柔性制造环境下的车间生产调度问题具有复杂性、不确定性、多约束等特点，是近年来生产管理和组合优化领域的重点和难点课题。本项目旨在应用运筹学、人工智能和Holonic制造系统（HMS)等学科相关理论和方法，采用分群策略研究不确定条件下的柔性Flow Shop生产调度问题，具有前沿性和探索性。主要研究内容是：分析HMS体系结构和运行机制，建立柔性Flow Shop的Holonic调度模型；依据随机特性分群的思想，研究基于聚类算法的资源Holon（加工机器）自组织机制；采用机器学习和仿真方法，建立Holon群随机特性的预测模型，实现不同生产环境下调度方法的自适应选择；引入基于数据挖掘的种群更新策略，探讨元启发式优化算法对资源Holon自治调度问题的求解效率；采用基于Q-学习和合同网协议的协商调度方法，提高资源Holon动态环境下的协调能力。研究成果将为解决不确定条件下的生产调度问题提供创新方法。

Scheduling in flexible manufacturing environment is characterized by high complexity, inherent uncertainty, and multi-constraint. As an NP-hard combinatorial optimization problem, it has attracted considerable attention of the researchers in both academia and industry. By incorporating operational research and artificial intelligence techniques, this project aims to develop a holonic scheduling model to solve flexible flow shop (FFS) problems under uncertainties. This project mainly covers the following research topics: (1) establishing a holonic scheduling architecture for FFS problems under uncertainties; (2) developing a cluster-based self-organization mechanism to generate holon clusters with different stochastic nature; (3) developing a procedure of approach assignment based on machine learning and simulation techniques to measure the stochastic nature of each holon cluster, and accordingly assigning a suitable approach for schedule generation; (4) establishing a data-mining-based chromosome generation mechanism to improve the performance of population-based meta-heuristic; (5) integrating Q-learning with Contract Net Protocol to provide better adaptability and responsiveness in the face of disturbances. The proposed holonic scheduling model of this project provides a promising methodology to solve FFS scheduling problems under uncertainties, and hence offers great academic and practical potential in real-world production scheduling.