{"type":"ScholarlyArticle","title":"Preference-Guided Multi-Policy Optimization for Flexible Job Shop Scheduling","authors":[{"name":"Inguk Choi","slug":"inguk-choi","given":"Inguk","family":"Choi","affiliation":""},{"name":"Woo-Jin Shin","slug":"woo-jin-shin","given":"Woo-Jin","family":"Shin","affiliation":""},{"name":"Sang-Hyun Cho","slug":"sang-hyun-cho","given":"Sang-Hyun","family":"Cho","affiliation":""},{"name":"Hyun-Jung Kim","slug":"hyun-jung-kim","given":"Hyun-Jung","family":"Kim","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/685","url":"https://ijcai.org/proceedings/2026/685","pdf":"https://ijcai.org/proceedings/2026/0685.pdf","markdown":"https://ijcai.org/proceedings/2026/685.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/685","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Planning and Scheduling","track":"Main Track","number":685,"pages":{"first":6156,"last":6165},"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"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}