Remanufacturing Scheduling Problem With Linear Deterioration Of Equipment Maintenance Time

Remanufacturing is an important direction of the development of China's manufacturing industry.Equipment maintenance is an important factor affecting manufacturing.The integrated optimization of production scheduling and equipment maintenance can effectively guide the implementation of scheduling plan,and improve the production efficiency of remanufacturing enterprises.This paper takes remanufacturing as the research background and the replacement flow shop scheduling problem as the research object.Considering the uncertainty of processing time in remanufacturing,this paper studies the integrated optimization problem of production scheduling and equipment maintenance with deteriorating maintenance time,aiming at making scheduling and preventive maintenance plans reasonably.A mathematical programming model is established,in which the processing time is represented by triangular fuzzy number,and the objective function of the model is to minimize the maximum completion time.In this study,considering deteriorating maintenance time and assuming that the maintenance time is related to the equipment continuous operation time of linear function,the analysis found that the linear function of different constant term,the optimal preventive maintenance time is different.The related properties and theorems are proposed and proved about this discovery,which provides a basis and reference for equipment preventive maintenance strategy.Based on the above research of the problem,an improved intelligent optimization algorithm is designed with the framework of genetic algorithm.Firstly,the proposed preventive maintenance strategy is used as a heuristic rule to design the coding scheme.Then,evolutionary methods such as sequential crossover,reverse mutation and elite retention strategy are adopted.Finally,Tabu-Search is performed on the optimal individual of each generation,and the local search ability of the algorithm is improved through adaptive selection of neighborhood space.In this paper,experiments have been done.The objective function values of the initial solutions generated by random rules and heuristic rules are compared,and the comparison results show the superiority of the heuristic rules proposed in this paper.The effectiveness of the improved algorithm is demonstrated by the example experiments of different job scales.The results proved that the algorithm and model in this paper can provide decision support for decision maker's scheduling and preventive maintenance plan.