A two-stage integrating optimization of production scheduling, maintenance and quality. (September 2020)

• Record Type: Journal Article
• Authors: Zheng, Jiafei; Yang, Hongbing; Wu, Qingsong; Wang, Ziyang
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 234:Number 11(2020)
• Page Start: 1448
• Page End: 1459
• Publication Date: 2020-09
• Subjects: Production scheduling -- preventive maintenance -- quality -- control chart -- genetic algorithm; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405420921733
• Languages: English
• ISSNs: 0954-4054

Abstract

During the process of actual production, production scheduling, preventive maintenance and quality control have a close interaction with each other and thus need to be integrated for improving manufacturing system performance. The aim of this article is to jointly optimize the job sequence, the preventive maintenance locations, the preventive maintenance interval and decision variables of control chart such that the expected cost per unit time and penalty cost due to schedule delay are minimized. A two-stage integrating optimization model of production scheduling, maintenance and quality in a single machine system is developed. A control chart is adopted to monitor the whole process with Taguchi's loss function estimating the quality cost of each condition and perfect preventive maintenance activity is conducted at the end of each interval. A genetic algorithm is designed to solve this model in order to obtain the optimal job sequence and the locations of preventive maintenance. Numerical examples and thorough sensitivity analyses are provided to illustrate the proposed integrated model. Finally, the efficiency of the proposed two-stage integration model is verified by the comparison of the stand-alone model under the same condition. Compared with the stand-alone model, the proposed integrated method can save the cost rate by 12%–42% under five different situations.