Mar 4, 2024 � This paper introduces a new approach that leverages Deep Reinforcement Learning (DRL) techniques to search for robust solutions, emphasizing JSSPs with�...
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May 25, 2024 � This paper presents Wheatley, a novel approach for solving JSSPs with uncertain operations duration. It combines Graph Neural Networks and Deep�...
Mar 4, 2024 � Our objective is to generate a robust schedule, i.e. that minimizes the average makespan. This paper introduces a new approach that leverages�...
May 30, 2024 � This paper addresses a variant of the job shop scheduling problem with total tardiness minimization where task durations and due dates are�...
Apr 10, 2024 � Solving Job Shop Scheduling with uncertain operations duration is a key changer for industrial efficiency. Our approach combines Graph�...
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Learning to Solve Job Shop Scheduling under Uncertainty Key contributions of this research include: (1) advancements in DRL applications to JSSPs, enhancing�...
This study presents a methodology that makes use of Deep Recurrent Q-Learning to develop an agent that acts as an online scheduler for flow-shop or job-shop�...
jolibrain/wheatley: Next-generation scheduling problem ... - GitHub
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Wheatley learns how to schedule well and generalize over problems and/or uncertainty. � Uses PPO as the main RL algorithm � Captures schedules in the form of�...
Apr 2, 2024 � TL;DR: The paper introduces Wheatley, a novel approach using Deep Reinforcement Learning and Graph Neural Networks to solve Job-Shop Scheduling�...
This study considers a job shop scheduling problem with reverse flows under uncertainty. Since the main parameter of the model (i.e., the processing time of�...