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

TL;DR

This paper introduces a novel algorithm based on Generalized Nested Rollout Policy Adaptation to improve solutions for the NP-hard Flexible Job-Shop Scheduling Problem, outperforming other MCTS-based methods.

Contribution

The paper presents a new algorithm tailored for FJSSP, demonstrating superior performance over existing MCTS-based approaches in scheduling efficiency.

Findings

The proposed algorithm outperforms other MCTS-based methods.

Experimental results show improved scheduling makespans.

Large instances still have room for optimization.

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.