A Distributed Hybrid Pipeline Scheduling Optimization Method

Abstract

The embodiment of the present invention provides a distributed hybrid pipeline scheduling optimization method, the method includes: initializing at least two scheduling schemes, and determining the factory allocation result of all the workpieces in each scheduling scheme and the workpiece processing sequence of each factory in the initial stage; For each scheduling scheme, the iterative process of determining the processing sequence for each stage after the initial stage according to various scheduling rules is repeated until the preset conditions are met; finally, the scheduling scheme with the smallest delay is obtained for realizing the distributed hybrid pipeline schedule. By repeatedly executing the iterative process of determining the processing sequence for each stage after the initial stage according to various scheduling rules, the algorithm can quickly converge with a low computational complexity, and the scheduling scheme with the smallest total delay time can be obtained, thereby improving the Pipeline scheduling efficiency. In addition, through the double-population divergence search and local enhancement search, the algorithm is further optimized, so as to obtain a scheduling scheme with better total delay time.

Description

technical field [0001] The invention relates to the field of hybrid pipeline analysis, in particular to a distributed hybrid pipeline scheduling optimization method. Background technique [0002] At present, distributed manufacturing and scheduling has become a trend. Distributed manufacturing can make use of the resources and processing conditions of multiple factories or enterprises to realize resource allocation and sharing, and on this basis, speed up the production and manufacturing of products with reasonable transportation and use costs. In the classic flow shop, a group of workpieces goes through multiple production stages according to the processing sequence, and only one machine is running in each stage. Now, in order to increase production capacity and balance the processing capacity of machines between different stages, in some processing stages Multiple machines are introduced to process at the same time, that is, hybrid flow shop scheduling. Mixed production ...

AI Technical Summary

Problems solved by technology

[0004] Due to the large-scale and strong coupling characteristics of the distributed hybrid pipeline scheduling problem, the existing precise algorithm has too much calculation; the heuristic algorithm is not effective, and it is easy to fall into local minima; and the current intelligent optimization algorithm does not address the characteristics of the problem The design has the disadvantages of high computational complexity, slow convergence, and limited optimization effect.

In summary, the current distributed hybrid pipeline scheduling method has the problems of high computational complexity and slow convergence

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