A Mixed Swarm Intelligent Optimization Method for Distributed Blocking Pipeline Scheduling

Abstract

The invention discloses a mixed group intelligent optimization method for distributed blocking pipeline scheduling, which includes: performing cooperative initialization on multiple factories and multiple workpieces to generate a plurality of first factory workpiece processing sequences; calculating the processing sequence of each first factory workpiece The adaptive parameters of the sequence are adjusted according to the adaptive parameters to generate multiple workpiece processing sequences of the second factory; multiple workpiece processing sequences of the third factory are generated through local search and adjustment within the factory and between factories; some of the workpiece processing sequences of the third factory are selected for processing Regeneration mechanism, updating multiple third factory workpiece processing sequences to determine the current optimal factory workpiece processing sequence; return to the second step to iteratively update the current optimal factory workpiece processing sequence until the preset iteration termination condition is met to output the optimal Optimal factory workpiece processing sequence. This method combines adaptive search and local search, so that individuals in the population can adjust the search range by themselves, and balance the coarse search and fine search capabilities of the algorithm.

Description

technical field [0001] The invention relates to the technical field of pipeline scheduling, in particular to a mixed group intelligent optimization method for distributed blocking pipeline scheduling. Background technique [0002] Manufacturing is the foundation of the national economy. With the continuous development of modern science and technology, reducing production costs, shortening production cycles, reducing resource consumption, and improving product quality have become the key for enterprises to gain an active position in the fierce market competition. Manufacturing not only affects the economic benefits of enterprises, but also has a huge impact on the living standards of the people and even the comprehensive strength of the country. Production scheduling is the planning and optimization of the manufacturing process, so as to maximize the use of production resources and improve production quality. It is the core link of the entire manufacturing process. An effici...

AI Technical Summary

Problems solved by technology

[0006] However, traditional blocking pipeline scheduling has complex characteristics such as constraints, dynamics, and large-scale nature. In addition, in a distributed environment, not only the sequencing of workpieces needs to be considered, but also the allocation of factories needs to be optimized, which makes the difficulty of the problem increase sharply.

Existing scheduling algorithms are difficult to be widely used in practice due to their own defects

For example, the heuristic algorithm has limited optimization ability, the tabu search algorithm has a slow convergence speed, and the greedy iterative algorithm is easy to fall into local minima, etc. In addition, most algorithms also have problems such as high time complexity, large space complexity, and poor robustness.

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