GOAL: Graph-Based Diffusion Solver for Multi-Objective Optimization
Researchers have introduced GOAL, a conditioned diffusion solver that operates on relational graph representations for the purpose of dynamic multi-objective optimization. This approach employs heterogeneous graph encoding, utilizing various edge types to represent different classes of constraints, which allows for manageable decision-making processes. The effectiveness of GOAL was assessed using Flow Shop, Job Shop, and Flexible Job Shop scheduling challenges, showcasing its ability to generalize across a range of structurally varied benchmarks.
Key facts
- GOAL is a conditioned diffusion solver over relational graph representations.
- It enables controllable decision generations by conditioning on human-specified objectives.
- Heterogeneous graph encoding uses distinct edge types for different constraint classes.
- Message passing structure allows selective information propagation according to constraint ontology.
- Evaluated on Flow Shop Problem (FSP), Job Shop Scheduling Problem (JSP), and Flexible Job Shop Scheduling Problem (FJSP).
- Demonstrates generalization across structurally diverse benchmarks.
- Published on arXiv with ID 2605.19119.
- Announce type is cross.
Entities
Institutions
- arXiv