduling Mathematical Model Scheduling Problem Machine Scheduling Production Scheduling

October 4, 2023

Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
Maziyar Khadivi, Todd Charter, Marjan Yaghoubi, Masoud Jalayer, Maryam Ahang, Ardeshir Shojaeinasab, Homayoun Najjaran
Reinforcement Learning Deep Reinforcement Learning NCD Method State of the Art Future Direction Machine Scheduling Production Scheduling

September 22, 2023

AI-Copilot for Business Optimisation: A Framework and A Case Study in Production Scheduling
Pivithuru Thejan Amarasinghe, Su Nguyen, Yuan Sun, Damminda Alahakoon
New Framework Case Study LLM Fine Tuning Large Scale Optimization Optimization Framework Production Scheduling AI Copilot Business Optimisation

April 24, 2023

Addressing distributional shifts in operations management: The case of order fulfillment in customized production
Julian Senoner, Bernhard Kratzwald, Milan Kuzmanovic, Torbjørn H. Netland, Stefan Feuerriegel
Distribution Shift Case Relevance Production Incident Job Shop Scheduling Production Scheduling

January 10, 2023

schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
Constantin Waubert de Puiseau, Jannik Peters, Christian Dörpelkus, Hasan Tercan, Tobias Meisen
Reinforcement Learning Deep Reinforcement Learning Scheduling Problem Software Framework Production Scheduling

December 21, 2022

A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling
Felix Grumbach, Nour Eldin Alaa Badr, Pascal Reusch, Sebastian Trojahn
Reinforcement Learning Metaheuristic Algorithm Optimal Scheduling Memetic Algorithm Flexible Job Shop Scheduling Problem Production Scheduling Objective Optimization

May 6, 2022

Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes
Felix Gehlhoff, Alexander Fay
Practical Algorithm Multi Agent System Production Incident Decentralized Agent Efficient Scheduling Production Scheduling Decentralized Manufacturing System

March 1, 2022

Distributional Reinforcement Learning for Scheduling of Chemical Production Processes
Max Mowbray, Dongda Zhang, Ehecatl Antonio Del Rio Chanona
Reinforcement Learning Task Scheduling Distributional Reinforcement Learning Optimal Scheduling Production Scheduling

Production Scheduling

Papers

Demystifying Reinforcement Learning in Production Scheduling via Explainable AI

Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling

A mathematical model for simultaneous personnel shift planning and unrelated parallel machine scheduling

Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

AI-Copilot for Business Optimisation: A Framework and A Case Study in Production Scheduling

Addressing distributional shifts in operations management: The case of order fulfillment in customized production

schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments

A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling

Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes

Distributional Reinforcement Learning for Scheduling of Chemical Production Processes