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June 12, 2026Journal of Project ManagementOpen Access

Dynamic flexible job shop scheduling using greedy actor–neural critic PPO reinforcement learning algorithm

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Authors

SCSelva Kumar ChandrasekarThi Qar UniversityHSHariss Kumar ShanmugaprabuAMAswath Mani

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Implication

Randomized trial demonstrates reduced makespan and energy consumption in smart manufacturing, suggesting improved efficiency.

Key Points

  • The aim is to develop an adaptive scheduling framework to effectively manage job assignments and minimize both makespan and energy consumption.
  • Implemented a Greedy Actor Neural Critic PPO Reinforcement Learning framework.
  • Formulated the scheduling problem as a Markov Decision Process.
  • Designed a multi-objective reward structure to penalize high makespan and energy usage.
  • Achieved makespan within ±2% of benchmarks for small instances.
  • Recorded approximately 23% reduction in makespan for larger cases.
  • Achieved 16–20% lower makespan and 15–25% lower energy consumption compared to RL-QL.

Cite This Study

Chandrasekar et al. (2026) studied this question.

synapsesocial.com/papers/6a2ba1438101cf8926f00cb7https://doi.org/10.5267/j.jpm.2026.4.009
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Adaptive Lagrangian Penalty-Enhanced Proximal Policy Optimization for Flexible Job Shop Rescheduling with Worker Workload Constraints Under Concurrent Dynamic Disturbances2026
  2. 2PPO-Graph Explorer: A New Method for Flexible Job Shop Scheduling via Entropy-Guided Attention Networks2026 · 1 citations
  3. 3A Hierarchical Reinforcement Learning Approach with Multi-Dimensional State Feature Extraction for Energy-Aware Flexible Job Shop Scheduling2026
  4. 4Flexible Job Shop Scheduling Problem Based on Deep Reinforcement Learning Using Dual Attention Network2026
  5. 5HMA-PPO for Integrated Production and Preventive Maintenance Scheduling in Distributed Flexible Job Shops with Job Arrivals2026