Learning to Schedule in Construction and Built-Environment Operations: A Systematic Critical Review of Deep Reinforcement Learning, Graph Neural Networks, and Multi-Agent Approaches

A systematic critical review of deep reinforcement learning, graph neural network and multi-agent methods for scheduling in construction and built-environment operations.

@unpublished{zhanglearning,
  title     = {Learning to Schedule in Construction and Built-Environment Operations: A Systematic Critical Review of Deep Reinforcement Learning, Graph Neural Networks, and Multi-Agent Approaches},
  author    = {Zhang, Z. and Ku Chia, T. P. and Zhang, W.},
  note      = {Under review at Automation in Construction}
}

1. [6]

   Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories

   Zhang, Z., and Zhang, W.*

   IEEE Transactions on Industrial Informatics.

2. [8]

   FM-CrossTraining: Computational Framework and Open Benchmark for Multiskill Maintenance Dispatching

   Zhang, Z.*, and Ku Chia, T. P.

   ASCE Journal of Computing in Civil Engineering. Major revision.

3. [9]

   Which Assets Deserve Attention First? Metric Choice and Budget Risk in Maintenance Prioritization from Infrastructure Work-Order Records

   Ku Chia, T. P., Hu, F., and Zhang, Z.*

   ASCE Journal of Infrastructure Systems. Major revision.

4. [10]

   An Open CMMS-Derived Benchmark for Building-Maintenance Work-Order Dispatching with a Decision Map Keyed on Crew Utilization

   Zhang, Z.*, Ku Chia, T. P., and Tang, J.

   ASCE Journal of Computing in Civil Engineering.

5. [12]

   PACT: Policy Across Composable Timing Rules with a Certified Reward for Flexible Job-Shop Scheduling

   Zhang, Z., and Zhang, W.*

   IEEE Transactions on Industrial Informatics (target venue).

6. [13]

   BOLT: Multi-Bottleneck Bound-Guided Learning for Flexible Job-Shop Scheduling with Time Lags and Resource-Coupled Transport

   Zhang, Z., and Zhang, W.*

   IEEE Transactions on Automation Science and Engineering (target venue).

7. [14]

   SURGE: A Human-in-the-Loop Framework That Recovers Hidden Urgency from Supervisor Overrides and Applies It to Every Maintenance Dispatch

   Ku Chia, T. P., and Zhang, Z.*