An integrated model for joint determination of production run length, adaptive control chart parameters and maintenance policy

ABSTRACT

The most existing models for determining the production run length assume that the production process is perfect, which means quality and machine deterioration never happen. To be closer to more realistic situation, an imperfect process is considered in which an adaptive $\bar{X}$ control chart with variable sampling interval is applied to monitor the quality characteristic of process. In addition, to reduce the failure rate of machine, two kinds of maintenance including reactive maintenance and preventive maintenance are considered. Then, the particle swarm optimization algorithm is used to minimize the total cost per production cycle subject to statistical quality constraints. Also a comparative study is accomplished to illustrate the effect of employing control chart with variable sampling interval on total cost. Finally a sensitivity analysis is conducted to extend insights into the expected total cost per production cycle.

KEYWORDS:

• Production run length
• economic-statistical design
• maintenance policy
• adaptive control chart
• PSO algorithm

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Ali Salmasnia

Ali Salmasnia is currently an Associate Professor in University of Qom, Qom, Iran. His  research  interests  include  quality  engineering,  reliability, applied multivariate  statistics  and  multi-criterion  decision  making. He is the author or co-author of various papers published in Journal of Manufacturing Systems, Computers and Industrial Engineering, Applied Soft Computing, Neurocomputing, Applied Mathematical Modelling, Expert Systems with Applications, Neural Computing and Applications, IEEE Transactions on Engineering Management, International Journal of Information Technology and Decision Making, TOP, Quality and Reliability Engineering International, Journal of information Science, Communications in Statistics-Simulation and Computation, International Journal of Advanced Manufacturing Technology, Arabian Journal for Science and Engineering and Scientia Iranica.

Farzaneh Soltani

Farzaneh Soltani received her MS degree in Industrial Engineering from the University of Qom in Iran. Her current research interests include quality engineering, maintenance and multi-criterion decision making.

Elham Heydari

Elham Heydari received her MS degree in Industrial Engineering from the University of Qom in Iran. Her current research interests include quality engineering, maintenance and multi-criterion decision making.

Samira Googoonani

Samira Googoonani is a MS student in Department of Industrial Engineering, Faculty of Engineering, University of Qom. Her current research interests include quality engineering, reliability and production planning.