Task scheduling solving method aiming at unknown machine adjustment time and sequence correlation

Abstract

The invention discloses a task scheduling solving method for unknown machine adjustment time and sequence correlation. The task scheduling solving method comprises the following steps: constructing a characteristic variable set related to machine adjustment time before task processing and a prediction model; determining constraints of a to-be-solved problem, and performing coding according to actual characteristics of the problem to obtain a feasible solution of the problem; randomly generating an initial scheduling sequence of tasks, predicting the machine adjustment time of each task in the current task sequence, and calculating an objective function value in the initial scheduling sequence; generating a new task scheduling sequence, predicting the machine adjustment time of each task in the current task sequence again, and calculating an objective function value in the new task sequence; and through comparison of target function increments, selecting an initial task sequence solution during next iteration, and carrying out repeated iteration until a preset termination condition is met, so as to finally obtain an optimal scheduling sequence of the tasks. According to the method, optimization solution of task scheduling can be quickly achieved for task scheduling with unknown machine adjustment time and sequence correlation.

Description

technical field [0001] The invention relates to the field of task scheduling solutions, in particular to a hybrid algorithm combining an integrated learning algorithm and a heuristic algorithm, which is suitable for solving task scheduling problems with unknown machine adjustment time and sequence correlation. Background technique [0002] Manufacturing is a pillar industry of the national economy, and most manufacturing industries have begun to transform and upgrade in the direction of smart manufacturing and smart factories. With the continuous improvement of the informatization of enterprise workshop equipment, the data that can be used for production optimization analysis continues to increase, providing data support for researchers and operators to grasp the uncertain factors in the production process. [0003] In the production process, one of the larger uncertain factors is the machine adjustment time before the task processing, such as the "working condition" time in...

AI Technical Summary

Problems solved by technology

At present, more research and applications mostly set prediction and optimization as two separate modules, and the solution idea is to complete the prediction first, and then optimize, and there is little connection between the two stages

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