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

arXiv: https://arxiv.org/abs/2505.08451 · HTML: https://mlsanity.com/paper/2505.08451/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 62 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12367/) (2020)
- [Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning](https://mlsanity.com/paper/2106.01086/) (2021)
- [Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications](https://mlsanity.com/paper/2303.08060/) (2023)
- [Generalized Nested Rollout Policy Adaptation](https://mlsanity.com/paper/2003.10024/) (2020)
- [Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess](https://mlsanity.com/paper/2110.01810/) (2021)
- [Learning a Prior for Monte Carlo Search by Replaying Solutions to Combinatorial Problems](https://mlsanity.com/paper/2401.10431/) (2024)
- [Monte Carlo Game Solver](https://mlsanity.com/paper/2001.05087/) (2020)
- [Generalized Nested Rollout Policy Adaptation with Limited Repetitions](https://mlsanity.com/paper/2401.10420/) (2024)
- [Generalized Nested Rollout Policy Adaptation with Dynamic Bias for Vehicle Routing](https://mlsanity.com/paper/2111.06928/) (2021)
- [Updating Lower and Upper Bounds for the Job-Shop Scheduling Problem Test Instances](https://mlsanity.com/paper/2504.16106/) (2025)

## Abstract (arXiv)

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.