# Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling

arXiv: https://arxiv.org/abs/2502.15791 · HTML: https://mlsanity.com/paper/2502.15791/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 69 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 25 of 25)

- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12367/) (2020)
- [Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood](https://mlsanity.com/paper/2012.10384/) (2020)
- [Learning to Delegate for Large-scale Vehicle Routing](https://mlsanity.com/paper/2107.04139/) (2021)
- [GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time](https://mlsanity.com/paper/2312.08224/) (2023)
- [Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt](https://mlsanity.com/paper/2310.18264/) (2023)
- [DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization](https://mlsanity.com/paper/2309.14032/) (2023)
- [Learning to branch with Tree MDPs](https://mlsanity.com/paper/2205.11107/) (2022)
- [Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model](https://mlsanity.com/paper/2302.00244/) (2023)
- [Online Mixed-Integer Optimization in Milliseconds](https://mlsanity.com/paper/1907.02206/) (2019)
- [Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning](https://mlsanity.com/paper/2302.01578/) (2023)
- [Learning to Compare Nodes in Branch and Bound with Graph Neural Networks](https://mlsanity.com/paper/2210.16934/) (2022)
- [Self-Improved Learning for Scalable Neural Combinatorial Optimization](https://mlsanity.com/paper/2403.19561/) (2024)
- [Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling](https://mlsanity.com/paper/2211.10936/) (2022)
- [Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning](https://mlsanity.com/paper/2305.05119/) (2023)
- [Warm Start of Mixed-Integer Programs for Model Predictive Control of Hybrid Systems](https://mlsanity.com/paper/1910.08251/) (2019)
- [CoCo: Online Mixed-Integer Control via Supervised Learning](https://mlsanity.com/paper/2107.08143/) (2021)
- [LinSATNet: The Positive Linear Satisfiability Neural Networks](https://mlsanity.com/paper/2407.13917/) (2024)
- [Learning to Configure Separators in Branch-and-Cut](https://mlsanity.com/paper/2311.05650/) (2023)
- [Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem](https://mlsanity.com/paper/2309.15517/) (2023)
- [Receding Horizon Task and Motion Planning in Changing Environments](https://mlsanity.com/paper/2009.03139/) (2020)
- [Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem](https://mlsanity.com/paper/2402.17606/) (2024)
- [Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows](https://mlsanity.com/paper/2303.03475/) (2023)
- [A Structure Exploiting Branch-and-Bound Algorithm for Mixed-Integer Model Predictive Control](https://mlsanity.com/paper/1903.09117/) (2019)

## Built on by (most cited first, 7 of 7)

- [FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization](https://mlsanity.com/paper/2505.16952/) (2025)
- [ReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflection](https://mlsanity.com/paper/2508.01724/) (2025)
- [Solve Smart, Not Often: Policy Learning for Costly MILP Re-solving](https://mlsanity.com/paper/2509.23470/) (2025)
- [A Learning Method with Gap-Aware Generation for Heterogeneous DAG Scheduling](https://mlsanity.com/paper/2603.23249/) (2026)
- [Imitation Learning for Combinatorial Optimisation under Uncertainty](https://mlsanity.com/paper/2601.05383/) (2026)
- [Graph-RHO: Critical-path-aware Heterogeneous Graph Network for Long-Horizon Flexible Job-Shop Scheduling](https://mlsanity.com/paper/2604.10073/) (2026)
- [DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents](https://mlsanity.com/paper/2605.27566/) (2026)

## Abstract (arXiv)

Long-horizon combinatorial optimization problems (COPs), such as the Flexible Job-Shop Scheduling Problem (FJSP), often involve complex, interdependent decisions over extended time frames, posing significant challenges for existing solvers. While Rolling Horizon Optimization (RHO) addresses this by decomposing problems into overlapping shorter-horizon subproblems, such overlap often involves redundant computations. In this paper, we present L-RHO, the first learning-guided RHO framework for COPs. L-RHO employs a neural network to intelligently fix variables that in hindsight did not need to be re-optimized, resulting in smaller and thus easier-to-solve subproblems. For FJSP, this means identifying operations with unchanged machine assignments between consecutive subproblems. Applied to FJSP, L-RHO accelerates RHO by up to 54% while significantly improving solution quality, outperforming other heuristic and learning-based baselines. We also provide in-depth discussions and verify the desirable adaptability and generalization of L-RHO across numerous FJSP variates, distributions, online scenarios and benchmark instances. Moreover, we provide a theoretical analysis to elucidate the conditions under which learning is beneficial.