变批量车间调度文化进化算法研究及应用

针对JIT环境下柔性生产的要求,多品种小批量生产使车间调度规划更加困难,许多生产调度优化问题属于NP-hard问题,其求解通常采用智能启发式算法。文化进化算法基于文化算法、文化进化思想及超群进化思想,通过上层文化空间的经验知识指导下层个体进化搜索的方向及步长,通过模拟人类社会文化进化的机制实现文化空间的进化与更新,最后将算法应用到变批量Flowshop问题的求解,用Matlab编程仿真测试,结果表明用此算法进行批量分割,解决生产调度优化问题是可行的,而且其搜索性能优于AGA算法。

Research on Cultural Evolutionary Algorithms Applied to Batch-changeable Scheduling Problems

Small-scale and variety production makes job shop scheduling even more difficult.Most of production scheduling optimization problems are NP-hard problems,whose solutions are usually obtained through using the heuristic algorithm.Using the cultural algorithm and the cultural evolution idea,the cultural evolutionary algorithm(CEA) guides the direction and steplength of the lower-layer individual′s evolutionary search through the empirical knowledge of the upper-layer cultural space and evolves and updates the cultural space through simulating the human cultural evolution mechanism.Finally the algorithm is applied to solving batch-changeable flow shop problems.It is also simulated with Matlab,and the simulation results show that it is feasible for the CEA to separate batches and solve production scheduling optimization problems.Its search performance is better than the adaptive genetic algorithm.