Stochastic Programming Model and Benders decomposition approach for Integrated Production and Maintenance Planning in Multi-Factory Production

Document Type : Research Paper

Authors

1 Industrial Engineering Department, Science and Research Branch Islamic Azad University, Tehran, Iran

2 Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran

3 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Abstract

Proper performance of production/supply centers in a multi-factory production (MFP) network requires undisrupted equipment. The design and implementation of a maintenance system is important for two reasons - firstly, extending the life of the equipment, and second, reducing the MFP network disruption and associated costs. The greater productivity of an MFP system is achieved by integrating maintenance and production decisions. In this study, considering a scenario-based uncertainty in the demand and failure rate, a robust scenario-based stochastic programming (RSSP) model has been presented. proposed RSSP model integrates the strategic and operational decisions of production and the maintenance, and takes into account the MFP disruption costs. We suggest three preventive maintenance strategies such as maintenance outsourcing, deployment of backup equipment, as well as periodic preventive maintenance for the MFP network. The objective function of the proposed model is to maximize the total profit, subject to, constraints such as limited capacity of production, storage, access to service centers, and budget should be satisfied. The proposed RSSP model is formulated as mixed linear program which can be solved in small-scale instances by the CPLEX Solver. Furthermore, Benders decomposition solution method is proposed for large-scale instances. Finally, a numerical study of CNG stations, as an MFP network, is conduct to demonstrate the applicability of the proposed model and analyze the results.

Keywords

References

[1] Chung, S. H., Chan, F. T., Chan, H. K. (2009a). “A modified genetic algorithm approach for scheduling of perfect maintenance in distributed production scheduling”. Engineering Applications of Artificial Intelligence, 22(7): 1005-1014.
[2] Behnamian, J., Fatemi Ghomi, S. M. T. (2013). “The heterogeneous multi-factory production network scheduling with adaptive communication policy and parallel machine”. Information Sciences, 219: 181-196.
[3] Wu, S.-j., Gebraeel, N., Lawley, M. A., Yih, Y. (2007). “A neural network integrated decision support system for condition-based optimal predictive maintenance policy”. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 37(2): 226-236.
[4] Chung, S. H., Lau, H. C., Ho, G. T., Ip, W. H. (2009b). “Optimization of system reliability in multi-factory production networks by maintenance approach”. Expert Systems with Applications, 36(6): 10188-10196.
[5] Hadidi, L. A., Al-Turki, U. M., Rahim, A. (2012). “Integrated models in production planning and scheduling, maintenance and quality: a review”. International Journal of Industrial and Systems Engineering, 10(1): 21-50.
[6] Boudjelida, A. (2019). “On the robustness of joint production and maintenance scheduling in presence of uncertainties”. Journal of Intelligent Manufacturing, 30(4): 1515-1530.
[7] Paprocka, I. (2019).” The model of maintenance planning and production scheduling for maximising robustness”. International Journal of Production Research, 57(14): 4480-4501.
[8] Vasili, M., Hong, T. S., Ismail, N., & Vasili, M. (2011). “Maintenance optimization models: a review and analysis”. International Conference on Industrial Engineering and Operations Management, Kuala lampur, Malaysia, 2(1): 1131-1138.
[9] بهنامیان، جواد.، فاطمی قمی، سید محمدتقی. (1392). "ارائه الگوریتم ترکیبی بر پایه بهینه‌سازی گروه ذرات و روش هایپرهیوریستیک برای زمان‌بندی کارخانه‌های توزیع شده با اتحاد مجازی". نشریه پژوهش‌های مهندسی صنایع در سیستم­های تولید، 1(1): 1-11.
[10] Behnamian, J., Fatemi Ghomi, S. M. T. (2016). “A survey of multi-factory scheduling“. Journal of Intelligent Manufacturing, 27(1): 231-249.
[11] Behnamian, J. (2017). “Matheuristic for the decentralized factories scheduling problem”. Applied Mathematical Modelling, 47: 668-684.
[12] Behnamian, J., Ghomi, S. F. (2012). “Incorporating transportation time in multi-agent production network scheduling”. International Journal of Computer Integrated Manufacturing, 25(12): 1111-1128.
[13] Behnamian, J., Ghomi, S. F. (2015). “Minimizing cost-related objective in synchronous scheduling of parallel factories in the virtual production network”. Applied Soft Computing, 29: 221-232.
[14] Behnamian, J., Zandieh, M., Ghomi, S. F. (2009). “Parallel-machine scheduling problems with sequence-dependent setup times using an ACO, SA and VNS hybrid algorithm”. Expert Systems with Applications, 36(6): 9637-9644.
[15] Li, G., Liu, M., Sethi, S. P., Xu, D. (2017). “Parallel-machine scheduling with machine-dependent maintenance periodic recycles”. International Journal of Production Economics, 186: 1-7.
[16] Chang, H.-C., Liu, T.-K. (2017). “Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms”. Journal of Intelligent Manufacturing, 28(8): 1973-1986.
[17] Costa, A., Cappadonna, F. A., Fichera, S. (2016). “Total tardiness minimization in a parallel machine system with flexible periodic maintenance”. Journal of Industrial and Production Engineering, 33(7): 485-494.
[18] Shen, J., Zhu, Y. (2018). “A parallel-machine scheduling problem with periodic maintenance under uncertainty”. Journal of Ambient Intelligence and Humanized Computing, 1-9.
[19] Elsayed, E., Dhillon, B. S. (1979). “Repairable systems with one standby unit”. Microelectronics Reliability, 19(3): 243-245.
[20] Lu, Z., Cui, W., Han, X. (2015). “Integrated production and preventive maintenance scheduling for a single machine with failure uncertainty”. Computers & Industrial Engineering, 80: 236-244.
[21] Liu, X., Wang, W., Peng, R. (2015). “An integrated production, inventory and preventive maintenance model for a multi-product production system”. Reliability Engineering & System Safety, 137: 76-86.
[22] Jiang, C., Lu, Z., Cui, W. (2016). “Heuristics for the identical machine scheduling problem with preventive maintenances”. Journal of Shanghai Jiaotong University (Science), 21(1): 112-120.
[23] Chansombat, S., Pongcharoen, P., Hicks, C. (2019). “A mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry”. International Journal of Production Research, 57(1): 61-82.
[24] Hnaien, F., Yalaoui, F., Mhadhbi, A., & Nourelfath, M. (2016). “A mixed-integer programming model for integrated production and maintenance”. IFAC-PapersOnLine, 49(12): 556-561.
[25] Alimian, M., Saidi-Mehrabad, M., & Jabbarzadeh, A. (2019). “A robust integrated production and preventive maintenance planning model for multi-state systems with uncertain demand and common cause failures”. Journal of Manufacturing Systems, 50: 263-277.
[26] بابایی مراد، سمانه، فتاحی، پرویز، باقری، حسن. (1398). "بهینه‌سازی توأم سیاست زمان‌بندی تولید و نگهداری و تعمیرات با در نظر گرفتن کمبود از نوع پس‌افت و تقاضا به‌صورت احتمالی". نشریه پژوهش های مهندسی صنایع در سیستم های تولید، 7(14): 47-57.
[27] Ghaleb, M., Taghipour, S., Sharifi, M., Zolfagharinia, H. (2020). “Integrated production and maintenance scheduling for a single degrading machine with deterioration-based failures”. Computers & Industrial Engineering, 143: 106432.
[28] 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 & Reliability Management, DOI:10.1108/IJQRM-10-2019-0335.
[29] Wu, O., Dalle Ave, G., Harjunkoski, I., Bouaswaig, A., Schneider, S. M., Roth, M., Imsland, L. (2020). “Optimal production and maintenance scheduling for a multiproduct batch plant considering degradation”. Computers & Chemical Engineering, 135: 106734.
[30] Mulvey, J. M., Vanderbei, R. J., Zenios, S. A. (1995). “Robust optimization of large-scale systems”. Operations research, 43(2): 264-281.
[31] Salehi, F., Mahootchi, M., Husseini, S. M. M. (2017). “Developing a robust stochastic model for designing a blood supply chain network in a crisis: A possible earthquake in Tehran”. Annals of Operations Research, 1-25.
[32] Yu, C.-S., Li, H.-L. (2000). “A robust optimization model for stochastic logistic problems”. International Journal of Production Economics, 64(1-3): 385-397.
[33] Rahmaniani, R., Crainic, T. G., Gendreau, M., Rei, W. (2017).”The Benders decomposition algorithm: A literature review”. European Journal of Operational Research, 259(3): 801-817.
[34] Belieres, S., Hewitt, M., Jozefowiez, N., Semet, F., Van Woensel, T. (2020). “A Benders decomposition-based approach for logistics service network design”. European Journal of Operational Research, 286(2): 523-537.
[35] Keyvanshokooh, E., Ryan, S. M., Kabir, E. (2016). “Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition”. European Journal of Operational Research, 249(1): 76-92.
Journal of Industrial Engineering Research in Production Systems
Volume 8, Issue 16
September 2020
Pages 77-93

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