Joint optimization of production, quality control and maintenance for serial-parallel multistage production systems

• Published in: Reliability engineering & system safety Vol. 204; p. 107146
• Main Authors: Cheng, Guoqing, Li, Ling
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.12.2020; Elsevier BV
• Subjects: Average cost rate; Computer simulation; Genetic algorithms; maintenance; Mathematical models; Monte Carlo simulation; Optimization; Preventive maintenance; Product quality; Productivity; Quality control; Reliability engineering; Serial-parallel multistage production system; Simulation-based optimization; Average cost rate; Serial-parallel multistage production system; maintenance; Simulation-based optimization; Quality control
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2020.107146

•A serial-parallel multistage production system is considered.•The system is subject to both reliability and quality deteriorations.•A joint model of production, quality control and maintenance is developed.•Quality information feedback assists maintenance decision-making.•A simulation-based optimization approach is proposed to solve the problem. This paper presents a joint model of production, quality control and preventive maintenance for a serial-parallel multistage production system. In each stage, there are multiple machines to meet the productivity and line-balance requirement. The machines deteriorate with usage and thus influences the product quality. A make-to-stock production policy is adopted to provide protection to stock against uncertainties. All items processed in each stage undergo quality checks. Conforming items are delivered to the next stage for further processing, whereas non-conforming items are scrapped. During production runs, based on the quality information feedback, preventive maintenances are performed to improve the machines reliability and hence the product quality. At the end of production runs, machines are inspected and maintained if necessary. The novelty of the proposed maintenance strategy consists in that both the machine structure importance measure and productivity are considered when selecting machines for maintenance. It aims to simultaneously determine the length of production run, quality control-threshold and maintenance-threshold such that the average cost rate is minimized. A stochastic mathematical model is developed which is solved by a simulation-based optimization approach coupling Monte Carlo Simulation and genetic algorithm. Finally, an illustrative example and some comparisons are provided to demonstrate the model.