柔性作业车间AGV与机器双资源集成调度研究

针对含有AGV（automated guided vehicle）的柔性作业车间调度问题，建立了以最小化最大完工时间为目标的双资源集成调度优化模型。在种群初始化过程中提出一种启发式初始化方法，提高种群初始解的质量，加快算法的收敛速度。针对离散粒子群算法易早熟的弊端，结合竞争学习机制和随机重启机制提出一种可有效避免早熟的混合离散粒子群优化算法。在考虑工件运输的柔性作业车间调度的基准数据集上做仿真实验，结果表明启发式初始化方法和混合离散粒子群算法求解此类问题时可行高效。

Research on Integrated Scheduling of AGV and Machine in Flexible Job Shop

Aiming at the flexible job shop scheduling problem with AGV (automated guided vehicle), a dual resource integrated scheduling optimization model with the objective of minimizing makespan is established. In the process of population initialization, a heuristic initialization method is proposed to improve the quality of population initial solution and accelerate the convergence speed of the algorithm. A hybrid discrete particle swarm optimization algorithm that can effectively avoid premature maturation is proposed by combining the competitive learning mechanism and the random restart mechanism to address the disadvantages of discrete particle swarm algorithms that are prone to premature maturation. Simulation experiments are carried out on the baseline data set of flexible job shop scheduling considering job transport. The results show that heuristic initialization method and hybrid discrete particle swarm optimization algorithm are feasible and efficient in solving such problems.