Research And Application Of Parallel Machine Scheduling Model With Variable Processing Time Under Different Due Window

The research on production scheduling problems has developed rapidly in the past few decades.Parallel machines,as the link between single machines and complex job shops,play an important role in the field of production scheduling.In the actual production process,due to the aging of the machine,etc.,the job has a deterioration effect,resulting in a longer processing time,and the rate-modifying activity is arranged to eliminate the effect of the deterioration effect.This scheduling problem considering the change of the processing time of the job is of great significance in actual scheduling problem.With the increasing competition among enterprises,advanced management concepts such as JIT production have been widely implemented in domestic enterprises.JIT production targets on-time delivery,requiring jobs to be completed within their respective due window.In the actual production scheduling process,depending on the actual order,considering whether the job needs to be delivered together,the scheduling problem under the due window can be divided into a common due window and multiple due windows.Scheduling under the due window has a large impact on the final scheduling scheme.Therefore,it is of great research value and practical significance to study the parallel machine scheduling problem under different due window.This paper studies the parallel machine scheduling problem.First,taking into account the effects of deterioration in actual production and the impact of rate-modifying activities on the change of job processing time,on the basis of considering a given common due window,the maximum completion time and early/tardiness penalty are used as optimization objectives.A parallel machine scheduling model with variable processing time under the common due window,and an improved hybrid genetic tabu search algorithm is proposed to solve the model.Secondly,consider the scheduling problem under different due window,in which each job has different due window,and introduce the concept of customer satisfaction,set the due window pessimistic value and optimistic value at both ends of the due window.And calculate the customer satisfaction according to the completion time of the job.Therefore,the maximum completion time and customer satisfaction are the optimization objectives.The parallel machine scheduling model with variable processing time under the multiple due windows is constructed,and an improved hybrid discrete differential evolution algorithm is proposed to solve the model.Finally,combined with the actual production background of the enterprise,taking the fastener production of Company LG as an example,analyze the production of heat treatment process and collect relevant processing data,and the two models constructed in this paper are applied and researched,combined with the proposed improved algorithm using Python software to solve,and then compare the two improved algorithms with other intelligent optimization algorithms,and finally prove the effectiveness and superiority of the improved algorithm proposed in this paper.