Integrated Multi-objective Optimization of Predictive Maintenance and Production Scheduling: Perspective from Lead Time Constraints

Abstract

For the integrated optimization of job-shop production scheduling and predictive maintenance, this paper fully considers such constraints as product delivery time and changing machine failure rate, and establishes a multi-objective optimization model aiming to minimize the processing cost and the product processing time. The model includes the changing machine failure rate into the integrated optimization of job-shop production scheduling and predictive maintenance, and enables the prediction of the machine state according to the processing time of the current job, laying the basis for the decision-making of the machine activity and the reasonable and effective production planning. In addition, the non-dominated sorting genetic algorithm (NSGA)-II was designed to solve the proposed model. The algorithm performance was improved through the operator crossover and mutation by the simulated binary crossover algorithm (SBX). The proposed strategy was verified through a case study.