基于改进遗传算法的Job-shop调度问题

Jobshop Scheduling Problems Based on Improved Genetic Algorithm

建立了作业调度问题的模型,阐明了遗传算法是一种有效的全局随机优化方法,并将遗传算法用于Job-shop调度问题的研究中.针对标准遗传算法计算费时、稳定性差等不足,从适应度尺度变换、稳态繁殖、自适应遗传参数等方面作了改进.给出了基于改进遗传算法的模型求解方法和步骤.经过实例计算,取得了良好的调度效果,表明该方法可为制定工程装备作战保障的指挥自动化决策提供科学、有效的支持.

In order to find global optimal results efficiently in Job-shop, traditional GAs were improved and used to study this problem. Genetic algorithm had disadvantages of slow convergence and poor stability in practical engineering. To overcome these problems, an improved genetic algorithm was proposed in terms of genetic operators, etc. Besides, the steps to solve the optimal model were put forward. With this model, ideal results were obtained. This shows that the method can offer a scientific and effective support for a decision maker in command automation of the engineering equipment＇s rush-repairs in battlefield.