提出新算法优化柔性作业车间调度，比现有方法更高效。
Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem
- 基于广义回溯策略适应，动态调整调度决策。
- 在大规模实例上缩短了制造工期，优于其他蒙特卡洛方法。
- 适合制造业生产排程与复杂调度场景研究者参考。
柔性作业车间调度问题（FJSSP）是典型的NP难组合优化问题，广泛应用于制造领域，目标是在不同机器上高效安排多道工序。这些工序被组织为任务，同一任务内的工序需按顺序执行。以往方法包括约束求解、禁忌搜索、遗传算法和蒙特卡洛树搜索（MCTS）。本文提出一种源自广义嵌套回溯策略适应的新型算法，用于求解FJSSP。实验表明，该算法性能优于其他基于MCTS的方法，尽管在大型实例上得到的完工时间仍远低于已知上界。
原文摘要 · Abstract (English)
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.
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