SRIS Module · IDP

Intelligent Decision and Planning

Planning, scheduling, and decision support for intelligent manufacturing and robotic systems under uncertainty, constraints, and multi-agent interaction.

Planning Scheduling Decision Support
01
Optimization
02
Resource Coordination
03
Human-in-the-Loop
SRIS Laboratory SCUT · GZIC
Smart, Reliable, and Interpretable Systems
Overview

Research scope

Intelligent Decision and Planning serves as one of the modular building blocks of the SRIS research portfolio. It connects methods, models, and deployment scenarios so that theory, algorithms, and system-level outcomes can be presented as a coherent academic narrative.

Planning & scheduling Robotics Manufacturing systems
Three key scientific problems:
  • how to model and solve large-scale dynamic scheduling problems under complex resource and precedence constraints
  • how to achieve real-time adaptive rescheduling under uncertainty and disruptions
  • how to balance multiple conflicting objectives such as efficiency, robustness, energy consumption, and delivery reliability.
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    Fast Pareto Set Approximation for Multi-objective Flexible Job Shop Scheduling via Parallel Preference-conditioned Graph Reinforcement Learning

    Chupeng Su, Cong Zhang, Chuang Wang, Weihong Cen, Gang Chen, Longhan Xie
    Swarm and Evolutionary Computation, 88, 101605
    Journal
    We develop a preference-conditioned graph reinforcement learning framework for fast Pareto set approximation in multi-objective flexible job shop scheduling. The method parallelizes preference-based learning and substantially improves both solution quality and computational speed on large-scale scheduling problems.

    Evolution Strategies-based Optimized Graph Reinforcement Learning for Solving Dynamic Job Shop Scheduling Problem

    Chupeng Su, Cong Zhang, Dan Xia, Baoan Han, Chuang Wang, Gang Chen, Longhan Xie
    Applied Soft Computing, 145, 110596
    Journal
    We propose a graph reinforcement learning framework with evolution strategies for dynamic job shop scheduling under machine breakdown and stochastic processing times. The approach formulates the scheduling problem as an MDP and learns robust policies that outperform reinforcement-learning-based and traditional baselines.

    Correct-by-Construction Approach for Self-Evolvable Robots

    Gang Chen, Zhaodan Kong
    Proceedings of the ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE 2017)
    Conference
    We introduce a formal modeling and design framework for self-evolvable robots that supports both structural and functional reconfiguration. The approach uses reconfiguration grammars and a correct-by-construction synthesis strategy to generate robot designs guaranteed to satisfy task specifications in a given workspace.
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