پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 705 |
| تعداد مقالات | 10,156 |
| تعداد مشاهده مقاله | 71,518,511 |
| تعداد دریافت فایل اصل مقاله | 63,228,606 |
The fuzzy model of dynamic production and maintenance planning in pumped-storage hydroelectricity | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 20، دوره 15، شماره 7، مهر 2024، صفحه 227-242 اصل مقاله (2.58 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.31113.4569 | ||
| نویسندگان | ||
| Farid Asgari1؛ Fariborz Jolai* 1، 2؛ Farzad Movahedisobhani1 | ||
| 1Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| تاریخ دریافت: 13 بهمن 1401، تاریخ بازنگری: 11 تیر 1402، تاریخ پذیرش: 15 تیر 1402 | ||
| چکیده | ||
| Developing hydropower plants is a successful strategy for sustainable energy production in countries. On the other hand, due to the high capacity of energy production in the pumping power plant sector, the strategy of saving and continuous exploitation of these power plants is one of the successful policies of governments. Therefore, in this research, the optimization of energy production and maintenance costs in one of the large storage pump power plants in Iran has been discussed and investigated based on the optimization mathematical model strategy. Therefore, a Mixed Integer Nonlinear Programming mathematical model was developed in this field. Due to the uncertainty in the presented mathematical model, the fuzzification strategy was used in the mathematical model. On the other hand, in order to achieve the optimal production plan, an energy production cost optimization policy has been presented to reduce the difference in supply and demand in the energy production network. In order to evaluate the presented mathematical model, four meta-heuristic algorithms of Multi-objective Keshtel Algorithm, Multi-objective Simulated Annealing, Non-dominated Ranking Genetic Algorithm and Non-dominated Sorting Genetic Algorithm II were used with binary coding. The results of this research have shown that the solution of the meta-heuristic NRGA algorithm has been done despite the approximation of the optimal solutions in a suitable period of time, and the results of the research indicate the applicability of the presented model in the studied power plant. Therefore, according to the level of optimization performed in the case study, it has caused the improvement of planning by 7% to 12% and effective optimization processes. | ||
| کلیدواژهها | ||
| production planning؛ maintenance؛ pump storage power plant؛ meta-heuristic algorithm؛ NSGA-II؛ NRGA؛ MOKA؛ MOSA | ||
| مراجع | ||
|
[1] A.M. Aguirre and L.G. Papageorgiou, Medium-term optimization-based approach for the integration of production planning, scheduling and maintenance, Comput. Chem. Eng. 116 (2018), 191–211. [2] O.P. Akkas and E. Cam, Optimal operation of virtual power plant in a day ahead market, 3rd Int. Symp. Multidiscip. Stud. Innov. Technol. (ISMSIT), IEEE, 2019, pp. 1–4. [3] F. Berthaut, A. Gharbi and K. Dhouib, Joint modified block replacement and production/inventory control policy for a failure-prone manufacturing cell, Omega 39 (2011), no. 6, 642–654. [4] A. Boudjelida, On the robustness of joint production and maintenance scheduling in presence of uncertainties, J. Intell. Manufact. 30 (2019), no. 4, 1515–1530. [5] B. Bouslah, A. Gharbia and R. Pellerin, Integrated production, sampling quality control and maintenance of deteriorating production systems with AOQL constraint, Omega 61 (2016), 110–126. [6] A.H. Elgamal, G. Kocher-Oberlehner, V. Robu and M. Andoni, Optimization of a multiple-scale renewable energy-based virtual power plant in the UK, Appl. Energy 256 (2019), 113973. [7] K. Ertogral and F.S. Ozturk, An integrated production scheduling and workforce capacity planning model for the maintenance and repair operations in airline industry, Comput. Ind. Eng. 127 (2019), 832–840. [8] C. Gaz, F. Flacco and A. De Luca, Extracting feasible robot parameters from dynamic coefficients using nonlinear optimization methods, IEEE Int. Conf. Robotics Autom. (ICRA), IEEE, 2016, pp. 2075–2081. [9] R. Glawar, M. Karner, T. Nemeth, K. Matyas and W. Sihn, An approach for the integration of anticipative maintenance strategies within a production planning and control model, Procedia CIRP 67 (2018), 46–51. [10] S. Hadayeghparast, A.S. Farsangi and H. Shayanfar, Day-ahead stochastic multi-objective economic/emission operational scheduling of a large-scale virtual power plant, Energy 172 (2019), 630–646. [11] A. Hamrol, A new look at some aspects of maintenance and improvement of production processes, Manag. Product. Eng. Rev. 9 (2018). [12] M.R. Homaeinezhad, M. Homaeinezhad, S. Akbari and D.N.G. Hosseini, Input-decoupled discrete-time sliding mode control algorithm for servo multi-field multi-armature DC machine, ISA Trans. 127 (2022), 283–298. [13] J.D. Hunt, M.A.V. Freitas and A.O.P. Junior, Enhanced-pumped-storage: Combining pumped-storage in a yearly storage cycle with dams in cascade in Brazil, Energy 78 (2014), 513–523. [14] K. Kang and V. Subramaniam, Joint control of dynamic maintenance and production in a failure-prone manufacturing system subjected to deterioration, Comput. Industr. Eng. 119 (2018), 309–320. [15] J. Kim and S.B. Gershwin, Analysis of long flow lines with quality and operational failures, IIE Trans. 40 (2008), no. 3, 284–296. [16] G.M. Kopanos and L. Puigjaner, Integrated operational and maintenance planning of production and utility systems, Solving large-scale production scheduling and planning in the process industries, Springer, Cham, 2019, pp. 191–244. [17] C.M. La Fata and G. Passannanti, A simulated annealing-based approach for the joint optimization of production/inventory and preventive maintenance policies, Int. J. Adv. Manufact. Technol. 91 (2017), no. 9–12, 3899–3909. [18] X. Liao and C. Fang, Selection of supplier portfolio in the presence of operational risk and disruption risk, IEEE Int. Conf. Ind. Engin. Engin. Manag., IEEE, 2015, pp. 1825–1829. [19] Q. Liu, M. Dong and F.F. Chen, Single-machine-based joint optimization of predictive maintenance planning and production scheduling, Robotics Comput.-Integrated Manufact. 51 (2018), 238–247. [20] Q. Liu, M. Dong, F.F. Chen, W. Lv and C. Ye, Single-machine-based joint optimization of predictive maintenance planning and production scheduling, Robotics Comput.-Integrated Manufact. 55 (2019), 173–182. [21] J. Liu, J. Shi and S.J. Hu, Quality-assured setup planning based on the stream-of-variation model for multi-stage machining processes, Int. Manufact. Sci. Engin. Conference, 47624 (2006), 529–538. [22] G. Martınez-Lucas, J.I. Perez-Dıaz, M. Chazarra, J.I. Sarasua, G. Cavazzini, G. Pavesi and G. Ardizzon, Risk of penstock fatigue in pumped-storage power plants operating with variable speed in pumping mode, Renew. Energy 133 (2019), 636–646. [23] J.F. Mennemann, L. Marko, J. Schmidt, W. Kemmetmuller and A. Kugi, Nonlinear model predictive control of a variable-speed pumped-storage power plant, IEEE Trans. Control Syst. Technol. 29 (2019), no. 2, 645–660. [24] S. Ozyon, Optimal short-term operation of pumped-storage power plants with differential evolution algorithm, Energy 194 (2020), 116866. [25] M. Rahimkhani, M. Saberian, A. Mordadi, S. Varmazyar and A. Tavakoli, Urinary tract infection with Candida glabrata in a patient with spinal cord injury, Acta Medica Iranica 53 (2015), no. 8, 516–517. [26] S.S. Sana, Preventive maintenance and optimal buffer inventory for products sold with warranty in an imperfect production system, Int. J. Product. Res. 50 (2012), no. 23, 6763–6774. [27] M. Schreiber, J. Klober-Koch, C. Richter and G. Reinhart, Integrated production and maintenance planning for cyber-physical production systems, Procedia CIRP 72 (2018), 934–939. [28] M. Shafiee, R. Ghazi and M. Moeini-Aghtaie, Day-ahead resource scheduling in distribution networks with presence of electric vehicles and distributed generation units, Electric Power Compon. Syst. 47 (2019), no. 16–17, 1–14. [29] M. Sheikh Alishahi, N. Eskandari, A. Mashayekhi and A. Azadeh, Multi-objective open shop scheduling by considering human error and preventive maintenance, Appl. Math. Modell. 67 (2019), 573–587. [30] M.H. Vasconcelos, P. Beires, C.L. Moreira and J.A.P. Lopes, Dynamic security of islanded power systems with pumped storage power plants for high renewable integration–A study case, J. Engin. 2019 (2019), no. 18, 4955–4960. [31] Y. Wu, T. Zhang, C. Xu, B. Zhang, L. Li, Y. Ke, Y. Yan and R. Xu, Optimal location selection for offshore wind-PV-seawater pumped storage power plant using a hybrid MCDM approach: A two-stage framework, Energy Conversion Manag. 199 (2019), 112066. [32] S. Xiao, Z. Chen and B.R. Sarker, Integrated maintenance and production decision for k-out-of-n system equipment with attenuation of product quality, Int. J. Qual. Reliabil. Manag. 36 (2019), no. 5, 735–751. [33] S. Yin, Q. Ai, Z. Li, Y. Zhang and T. Lu, Energy management for aggregate prosumers in a virtual power plant: A robust Stackelberg game approach, Int. J. Electric. Power Energy Syst. 117 (2020), 105605. [34] L. Zhang, J. Zhang, X. Yu, J. Lv and X. Zhang, Transient simulation for a pumped storage power plant considering pressure pulsation based on field test, Energies 12 (2019), no. 13, 2498. | ||
|
آمار تعداد مشاهده مقاله: 13,684 تعداد دریافت فایل اصل مقاله: 17,798 |
||