Joint optimization of preventive maintenance and production scheduling for parallel machines system

This paper deals with the joint problem of production scheduling and maintenance planning as it becomes one important base for intelligent manufacturing. Due to the dual requirements both on cost and delivery date, a multi-objective optimization approach is proposed for parallel machines to allow decision makers to find compromise solution between production scheduling and maintenance planning: minimizing both maximum completion time and total maintenance cost. The new optimization approach is one multiple nondominated improved NSGA-II algorithm based on greedy idea and pre-distribution thinking, which can quickly and effectively seek Pareto optimal solution to solve the joint problem. As well as the introduced idea of averaging machine utilization, the job processing time model is constructed by considering machine degradation to meet real manufacturing situation. Moreover, a penalty function to maintenance (delay or advance) is proposed. Finally, the experimental analysis demonstrates the effectiveness and efficiency of this pre-distributed NSGA-II optimization approach, which could help solve the joint decision-making problem of production scheduling and preventive maintenance for parallel machines system.