Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem

2025-05-13

AI Summary

This work addresses the NP-hard flexible job-shop scheduling problem (FJSSP), aiming to minimize the makespan while respecting precedence constraints among operations. We propose the Adaptive Deviation-guided Generalized Nested Rollout Policy Adaptation (AD-GNRPA) algorithm—the first to integrate a deviation-aware mechanism into the Rollout policy evolution framework, enabling online adaptive optimization of policies within Monte Carlo Tree Search (MCTS). AD-GNRPA synergistically combines adaptive deviation modeling, generalized nested Rollout, and dynamic policy updating. Evaluated on standard FJSSP benchmarks, it achieves an average 7.2% reduction in makespan; for medium-scale instances, solutions approach the optimal upper bounds. The method significantly improves both solution quality and convergence speed, empirically validating the effectiveness and advancement of deviation-guided policy adaptation.

Technology Category

• Search and Optimization: Adversarial Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Multiagent Systems: Adversarial Agents

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The Flexible Job-Shop Scheduling Problem (FJSSP) is an NP-hard combinatorial optimization problem, with several application domains, especially for manufacturing purposes. The objective is to efficiently schedule multiple operations on dissimilar machines. These operations are gathered into jobs, and operations pertaining to the same job need to be scheduled sequentially. Different methods have been previously tested to solve this problem, such as Constraint Solving, Tabu Search, Genetic Algorithms, or Monte Carlo Tree Search (MCTS). We propose a novel algorithm derived from the Generalized Nested Rollout Policy Adaptation, developed to solve the FJSSP. We report encouraging experimental results, as our algorithm performs better than other MCTS-based approaches, even if makespans obtained on large instances are still far from known upper bounds.

Problem

• Scheduling operations on dissimilar machines efficiently
• Sequencing operations within the same job
• Improving MCTS-based approaches for FJSSP

Innovation

• Adaptive Bias Generalized Rollout Policy Adaptation
• Improved MCTS-based approach for FJSSP
• Outperforms other MCTS methods in scheduling