一种求解柔性作业车间调度问题的改进灰狼优化算法

An improved grey wolf optimization for solving flexible job shop scheduling problem

针对柔性作业车间调度问题,以最大完工时间最小化为目标提出了一种改进灰狼优化算法(IGWO),采用两段式的编码方式来构造算法个体位置与调度方案之间的关系,使用基于启发式规则的初始化种群方法来提高初始解的质量.为了平衡算法的全局搜索与局部搜索,提出一种基于双曲正切函数的非线性收敛因子公式,并在算法的个体更新阶段提出了一种基于适应度值的加权方法,在算法决策层嵌入了变邻域搜索算法.通过仿真实验表明,算法在求解柔性作业车间调度问题上是有效的.

This paper focuses on the flexible job shop scheduling problem and proposes an improved gray wolf optimization(IGWO)algorithm,with the goal of minimizing the maximum completion time.A two-phase coding method is used to construct the relationship between the individual locations and the scheduling scheme.The initial population method based on the heuristic rule is used to improve the quality of initial solution.In order to balance global search and local search,a hyperbolic-tangent-function-based non-linear convergence factor formula is proposed,in the individual update stage of the algorithm,a weighting method based on fitness value is proposed,the variable neighborhood search algorithm is embedded into the decision-making layer of the algorithm.Simulation results show that the algorithm is effective in solving the flexible job shop scheduling problem.