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Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Cong Zhang · Wen Song · Zhiguang Cao · Jie Zhang · Puay Siew Tan · Xu Chi

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #616

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.

Author Information

Cong Zhang (Nanyang Technological University)
Wen Song (Institute of Marine Scinece and Technology, Shandong University)
Zhiguang Cao (National University of Singapore)
Jie Zhang (Nanyang Technological University)
Puay Siew Tan (SIMTECH)
Xu Chi (Singapore Institute of Manufacturing Technology, A-Star)

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