Proceedings ArticleDOI
Deep Q-Network Model for Dynamic Job Shop Scheduling Pproblem Based on Discrete Event Simulation
Yakup Turgut,Cafer Erhan Bozdag +1 more
- pp 1551-1559
TLDR
In this article, a deep Q-network (DQN) model is applied to train an agent to learn how to schedule the jobs dynamically by minimizing the delay time of jobs.Abstract:
In the last few decades, dynamic job scheduling problems (DJSPs) has received more attention from researchers and practitioners. However, the potential of reinforcement learning (RL) methods has not been exploited adequately for solving DJSPs. In this work deep Q-network (DQN) model is applied to train an agent to learn how to schedule the jobs dynamically by minimizing the delay time of jobs. The DQN model is trained based on a discrete event simulation experiment. The model is tested by comparing the trained DQN model against two popular dispatching rules, shortest processing time and earliest due date. The obtained results indicate that the DQN model has a better performance than these dispatching rules.read more
Citations
More filters
Journal ArticleDOI
An End-to-End Deep Learning Method for Dynamic Job Shop Scheduling Problem
TL;DR: Wang et al. as discussed by the authors proposed an end-to-end transformer-based deep learning method named spatial pyramid pooling-based transformer (SPP-Transformer), which shows strong generalizability and can be applied to different-sized DJSSPs.
Proceedings ArticleDOI
Reinforcement Learning-based Job Shop Scheduling for Remanufacturing Production
TL;DR: Wang et al. as mentioned in this paper adopted Q-learning and DQN to solve the remanufacturing scheduling problem, where system states are extracted and five common-used heuristic scheduling rules are selected as the action set, and the reward function was designed consistent with the objective function.
Proceedings ArticleDOI
Discrete-Event Simulation and Machine Learning for Prototype Composites Manufacture Lead Time Predictions
TL;DR: In this article , a machine learning algorithm was used to predict the lead times of composite products based on the current state of the system using discrete-event simulation (DES), and three types of composites materials and their manufacturing process were initially modelled: dry fiber, prepreg and thermoplastic.
Proceedings ArticleDOI
Discrete-Event Simulation and Machine Learning for Prototype Composites Manufacture Lead Time Predictions
Jamie Karl Smith,Calum Dickinson +1 more
TL;DR: In this paper , three types of composites materials and their manufacturing process were initially modelled: dry fiber, prepreg and thermoplastic, and the accuracies of three machine learning algorithms were compared.
Proceedings ArticleDOI
Reinforcement Learning-based Job Shop Scheduling for Remanufacturing Production
Yue Bai,Yaqiong Lv +1 more
TL;DR: Wang et al. as mentioned in this paper adopted Q-learning and DQN to solve the remanufacturing scheduling problem, where system states are extracted and five common-used heuristic scheduling rules are selected as the action set, and the reward function was designed consistent with the objective function.
References
More filters
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
TL;DR: A tutorial survey of recent works on solving classical JSP using genetic algorithms using various hybrid approaches of genetic algorithms and conventional heuristics is given.
Book
Heuristic scheduling systems : with applications to production systems and project management
TL;DR: This book discusses one-MACHINE PROBLEMS, flow shops and job shops, project scheduling, and control, and model Extensions.
Journal ArticleDOI
Dynamic job shop scheduling: A survey of simulation research
TL;DR: This paper provides a state-of-the-art survey of the simulation-based research on dynamic job shop scheduling with a distinct emphasis on two important aspects.
Journal ArticleDOI
Heuristics in Job Shop Scheduling
TL;DR: The approach is simulative in that the operation of the shop is simulated in a Fortran program, but in addition to the straightforward use of priority rules for determining sequences of jobs on the machines, a number of heuristics or rules of thumb are incorporated.