Research On Batch Optimal Scheduling Of Parallel Machines Considering Machine Adjustment Time

In order to adapt to the changing market environment and the individualization of customer needs,more and more manufacturing enterprises have begun to pay attention to the rational allocation of production resources and the study of shop scheduling problems.With the continuous deepening of the research on the scheduling problem of the workshop,the parallel machine scheduling model with single constraint condition and workpiece production and processing in a single or whole batch is no longer in line with the workshop production under actual working conditions.To this end,this paper fully considers the constraints and influence factors of production and processing in parallel workshops under actual working conditions,and studies the batching scheme and sub-batch allocation and sequencing of parallel machine batch scheduling problems.The main work contents are as follows:Based on the correlation model of classical parallel machine scheduling problem,a parallel machine batch scheduling model which considers machine adjustment time is established.The model regards minimize the maximum completion time as the optimization goal,and the non-equivalent parallel machine scheduling model is used as the model basis.The production and processing of the workpieces are batched,focusing on the adjustment time of the machine and the limitation of the number of molds under actual working conditions,and these two factors are constraints of the parallel machine scheduling problem.The model which is built in this paper solves two main problems: namely how to process the workpieces in batches and how to distribute and sort the sub-batch after batching.To this end,referencing to genetic difference hybrid algorithm,this paper designs a hybrid algorithm that combines gray wolf algorithm and differential evolution algorithm to meet the optimization of workpiece batch and sub-batch allocation.At the same time,genetic algorithm,genetic differential evolution hybrid algorithm and graywolf differential evolution hybrid algorithm are used to solve the classical parallel machine scheduling problems of different scales and show the results of three algorithms.Comparing the results,it is proved that the designed grey wolf differential evolution hybrid algorithm is also suiTab for solving the classical parallel machine scheduling model problem.This paper uses genetic differential evolution hybrid algorithm and gray-wolf differential evolution hybrid algorithm to solve the parallel machine batch scheduling problem and the parallel machine batch scheduling problem considering the machine adjustment time under the actual machine condition.The experimental results show that the grey-wolf differential evolution hybrid algorithm is superior to the genetic differential evolution hybrid algorithm in optimization performance.