5 papers
ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention
Henrik Abgaryan, Tristan Cazenave, Ararat Harutyunyan
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, yet their direct application to NP-hard combinatorial problems (CPs) remains underexplored. In thi…
Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem
Lotfi Kobrosly, Marc-Emmanuel Coupvent des Graviers, Christophe Guettier +1
The Flexible Job-Shop Scheduling Problem (FJSSP) is an NP-hard combinatorial optimization problem, with several application domains, especially for manufacturing purposes. The obje…
Updating Lower and Upper Bounds for the Job-Shop Scheduling Problem Test Instances
Marc-Emmanuel Coupvent des Graviers, Lotfi Kobrosly, Christophe Guettier +1
The Job-Shop Scheduling Problem (JSSP) and its variant, the Flexible Job-Shop Scheduling Problem (FJSSP), are combinatorial optimization problems studied thoroughly in the literatu…
Starjob: Dataset for LLM-Driven Job Shop Scheduling
Henrik Abgaryan, Tristan Cazenave, Ararat Harutyunyan
Large Language Models (LLMs) have shown remarkable capabilities across various domains, but their potential for solving combinatorial optimization problems remains largely unexplor…
LLMs can Schedule
Henrik Abgaryan, Ararat Harutyunyan, Tristan Cazenave
The job shop scheduling problem (JSSP) remains a significant hurdle in optimizing production processes. This challenge involves efficiently allocating jobs to a limited number of m…