Jul 18, 2023 · We develop a novel DRL-based PMS method, called DPMS, in which the developed model considers the characteristics of PMS to design states and the reward.
Abstract—Parallel machine scheduling (PMS) is a common setting in many manufacturing facilities, in which each job is allowed to be processed on one of the ...
Oct 22, 2024 · It involves scheduling n n jobs on m m machines to minimize certain objective functions. For preemptive scheduling, most problems are not only ...
In this paper, we propose an incremental learning-based scheduling method (ILS) in which neural networks (NNs) are periodically trained by utilizing schedules ...
This paper proposes a generative deep reinforcement learning method that investigates the dynamic parallel machines scheduling problems with adaptive ...
Missing: Machine Q- Network.
Nov 21, 2024 · In this paper, we used Deep Reinforcement Learning (DRL) approach on DJSSP to minimize the Makespan. A Deep Q Network (DQN) algorithm is ...
Nov 21, 2024 · In this paper, we propose an incremental learning-based scheduling method (ILS) in which neural networks (NNs) are periodically trained by ...
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What is parallel machine scheduling?
Feb 6, 2023 · This study considers a parallel dedicated machine scheduling problem towards minimizing the total tardiness of allocated jobs on machines.
RL-based parallel machine scheduling optimization algorithms are mainly designed for the dynamic scheduling problem. For the dynamic parallel machine scheduling.
Nov 12, 2024 · This paper addresses the Unrelated Parallel Machine Scheduling Problem (UPMS) with setup times and resources using a Multi-Agent Reinforcement ...