Robust Integrated Optimization Of Production Scheduling And Maintenance Planning Based On Improved Gray Wolf Algorithm

In classical scheduling problems,it is assumed that the machine is always available.However,working for a long time will reduce the reliability of the machine.In serious cases,the machine may break down suddenly,leading to production interruption,resulting in huge economic losses,and in serious cases,causing human casualties.In order to avoid the above-mentioned problems,it is necessary to carry out preventive maintenance of the machine and reduce the failure probability.On the other hand,preventive maintenance needs to occupy a certain production time.Only by integrating production scheduling and maintenance planning optimization,and arranging preventive maintenance in a reasonable time,can the production efficiency of enterprises be improved under the premise of ensuring the reliability of equipment.At the same time,preventive maintenance can not eliminate the occurrence of machine failure,failure maintenance will occupy the production time,resulting in production delays.Therefore,in order to guarantee the high efficiency,the stability and the machine reliability,it is necessary to integrate the production scheduling,the preventive maintenance and the fault maintenance together to carry on the robust integration optimization.This paper mainly includes the following work:First,the Shandong Bar integration optimization problem includes three decision variables: The job sequence,the preventive maintenance location and the insertion idle time,which are related to the production scheduling,the preventive maintenance and the robustness of the system.On this basis,a two-stage decomposition mathematical model is designed to solve the problem that can not be solved by the traditional robust integrated optimization model.Compared with the traditional model,considering the characteristics of machine failure function,three variables are decided by stages.Secondly,a robust integrated optimization framework is proposed to realize the synchronous decision of the above three variables.The robust integrated optimization framework is divided into two layers: the outer layer determines the processing sequence and the preventive maintenance position,and the inner layer optimizes the insertion idle time.Thirdly,an outer loop algorithm based on neighborhood search is designed.By constructing the neighborhood of the initial processing sequence to search for the new processing sequence,the preventive maintenance position corresponding to the new processing sequence is determined.Finally,the effectiveness and superiority of the improved algorithm are proved through the large-scale case comparison experiment,and the improved algorithm obtains the optimal solution and the average solution with better performance in the large-scale case application,the insertion idle time obtained is more robust.Then,an inner loop algorithm is designed to optimize the insertion idle time.The inner loop algorithm includes two innovative steps: First,an initialization heuristic rule based on critical path is proposed to generate the initial solution of idle time and improve the performance of the initial solution;Enhanced communication between low-ranking Wolf Packs.Finally,through the large-scale case comparison experiment,the validity and superiority of the improved algorithm are proved.The improved algorithm obtains the superior optimal solution and the average solution in the large-scale case application,and the solution is more robust.