An integrated production and maintenance planning model under VP-T2 Hotelling chart. (April 2018)

• Record Type: Journal Article
• Authors: Salmasnia, Ali; Kaveie, Maryam; Namdar, Mohammadreza
• Is Part Of: Computers & industrial engineering. Volume 118(2018)
• Page Start: 89
• Page End: 103
• Publication Date: 2018-04
• Subjects: Economic production quantity -- Maintenance policy -- Statistical process monitoring -- VP-T2 Hotelling chart -- Multiple quality characteristics; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.02.021
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: An integrated production and maintenance planning model under VP-T 2 Hotelling chart is proposed. The applied VP-T 2 Hotelling chart monitors multivariate quality characteristics in an imperfect production processes. The expected cost of production cycle is minimized subject to statistical quality constraints. Abstract: Economic production quantity, maintenance policy, and statistical process monitoring are three interrelated concepts in the optimization of production process, while they have usually been analyzed separately in the literature. Moreover, the existing studies integrating these three concepts suffer from three significant drawbacks as follows: (1) using control charts with fixed parameters to monitor the process, while they are slow in detecting small and middle shifts; (2) monitoring one quality characteristic, whereas in real condition, several quality characteristics of the process may be considered; (3) design of control charts by minimizing the quality cost ignoring statistical properties that decrease the power of the control chart significantly. To overcome the aforementioned disadvantages, this study presents an integrated production and maintenance planning model that applies a control chart with variable parameters. This control chart, the VP-T 2 Hotelling chart, is used to monitor multiple quality characteristics. According to the complexity of the problem, the expected total cost per time unit is optimized by the particle swarm optimization algorithm subject to statistical quality constraints. Finally, to show the effectiveness of the suggested approach, two comparative studies are presented. The first one confirms that integration of production planning, maintenance policy, and statistical process monitoring leads to a significant increase in the cost savings. The latter results demonstrate that applying the VP chart improves the cost cycle in comparison to the chart with fixed parameters.