A joint determination of production cycle length, maintenance policy, and control chart parameters considering time value of money under stochastic shift size

Document Type : Article

Authors

Department of Industrial Engineering, Faculty of Engineering and Technology, University of Qom, Qom, Iran.

Abstract

Statistical process monitoring, maintenance policy, and production cycle length usually have been investigated separately while they are three dependent aspects in the industrial systems. Moreover, most of the papers that integrated simultaneously these aspects, suffer from three major drawbacks as follows: (1) Optimizing the production cost without considering the time value of money to simplify the model; (2) Considering the fixed shift size while it is a random variable in the real condition; (3) Economic design of control charts ignoring the statistical properties that lead to reduce the control chart power, extremely. To eliminate these weaknesses, this paper presents an integrated model of production cycle length, maintenance policy, and economic-statistical design considering the time value of money and the stochastic shift size. Furthermore, to maintain the reliability of the system at an acceptable level, the presented model uses non-uniform sampling. Finally, three comparative studies on the main contributions are presented to illustrate the advantages of the model and a sensitivity analysis is implemented on the several parameters to extend insights into the matter.

Keywords

Main Subjects


References:
1. Ben-Daya, M. “The economic production lot-sizing problem with imperfect production processes and imperfect maintenance”, International Journal of Production Economics, 76, pp. 257-264 (2002).2. Ben-Daya, M. “Integrated production maintenance and quality model for imperfect processes”, IIE transactions, 31, pp. 491-501 (1999).
3. Cheng, L., Tsou, C.S. and Yang, D.Y. “Cost-service tradeoff analysis of reorder-point-lot-size inventory models”, Journal of Manufacturing Systems, 37, pp. 217-226 (2015).
4. Zhou, X., Wu, C., Li, Y. and  Xi, L. “A preventive maintenance model for leased equipment subject to internal degradation and external shock damage”, Reliability Engineering & System Safety, 154, pp. 1-7 (2016).
5. Costa, A.F. and Rahim, M.A. “Economic Design of and R Charts Under Weibull Shock Models”, Communications in Statistics-Theory and Methods, 42, pp. 3902-3925 (2013).‏
6. Gan, S., Zhang, Z., Zhou, Y. and Shi, J. “Joint optimization of maintenance, buffer, and spare parts for a production system”, Applied Mathematical Modelling, 39, pp. 6032-6042 (2015).
7. Wen, D., Ershun, P., Ying, W. and Wenzhu, L. “An economic production quantity model for a deteriorating system integrated with predictive maintenance strategy”, Journal of Intelligent Manufacturing, 27, pp. 1323-1333 (2016).
8. Rahim, M.A. and Ben-Daya, M. “Joint determination of production quantity, inspection schedule, and quality control for an imperfect process with deteriorating products”, Journal of the Operational Research Society, 52, pp. 1370-1378 (2001).
9. Cheng, J.C. and Chou, C.Y. “A real-time inventory decision system using Western Electric run rules and ARMA control chart”, Expert Systems with Applications, 35, pp. 755-761 (2008).
10. Xiang, Y. “Joint optimization of control chart and preventive maintenance policies: a discrete-time Markov chain approach”, European Journal of Operational Research, 229, pp. 382–390 (2013).
11. Wu, J. and Makis, V. “Economic and economic-statistical design of a chi-square chart for CBM”, European Journal of operational research, 188, pp. 516-529 (2008).
12. Makis, V. and Fung, J. “An EMQ model with inspections and random machine failures”, Journal of the Operational Research Society, 49, pp. 66-76 (1998).
13. Jiang, Y., Chen, M. and Zhou, D. “Joint optimization of preventive maintenance and inventory policies for multi-unit systems subject to deteriorating spare part inventory”, Journal of Manufacturing Systems, 35, pp. 191-205 (2015).
14. Salmasnia, A., Kaveie, M. and Namdar, M. “An integrated production and maintenance planning model under VP-T2 Hotelling chart”, Computers & Industrial Engineering, 118, pp. 89-103 (2018).
15. Bouslah, B., Gharbi, A. and Pellerin, R. “Integrated production, sampling quality control and maintenance of deteriorating production systems with AOQL constraint”, Omega, 61, pp. 110-126 (2016).
16. Lin, Y.H., Chen, Y.C. and Wang, W.Y. “Optimal production model for imperfect process with imperfect maintenance, minimal repair and rework”, International Journal of Systems Science: Operations & Logistics, pp. 1-12 (2016).
17. Beheshti Fakher, H., Nourelfath, M. and Gendreau, M. “A cost minimization model for joint production and maintenance planning under quality constraints”, International Journal of Production Research, pp. 1-14 (2016).
18. Ben-Daya, M. and Makhdoum, M. “Integrated production and quality model under various preventive maintenance policies”, Journal of the Operational Research Society, 49, pp. 840-853 (1998).
19. Nourelfath, M., Nahas, N. and Ben-Daya, M. “Integrated preventive maintenance and production decisions for imperfect processes”, Reliability Engineering and System Safety, 148, pp. 21-31 (2016).
20. Luciano, E. and Peccati, L. “Capital structure and inventory management: The temporary sale price problem”, International Journal of Production Economics, 59, pp. 169-178 (1999).
21. Van der Laan, E. “An NPV and AC analysis of a stochastic inventory system with joint manufacturing and remanufacturing”, International Journal of Production Economics, 81, pp. 317-331 (2003).
22. Disney, S.M., Warburton, R.D. and Zhong, Q.C. “Net present value analysis of the economic production quantity”, IMA Journal of Management Mathematics, 24(4), pp. 423-435 (2013).‏
23. Lin, R., Lin, J.S.J., Chen, K. and Julian, P.C. “Note on inventory model with net present value”, Journal of Interdisciplinary Mathematics, 10, pp.587-592 (2007).‏
24. Beullens, P. and Janssens, G.K. “Adapting inventory models for handling various payment structures using net present value equivalence analysis”, International Journal of Production Economics, 157, pp. 190-200 (2014).‏
25. Duncan, A.J. “The economic design of X charts used to maintain current control of a process”, Journal of the American Statistical Association, 51, pp. 228–242 (1956).
26. Saniga, E.M. “Economic statistical control-chart designs with an application to X and R charts”, Technometrics, 31, pp. 313–320 (1989).
27. Nenes, G., Tasias, K.A. and Celano, G. “A general model for the economic-statistical design of adaptive control charts for processes subject to multiple assignable causes”, International Journal of Production Research, 53, pp. 2146-2164 (2015).
28. Yin, H., Zhang, G., Zhu, H., Deng, Y. and He, F. “An integrated model of statistical process control and maintenance based on the delayed monitoring”, Reliability Engineering and System Safety, 133, pp. 323-333 (2015).
29. Pan, E., Jin, Y., Wang, Sh. and Cang, T. “An integrated EPQ model based on a control chart for an imperfect production process”, International Journal of Production Research, 50, pp.6999-7011 (2012).
30. Salmasnia, A., Abdzadeh, B. and Namdar, M. “A joint design of production run length, maintenance policy and control chart with multiple assignable causes”, Journal of Manufacturing Systems, 42, pp. 44-56 (2017).
31. Wu, Z., Shamsuzzaman, M. and Wang, Q. “The cost minimization and manpower deployment to SPC in a multistage manufacturing system. International Journal of Production Economics”, 106, pp.275-287 (2007).‏
32. Celano, G., De Magalhães, M.S., Costa, A.F. and Fichera, S. “A stochastic shift model for economically designed charts constrained by the process stage configuration”, International Journal of Production Economics, 132, pp.315-325 (2011). ‏
33. El-Kassar, A.N., Salameh M., and Bitar, M. “Effects of time value of money on the EPQ Model with the imperfect quality items of Raw material”,  Proceedings of Academy of Information and Management Sciences, New Orleans, 16 (1) , pp.11-18 (2012).
34. Faraz, A. and Saniga, E. “Economic statistical design of a T2 control chart with double warning lines”, Quality and Reliability Engineering International, 27, pp. 125-139 (2011).
35. Bashiri, M., Amiri, A., Doroudyan, M.H. and Asgari, A. “Multi-objective genetic algorithm for economic statistical design of control chart”, Scientia Iranica, 20, pp. 909-918 (2013).‏
36. Chih, M., Yeh, L.L. and Li, F.C. “Particle swarm optimization for the economic and economic statistical designs of the control chart”, Applied Soft Computing, 11, pp. 5053-5067 (2011).
37. Talbi, E.G. “Metaheuristics from design to implementation”, John Wiley & Sons, Inc., (2009).
38. Niaki, S.T.A., Malaki, M. and Ershadi, M.J. “A particle swarm optimization approach on economic and economic-statistical designs of MEWMA control charts”, Scientia Iranica, 18, pp. 1529-1536 (2011).‏
39. Hajinejad, D., Salmasi, N. and Mokhtari, R. “A fast hybrid particle swarm optimization algorithm for flow shop sequence dependent group scheduling problem”, Scientia Iranica, 18, pp. 759-764 (2011).‏
40. Kennedy, J., Eberhart, R.C. and Shi, Y. “Swarm intelligence”, San Francisco, Morgan Kaufmann Publishers, (2001).
41. Perez, R.E. and Behdinan, K. “Particle swarm approach for structural design optimization”, Computers & Structures, 85, pp. 1579-1588 (2007).
42. Lee, B.H. and Rahim, M.A. “An integrated economic design model for quality control, replacement, and main
43. Safaei, A.S, Kazemzadeh R.B. and Gan H.S. “Robust economic-statistical design of X-bar control chart”, International Journal of Production Research, 53, pp. 4446-4458 (2015).
44. Seif, A., Faraz A. and Sadeghifar M. “Evaluation of the economic statistical design of the multivariate T2 control chart with multiple variable sampling intervals scheme: NSGA-II approach”, Journal of Statistical Computation and Simulation, 85, 2442-2455 (2015).
Volume 27, Issue 1
Transactions on Industrial Engineering (E)
January and February 2020
Pages 427-447
  • Receive Date: 10 December 2017
  • Revise Date: 20 May 2018
  • Accept Date: 16 July 2018