Proceedings

Preference-Guided Multi-Policy Optimization for Flexible Job Shop Scheduling

Inguk Choi, Woo-Jin Shin, Sang-Hyun Cho, Hyun-Jung Kim

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 6156-6165.

https://doi.org/10.24963/ijcai.2026/685

Keywords

  • Planning and Scheduling: Search in planning and scheduling
  • AI: Planning and Scheduling
  • Planning and Scheduling: Scheduling
  • Planning and Scheduling: Learning in planning and scheduling

Abstract

Under the shift toward Industry 4.0, mass-customized manufacturing systems have introduced complex scheduling problems, such as the Flexible Job Shop Scheduling Problem (FJSSP). Recent Deep Reinforcement Learning (DRL)-based heuristics have shown promise, yet existing methods often suffer from two key limitations: they typically rely on single-policy optimization, which limits exploration, and on imprecise reward functions, which fail to accurately reflect decision quality. To address these challenges simultaneously, we propose PGMPO (Preference-Guided Multi-Policy Optimization), a novel learning framework consisting of (1) a simple but effective multi-policy modeling approach that allows a single network to represent multiple decision-makers, and (2) a preference-driven model optimization method that effectively guides policies to learn diverse and specialized problem-solving strategies without the need for explicit reward functions. Experimental results demonstrate that PGMPO substantially boosts the performance of existing neural solvers across several benchmarks.

Inguk Choi, Woo-Jin Shin, Sang-Hyun Cho, and Hyun-Jung Kim. 2026. Preference-Guided Multi-Policy Optimization for Flexible Job Shop Scheduling. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 6156–6165. https://doi.org/10.24963/ijcai.2026/685
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