Solving Method for Task Scheduling with Unknown and Sequence-Dependent Adjustment Time for Machines

Abstract

The invention discloses a method for solving task scheduling with unknown machine adjustment time and sequence correlation, including: constructing a feature variable set and a prediction model related to machine adjustment time before task processing; determining the constraints of the problem to be solved, and performing the task according to the actual characteristics of the problem Encoding to get a feasible solution to the problem; Randomly generate the initial scheduling sequence of tasks, predict the machine adjustment time of each task under the current task sequence, and calculate the objective function value under the initial scheduling sequence; generate a new task scheduling sequence, and then adjust the current task sequence Predict the machine adjustment time of each task, and calculate the objective function value under the new task sequence; through the comparison of the objective function increment, select the initial task sequence solution in the next iteration, and repeat the iteration until the preset termination condition is met. Finally, the optimal scheduling sequence of tasks is obtained. The invention can quickly realize the optimal solution of task scheduling 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

Images