A Reinforcement Learning Environment For Job-Shop Scheduling

• URL: http://arxiv.org/abs/2104.03760v1
• Date: Thu, 8 Apr 2021 13:26:30 GMT
• Title: A Reinforcement Learning Environment For Job-Shop Scheduling
• Authors: Pierre Tassel, Martin Gebser, Konstantin Schekotihin
• Abstract summary: This paper presents an efficient Deep Reinforcement Learning environment for Job-Shop Scheduling. We design a meaningful and compact state representation as well as a novel, simple dense reward function. We demonstrate that our approach significantly outperforms existing DRL methods on classic benchmark instances.
• Score: 2.036811219647753
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scheduling is a fundamental task occurring in various automated systems applications, e.g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste. Nevertheless, finding such schedules is often intractable and cannot be achieved by Combinatorial Optimization Problem (COP) methods within a given time limit. Recent advances of Deep Reinforcement Learning (DRL) in learning complex behavior enable new COP application possibilities. This paper presents an efficient DRL environment for Job-Shop Scheduling -- an important problem in the field. Furthermore, we design a meaningful and compact state representation as well as a novel, simple dense reward function, closely related to the sparse make-span minimization criteria used by COP methods. We demonstrate that our approach significantly outperforms existing DRL methods on classic benchmark instances, coming close to state-of-the-art COP approaches.

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