Maintenance Scheduling Optimization in a Multiple Production Line Considering Human Error
Pages 655 - 666
Abstract
An analytical multiobjective maintenance planning model that maximizes reliability while minimizing cost and human error is proposed. In order to incorporate human error, the model minimizes the maximum human error over the planning horizon. Human Error Assessment and Reduction Technique HEART is used to quantify the human error. Maintenance activities include adjustment and replacement activities, in which each of them consumes a certain amount of human resource, spare parts, and budget and brings about a specified level of reliability and human error. Economic dependence is also considered, in which grouping maintenance activities reduces total cost. However, this may increase human error probability due to operator fatigue or time pressure. The main purpose is to investigate the relationship between human factors and maintenance activities to find the preferred maintenance plan. A multiple production line is considered as a case study. A sensitivity analysis is performed, and the effects of grouping and human factors on the preferred maintenance plan are discussed. It is shown how human proficiency may affect reliability and cost.
References
[1]
Agnihotri, R. K., Singhal, G., &Khandelwal, S. K. 1992. Stochastic analysis of a two-unit redundant system with two types of failure. Microelectronics Reliability, Volume 32 Issue 7, pp.901-904.
[2]
Aju kumar, V. N., &Gandhi, O. P. 2011. Quantification of human error in maintenance using graph theory and matrix approach. Quality and Reliability Engineering International, Volume 27 Issue 8, pp.1145-1172.
[3]
Azadeh, A., Sheikhalishahi, M., Firoozi, M., &Khalili, S. M. 2013. An integrated multi-criteria Taguchi computer simulation-DEA approach for optimum maintenance policy and planning by incorporating learning effects. International Journal of Production Research, Volume 51 Issue 18, pp.5374-5385.
[4]
Azadeh, A., Sheikhalishahi, M., &Monshi, F. 2015. Selecting optimum maintenance activity plans by a unique simulation-multivariate approach. International Journal of Computer Integrated Manufacturing.
[5]
Bris, R., Chatelet, E., &Yalaoui, F. 2003. New method to minimize the preventive maintenance cost of series-parallel systems. Reliability Engineering and System Safety, Volume 82, pp.247-255.
[6]
Cabahug, R. R., Edwards, D. J., &Nicholas, J. 2004. Classifying plant operator maintenance proficiency: Examining personal variables. Building Research and Information, Volume 32 Issue 2, pp.119-127.
[7]
Chen, W., &Huang, S. P. 2014. Human reliability analysis in aviation maintenance by a Bayesian network approach. In G.Deodatis, B. R.Ellingwood, &D. M.Frangopol Eds., Safety, reliability, risk and life-cycle performance of structures and infrastructures pp. pp.2091-2096. Boca Raton, FL: CRC Press.
[8]
Dekker, R., Wildeman, R., &Duyn Schouten, F. 1997. A review of multi-component maintenance models with economic dependence. Mathematical Methods of Operations Research, Volume 45 Issue 3, pp.411-435.
[9]
Dhillon, B. S., &Yang, N. F. 1995. Probabilistic analysis of a maintainable system with human error. Journal of Quality in Maintenance Engineering, Volume 1 Issue 2, pp.50-59.
[10]
Dyer, R. F., &Forman, E. H. 1992. Group decision support with the analytic hierarchy process. Decision Support Systems, Volume 8, pp.99-124.
[11]
Ebrahimipour, V., Najjarbashi, A., &Sheikhalishahi, M. 2015. Multi-objective modeling for preventive maintenance scheduling in a multiple production line. Journal of Intelligent Manufacturing, Volume 26 Issue 1, pp.111-122.
[12]
Endsley, M. R., &Robertson, M. M. 2000. Situation awareness in aircraft maintenance teams. International Journal of Industrial Ergonomics, Volume 26 Issue 2, pp.301-325.
[13]
Humphreys, P. 1995. Human reliability assessor's guide. A report by the Human Factors in Reliability Group Reports: SRDA-R11. Didcot, UK: AEA Technology.
[14]
Kiassat, C., Safaei, N., &Banjevic, D. 2014. Choosing the optimal intervention method to reduce human-related machine failures. European Journal of Operational Research, Volume 233 Issue 3, pp.604-612.
[15]
Kim, J., &Park, J. 2012. Reduction of test and maintenance human errors by analyzing task characteristics and work conditions. Progress in Nuclear Energy, Volume 58, pp.89-99.
[16]
Kirwan, B. 1996. The validation of three Human Reliability Quantification techniques-THERP, HEART and JHEDI: Part 1. Technique descriptions and validation issues. Applied Ergonomics, Volume 6, pp.359-373.
[17]
Kirwan, B., Kennedy, R., Taylor-Adams, S., &Lambert, B. 1994. Validation of three human reliability assessment techniques: THERP, HEART and JHEDI Vol. 1. Birmingham: Industrial Ergonomics Group, University of Birmingham.
[18]
Kralj, B., &Petrovic, R. 1995. A multi-objective optimization approach to thermal generation units maintenance scheduling. European Journal of Operational Research, Volume 84, pp.481-493.
[19]
Langer, R., Li, J., Biller, S., Chang, Q., Huang, N., &Xiao, G. 2010. Simulation study of a bottleneck-based dispatching policy for a maintenance workforce. International Journal of Production Research, Volume 48 Issue 6, pp.1745-1763.
[20]
Levitin, G., &Lisnianski, A. 2000. Short communication optimal replacement scheduling in multi-state series-parallel systems. Quality and Reliability Engineering International, Volume 16, pp.157-162.
[21]
Liang, G. F., Lin, J. T., Hwang, S. L., Wang, E. M. Y., &Patterson, P. 2010. Preventing human errors in aviation maintenance using an on-line maintenance assistance platform. International Journal of Industrial Ergonomics, Volume 40, pp.356-367.
[22]
Lim, J. H., &Park, D. H. 2007. Optimal periodic preventive maintenance schedules with improvement factors depending on number of preventive maintenances. Asia Pacific Journal of Operational Research, Volume 24 Issue 1, pp.111-124.
[23]
Moghaddam, K. S. 2008. Preventive maintenance and replacement scheduling: Models and algorithms Unpublished doctoral dissertation. University of Louisville, Louisville, Kentucky.
[24]
More, J. D., Tanscheit, R., Vellasco, M. M., Pacheco, M. A., &Swarcman, D. M. 2007. A fuzzy approach to the study of human reliability in the petroleum industry. Advances in Soft Computing, Volume 42, pp.415-424.
[25]
Noroozi, A., Khan, F., MacKinnon, S., Amyotte, P., &Deacon, T. 2014. Determination of human error probabilities in maintenance procedures of a pump. Process Safety and Environmental Protection, Volume 92, pp.131-141.
[26]
Park, H., Kang, M. J., &Son, S. 2012. Factors affecting quality and performance-a case study of Korean aircraft maintenance unit. Total Quality Management, Volume 23 Issue 2, pp.197-219.
[27]
Park, J., Jung, W., &Ha, J. 2001. Development of the step complexity measure for emergency operating procedures using entropy concepts. Reliability Engineering and System Safety, Volume 71, pp.115-130.
[28]
Pereira, C., Lapa, C., Mol, A., &Da Luz, A. 2009. A PSO approach for preventive maintenance scheduling optimization. International Nuclear Atlantic Conference, September 27-October 2, 2009, Rio de Janeiro, Brazil.
[29]
Reiman, T., &Oedewald, P. 2007. Organizational factors and safe human performance-work psychological model. 2007 IEEE 8th Human Factors and Power Plants and HPRCT, 13th Annual Meeting pp. 140-144, August 26-31, 2007, Monterey, California.
[30]
Ruiz, P. P., Foguem, B. K., &Grabot, B. 2014. Knowledge-based systems generating knowledge in maintenance from experience feedback. Knowledge-Based Systems, Volume 68, pp.4-20.
[31]
Sheikhalishahi, M. 2014. An integrated simulation-data envelopment analysis approach for maintenance activities planning. International Journal of Computer Integrated Manufacturing, Volume 27 Issue 9, pp.858-868.
[32]
Sheikhalishahi, M., Ebrahimipour, V., &Farahani, M. H. 2013. An integrated GA-DEA algorithm for determining the most effective maintenance policy for a k-out-of-n problem. Journal of Intelligent Manufacturing, Volume 25 Issue 6, pp.1455-1462.
[33]
Sheikhalishahi, M., Ebrahimipour, V., Shiri, H., Zaman, H., &Jeihoonian, M. 2013. A hybrid GA-PSO approach for reliability optimization in redundancy allocation problem. International Journal of Advanced Manufacturing Technology, Volume 68 Issue 1-4, pp.317-338.
[34]
Shirmohammadi, A. H., Zhang, Z. G., &Love, E. 2007. A computational model for determining the optimal preventive maintenance policy with random breakdowns and imperfect repairs. IEEE Transactions on Reliability, Volume 56, pp.332-339.
[35]
Taboada, H. A., Espiritu, J. F., &Coit, D. W. 2008. MOMS-GA: A multi-objective multi-state genetic algorithm for system reliability optimization design problems. IEEE Transactions on Reliability, Volume 57, pp.182-191.
[36]
Tam, A., Chan, W., &Price, J. 2006. Optimal maintenance intervals for a multi-component system. Production Planning and Control, Volume 17, pp.769-779.
[37]
Tian, Z., Zuo, M. J., &Huang, H. 2008. Reliability-redundancy allocation for multi-state series-parallel systems. IEEE Transactions on Reliability, Volume 57 Issue 2, pp.303-310.
[38]
Toriizuka, T. 2001. Application of performance shaping factor PSF for work improvement in industrial plant maintenance tasks. International Journal of Industrial Ergonomics, Volume 28, pp.225-236.
[39]
Usanmaz, O. 2011. Training of the maintenance personnel to prevent failures in aircraft systems. Engineering Failure Analysis, Volume 18 Issue 7, pp.1683-1688.
[40]
<familyNamePrefix>Van</familyNamePrefix>Horenbeek, A., &Pintelon, L. 2013. A dynamic predictive maintenance policy for complex multi-component systems. Reliability Engineering & System Safety, Volume 120, pp.39-50.
[41]
Wang, C. H., &Hwang, S. L. 2004. A stochastic maintenance management model with recovery factor. Journal of Quality in Maintenance Engineering, Volume 10 Issue 2, pp.154-165.
[42]
Wang, C. H., &Sheu, S. H. 2003. Determining the optimal production-maintenance policy with inspection errors: Using a Markov chain. Computers and Operations Research, Volume 30 Issue 1, pp.1-17.
[43]
Wang, C. H., &Tsai, S. W. 2012. Optimizing bi-objective imperfect preventive maintenance model for series-parallel system using established hybrid genetic algorithm. Journal of Intelligent Manufacturing, Volume 25 Issue 3, pp.603-616.
[44]
Williams, J. C. 1986. HEART-a proposed method for assessing and reducing human error. Ninth Advances in Reliability Technology Symposium, NEC, Birmingham, AEA Technology, June 1986, Culcheth, Warrington.
[45]
Williams, J. C. 1988. A data-based method for assessing and reducing human error to improve operational performance. Proceedings of the IEEE Fourth Conference on Human Factors and Power Plants pp. 436-450, June 5-9, 1988. Monterey, California. New York, NY: IEEE.
Recommendations
Multi-objective modeling for preventive maintenance scheduling in a multiple production line
The change of market and the explosion of product variety have led to increased automation and the need for complex equipment. Therefore, the reliability and the maintainability of the equipment play an important role in controlling both the quantity ...
Optimisation of preventive maintenance grouping strategy for multi-component series systems
Preventive maintenance grouping in series system is studied.Particle swarm optimisation is applied to resolve a multi-objective problem.The approach aims to minimize the cost and maximize the system availability.The obtained results are compared with ...
Preventive Maintenance Scheduling: Decision Model Development and Case Study Analysis
In this work, the effectiveness of preventive maintenance scheduling PMS decisions was reported based on a techno-economic model that reflects cost objective function for ship maintenance activities. With a potential to impact on both transportation ...
Comments
Information & Contributors
Information
Published In
Publisher
John Wiley and Sons Ltd.
United Kingdom
Publication History
Published: 01 November 2016
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 15 Jan 2025