Learning to dispatch for job shop scheduling via deep reinforcement learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance. In this paper, we propose to automatically learn PD...
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| Main Authors: | , , , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2020
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| Online Access: | https://ink.library.smu.edu.sg/sis_research/8133 https://ink.library.smu.edu.sg/context/sis_research/article/9136/viewcontent/NeurIPS_2020_learning_to_dispatch_for_job_shop_scheduling_via_deep_reinforcement_learning_Paper.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance. In this paper, we propose to automatically learn PDRs via an end-to-end deep reinforcement learning agent. We exploit the disjunctive graph representation of JSSP, and propose a Graph Neural Network based scheme to embed the states encountered during solving. The resulting policy network is size-agnostic, effectively enabling generalization on large-scale instances. Experiments show that the agent can learn high-quality PDRs from scratch with elementary raw features, and demonstrates strong performance against the best existing PDRs. The learned policies also perform well on much larger instances that are unseen in training. |
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