用于解决生产调度冲突问题的并行快速排序算法

Parallel Fast Sorting Algorithm for Solving the Problem of Production Scheduling Conflict

本文并行快速排序算法对生产调度冲突问题进行解决,构建了BP神经网络,并与并行快速排序算法相结合建立了生产调度冲突问题模型,并利用加工时间条件进行约束后,对生产调度冲突相关数据进行采集,对训练初始目标进行设定,并根据不通过的初始目标设定不同排序规则,将排序规则输入到生产调度冲突问题模型,对生产调度资源样本进行迭代训练,将迭代结果与实际的期望值比较,分析并修正两者之间的误差,将修正后的最佳输出状态排序方法进行输出,避免发生生产调度冲突问题。实验结果表明,本文提出的方法对于生产调度资源数据的调度都可以输出最佳方案,可以对生产调度冲突问题进行有效解决。

The parallel fast sorting algorithm is a kind of combinatorial optimization method, which is widely used in the task allocation of each industry production workshop. In this paper, parallel quick sort algorithm to solve the scheduling conflict problem, BP neural network is built, and combined with the parallel quicksort algorithm establishes a model of conflict problem of production scheduling, and constrained by the conditions of processing time, relevant data were collected on the production scheduling conflicts, the training set the initial target, and set different according to the initial target through the collation, sorting rules model input to production scheduling conflicts, iterative training on production scheduling resource sample, iterative results and actual expected value comparison, analysis and correction of error between the two, the best output ranking method of modified output, to avoid scheduling conflict the problem. The experimental results show that the proposed method can output the optimal scheme for scheduling resource data, and can effectively solve the problem of production scheduling conflict.