Abstract
This paper addresses the joint optimization model of production scheduling, work-in-process (WIP) inventory control and group preventive maintenance (PM) planning in a multi-machine system with multi-components. The objective is to obtain optimum production sequence, PM intervals and grouping of components, which minimize the total expected cost per unit time of the system. A new meta-heuristic named Jaya algorithm and two popular algorithms viz. simulated annealing (SA) and particle swarm optimization (PSO) are applied to optimize the objective function. Initially, the optimum group of components is obtained based on the integer multiples of individual PM intervals. Secondly, the job permutation sequence incorporating group PM intervals is identified with the largest order value (LOV) rule. The shift in optimum PM intervals is realized with an advanced-postpone balancing approach. Computational results reveal that the proposed integrated model along with group PM yields up to 25% cost reductions when compared to the integrated model with individual maintenance as well as 37% savings while no integration is performed. Furthermore, the performance of algorithms is evaluated with large-sized problems. The obtained results show that Jaya and SA yielded comparable results, however, PSO is least productive. Thus, the proposed approach yields better economic performance and brings more improvised solutions as compared to the conventional methods of integrated scheduling and maintenance optimization problems.
Similar content being viewed by others
References
Boufellouh, R., & Belkaid, F. (2020). Bi-objective optimization algorithms for joint production and maintenance scheduling under a global resource constraint: Application to the permutation flow shop problem. Computers and Operations Research, 122, 104943. https://doi.org/10.1016/j.cor.2020.104943
Buddala, R., & Mahapatra, S. S. (2018). An integrated approach for scheduling flexible job-shop using teaching–learning-based optimization method. Journal of Industrial Engineering International, 15(1), 181–192. https://doi.org/10.1007/S40092-018-0280-8
Bülbül, K., Kaminsky, P., & Yano, C. (2004). Flow shop scheduling with earliness, tardiness, and intermediate inventory holding costs. Naval Research Logistics (NRL), 51(3), 407–445. https://doi.org/10.1002/nav.20000
Chalabi, N., Dahane, M., Beldjilali, B., & Neki, A. (2016). Optimisation of preventive maintenance grouping strategy for multi-component series systems: Particle swarm based approach. Computers and Industrial Engineering, 102, 440–451. https://doi.org/10.1016/j.cie.2016.04.018
Chen, X. (2015). An integrated model of production scheduling and maintenance planning under imperfect preventive maintenance. Maintenance and Reliability, 17(1), 70–79.
Chen, X., An, Y., Zhang, Z., & Li, Y. (2020). An approximate nondominated sorting genetic algorithm to integrate optimization of production scheduling and accurate maintenance based on reliability intervals. Journal of Manufacturing Systems, 54, 227–241. https://doi.org/10.1016/j.jmsy.2019.12.004
Colledani, M., Tolio, T., Fischer, A., Iung, B., Lanza, G., Schmitt, R., & Váncza, J. (2014). Design and management of manufacturing systems for production quality. CIRP Annals - Manufacturing Technology, 63(2), 773–796. https://doi.org/10.1016/j.cirp.2014.05.002
Cui, L. (2008). Maintenance models and optimization. In Handbook of performability engineering (pp. 789–805). London: Springer London. https://doi.org/10.1007/978-1-84800-131-2_48
Cui, W., Lu, Z., Li, C., & Han, X. (2018). A proactive approach to solve integrated production scheduling and maintenance planning problem in flow shops. Computers and Industrial Engineering, 115(2017), 342–353. https://doi.org/10.1016/j.cie.2017.11.020
Das, H., Naik, B., & Behera, H. S. (2020). A Jaya algorithm based wrapper method for optimal feature selection in supervised classification. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/J.JKSUCI.2020.05.002
De, A., Mogale, D. G., Zhang, M., Pratap, S., Kumar, S. K., & Huang, G. Q. (2020). Multi-period multi-echelon inventory transportation problem considering stakeholders behavioural tendencies. International Journal of Production Economics, 225, 107566. https://doi.org/10.1016/j.ijpe.2019.107566
Du, D.-C., Vinh, H.-H., Trung, V.-D., Hong Quyen, N.-T., & Trung, N.-T. (2017). Efficiency of Jaya algorithm for solving the optimization-based structural damage identification problem based on a hybrid objective function. Engineering Optimization, 0273(September), 1–19. https://doi.org/10.1080/0305215X.2017.1367392
Ebeling, C. E. (2004). An introduction to reliability and maintainability engineering. McGraw-Hill. https://books.google.co.in/books/about/An_introduction_to_reliability_and_maint.html?id=iFumyeVLIEAC. Accessed 21 February 2018.
Feng, H., Tan, C., Xia, T., Pan, E., & Xi, L. (2019). Joint optimization of preventive maintenance and flexible flowshop sequence-dependent group scheduling considering multiple setups. Engineering Optimization, 51(9), 1529–1546. https://doi.org/10.1080/0305215X.2018.1540696
Geng, J., Azarian, M., & Pecht, M. (2015). Opportunistic maintenance for multi-component systems considering structural dependence and economic dependence. Journal of Systems Engineering and Electronics, 26(3), 493–501. https://doi.org/10.1109/JSEE.2015.00057
Hadidi, L. A., Turki, U. M. Al, & Rahim, A. (2012). Integrating production scheduling and maintenance: Practical implications. In 2012 International conference on industrial engineering and operations management, (2010) (pp. 336–343).
Hadidi, L. A., Turki, U. M., & Al Rahim, A. (2011). Integrated models in production planning and scheduling, maintenance and quality: A review. International Journal of Industrial and Systems Engineering, 10(1), 21. https://doi.org/10.1504/ijise.2012.044042
Kaplanoǧlu, V. (2014). Multi-agent based approach for single machine scheduling with sequence-dependent setup times and machine maintenance. Applied Soft Computing Journal, 23, 165–179. https://doi.org/10.1016/j.asoc.2014.06.020
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95—international conference on neural networks (Vol. 4, pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science (new York, n.y.), 220(4598), 671–680. https://doi.org/10.1126/science.220.4598.671
Kolus, A., El-Khalifa, A., Al-Turki, U. M., & Duffuaa, S. O. (2020). An integrated mathematical model for production scheduling and preventive maintenance planning. International Journal of Quality and Reliability Management. https://doi.org/10.1108/IJQRM-10-2019-0335
Kumar, S., Purohit, B. S., & Lad, B. K. (2014). Integrated approach for job scheduling and multi-component maintenance planning ina production system. In 5th international & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT Guwahati, Assam, India.
Kumar, S., Purohit, B. S., Manjrekar, V., Singh, V., & Lad, B. K. (2018). Investigating the value of integrated operations planning: A case-based approach from automotive industry. International Journal of Production Research, 56(22), 6971–6992. https://doi.org/10.1080/00207543.2018.1424367
Lee, J., Lapira, E., Yang, S., & Kao, A. (2013). Predictive manufacturing system - Trends of next-generation production systems. IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 46). IFAC. https://doi.org/10.3182/20130522-3-BR-4036.00107
Li, M., Li, H., & Liu, Q. (2010). Integrated production scheduling and opportunistic preventive maintenance in the flowshop manufacturing system. In 2nd international conference on information science and engineering, ICISE2010 - Proceedings, (pp. 294–298). https://doi.org/10.1109/ICISE.2010.5690889
Li, N., Chan, F. T. S., Chung, S. H., & Tai, A. H. (2017). A stochastic production-inventory model in a two-state production system with inventory deterioration, rework process, and backordering. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(6), 916–926. https://doi.org/10.1109/TSMC.2016.2523802
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, 24(01), 111–124. https://doi.org/10.1142/S0217595907001139
Liu, H., Gao, L., & Pan, Q. (2011). A hybrid particle swarm optimization with estimation of distribution algorithm for solving permutation flowshop scheduling problem. Expert Systems with Applications, 38(4), 4348–4360. https://doi.org/10.1016/j.eswa.2010.09.104
M’Hallah, R. (2014). Minimizing total earliness and tardiness on a permutation flow shop using VNS and MIP. Computers and Industrial Engineering (Vol. 75). Elsevier. https://doi.org/10.1016/j.cie.2014.06.011
Mazdeh, M. M., & Rostami, M. (2014). A branch-and-bound algorithm for two-machine flow-shop scheduling problems with batch delivery costs. International Journal of Systems Science: Operations & Logistics, 1(2), 94–104. https://doi.org/10.1080/23302674.2014.942408
Mishra, A. K., Shrivastava, D., Bundela, B., & Sircar, S. (2020). An efficient Jaya algorithm for multi-objective permutation flow shop scheduling problem. Advances in intelligent systems and computing (Vol. 949). Singapore: Springer. https://doi.org/10.1007/978-981-13-8196-6_11
Mishra, A., & Shrivastava, D. (2018). A TLBO and a Jaya heuristics for permutation flow shop scheduling to minimize the sum of inventory holding and batch delay costs. Computers & Industrial Engineering, 124(July), 509–522. https://doi.org/10.1016/J.CIE.2018.07.049
Mishra, A. K., Shrivastava, D., & Vrat, P. (2019). An opportunistic group maintenance model for the multi-unit series system employing Jaya algorithm. Opsearch, 57(2), 603–628. https://doi.org/10.1007/s12597-019-00422-y
Miyata, H. H., Nagano, M. S., & Gupta, J. N. D. (2019a). Integrating preventive maintenance activities to the no-wait flow shop scheduling problem with dependent-sequence setup times and makespan minimization. Computers and Industrial Engineering, 135, 79–104. https://doi.org/10.1016/j.cie.2019.05.034
Miyata, H. H., Nagano, M. S., & Gupta, J. N. D. (2019b). Incorporating preventive maintenance into the m-machine no-wait flow-shop scheduling problem with total flow-time minimization: A computational study. Engineering Optimization, 51(4), 680–698. https://doi.org/10.1080/0305215X.2018.1485903
Navaei, J., Fatemi Ghomi, S. M. T., Jolai, F., & Mozdgir, A. (2014). Heuristics for an assembly flow-shop with non-identical assembly machines and sequence dependent setup times to minimize sum of holding and delay costs. Computers and Operations Research, 44, 52–65. https://doi.org/10.1016/j.cor.2013.10.008
Pandey, D., Kulkarni, M. S., & Vrat, P. (2010). Joint consideration of production scheduling, maintenance and quality policies: A review and conceptual framework. International Journal of Advanced Operations Management, 2, 1. https://doi.org/10.1504/IJAOM.2010.034583
Pandey, D., Kulkarni, M. S., & Vrat, P. (2011). A methodology for joint optimization for maintenance planning, process quality and production scheduling. Computers & Industrial Engineering, 61(4), 1098–1106. https://doi.org/10.1016/j.cie.2011.06.023
Pham, H., & Wang, H. (1996). Imperfect maintenance. European Journal of Operational Research, 94(3), 425–438. https://doi.org/10.1016/S0377-2217(96)00099-9
Purohit, B. S., & Lad, B. K. (2016). Production and maintenance planning: An integrated approach under uncertainties. The International Journal of Advanced Manufacturing Technology, 86(9–12), 3179–3191. https://doi.org/10.1007/s00170-016-8415-9
Rahman, H. F., Sarker, R., & Essam, D. (2015). A genetic algorithm for permutation flow shop scheduling under make to stock production system. Computers and Industrial Engineering, 90, 12–24. https://doi.org/10.1016/j.cie.2015.08.006
Rivera-Gómez, H., Gharbi, A., Kenné, J. P., Montaño-Arango, O., & Corona-Armenta, J. R. (2020). Joint optimization of production and maintenance strategies considering a dynamic sampling strategy for a deteriorating system. Computers and Industrial Engineering, 140, 106273. https://doi.org/10.1016/j.cie.2020.106273
Rojoko A. (2017). EBSCOhost | 124305798 | Industry 4.0 Concept: Background and Overview. Accessed 2 July 2021
Safari, E., Sadjadi, S. J., & Shahanaghi, K. (2010). Scheduling flowshops with condition-based maintenance constraint to minimize expected makespan. International Journal of Advanced Manufacturing Technology, 46(5–8), 757–767. https://doi.org/10.1007/s00170-009-2151-3
Seeanner, F., & Meyr, H. (2013). Multi-stage simultaneous lot-sizing and scheduling for flow line production. Or Spectrum, 35(1), 33–73. https://doi.org/10.1007/s00291-012-0296-1
Suresh, K., & Kumarappan, N. (2012). Particle swarm optimization based generation maintenance scheduling using probabilistic approach. Procedia Engineering, 30(2011), 1146–1154. https://doi.org/10.1016/j.proeng.2012.01.974
Tambe, P. P., & Kulkarni, M. S. (2014). A novel approach for production scheduling of a high pressure die casting machine subjected to selective maintenance and a sampling procedure for quality control. International Journal of System Assurance Engineering and Management, 5(3), 407–426. https://doi.org/10.1007/s13198-013-0183-4
Tambe, P. P., & Kulkarni, M. S. (2015). A superimposition based approach for maintenance and quality plan optimization with production schedule, availability, repair time and detection time constraints for a single machine. Journal of Manufacturing Systems, 37, 17–32. https://doi.org/10.1016/j.jmsy.2015.09.009
Tambe, P. P., Mohite, S., & Kulkarni, M. S. (2013). Optimisation of opportunistic maintenance of a multi-component system considering the effect of failures on quality and production schedule: A case study. International Journal of Advanced Manufacturing Technology, 69(5–8), 1743–1756. https://doi.org/10.1007/s00170-013-5122-7
Venkata Rao, R. (2019). Introduction. In Jaya: An advanced optimization algorithm and its engineering applications (pp. 1–8). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-78922-4_1
Xiao, L., Song, S., Chen, X., & Coit, D. W. (2016). Joint optimization of production scheduling and machine group preventive maintenance. Reliability Engineering and System Safety, 146, 68–78. https://doi.org/10.1016/j.ress.2015.10.013
Yu, A. J., & Seif, J. (2016). Minimizing tardiness and maintenance costs in flow shop scheduling by a lower-bound-based GA. Computers and Industrial Engineering, 97, 26–40. https://doi.org/10.1016/j.cie.2016.03.024
Zandieh, M., Khatami, A. R., & Rahmati, S. H. A. (2017). Flexible job shop scheduling under condition-based maintenance: Improved version of imperialist competitive algorithm. Applied Soft Computing Journal, 58, 449–464. https://doi.org/10.1016/j.asoc.2017.04.060
Zhang, Z., Tang, Q., & Chica, M. (2021). Maintenance costs and makespan minimization for assembly permutation flow shop scheduling by considering preventive and corrective maintenance. Journal of Manufacturing Systems, 59, 549–564. https://doi.org/10.1016/j.jmsy.2021.03.020
Zhou, X., Lu, Z., & Xi, L. (2012). Preventive maintenance optimization for a multi-component system under changing job shop schedule. Reliability Engineering and System Safety, 101, 14–20. https://doi.org/10.1016/j.ress.2012.01.005
Zhou, X., Xi, L., & Lee, J. (2009). Opportunistic preventive maintenance scheduling for a multi-unit series system based on dynamic programming. International Journal of Production Economics, 118(2), 361–366. https://doi.org/10.1016/j.ijpe.2008.09.012
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mishra, A.K., Shrivastava, D., Tarasia, D. et al. Joint optimization of production scheduling and group preventive maintenance planning in multi-machine systems. Ann Oper Res 316, 401–444 (2022). https://doi.org/10.1007/s10479-021-04362-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-021-04362-z