Genetic algorithm approach to constructing flexible production schedules

1、基于搜索空间分解的大规模调度问题求解方法，当问题包含多个变量时，GA中的个体用多个染色体表示。2、提出了一种灵活处理问题约束的遗传算法构造方法定义了个体描述，设计了遗传算子，使问题在复杂约束条件下得到可行解.一种基于遗传算法的重调度方法当问题包含约束变化时，构造初始种群，使重调度问题易于求解。本文提出了一种具有多种约束条件的大型作业车间调度问题的最优调度方法。带附加约束的大规模作业车间调度问题。5、多台并行机流水作业的优化调度针对复杂的流水作业调度问题，将一些调度规则与传统的遗传算子相结合，设计了遗传算法。

1, A method for solving large-scale scheduling problems based on search space decomposition.When the problem includes many variables, the individual in GA is represented in terms of plural chromosomes. Consequently the search space is decomposed and the optimal solution is obtained efficiently by applying heuristics to the search in the decomposed space.2, A method for constructing GA to treat the problem constraints flexiblyThe individual description is defined and the genetic operators are designed so as to obtain a feasible solution for the problem with complicated constraints.3, A method of rescheduling by using GAWhen the problem includes constraint variations, the initial population is constructed so as to solve a rescheduling problem easily.4, An optimal schedule for large-scale job shop processes with various constraintsThe individual description and introduction of heuristics are proposed for solving large-scale job shop scheduling problems with additional constraints.5, An optimal schedule for flow shop processes with parallel machinesFor treating complicated flow shop scheduling problems, GA is designed by combining some dispatching rules with the conventional genetic operators.