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Equivalent availability index for the performance measurement of haul truck fleets

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This article presents a model of performance analysis for a truck fleet system in an openpit mine, considering special characteristics of haul fleets. In these systems, the expected availability of each piece of equipment and its operating capacity are the fundamental variables to construct a global fleet performance function. Our analytical algorithm considers heterogeneous fleets with known individual characteristics of transport capacity and failure and repair behavior. The results converge to a new indicator denominated “Equivalent Availability” (EA), which arises from the need to evaluate the capacity of the truck fleet to operate at a lower payload than required using different combinations of equipment to achieve an availability goal. EA is a key indicator to determine the productive capacity of a process, and for selecting equipment and their combinations to achieve production objectives. To exemplify the potentialities of the EA, a case study is implemented in a Chilean copper truck fleet mining process.
Rocznik
Strony
583--591
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
  • Industrial Engineering Department, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso, Chile
  • Industrial Engineering Department, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso, Chile
autor
  • MINES ParisTech, PSL Research University, Sophia Antipolis, France
  • Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
  • Department of Nuclear Engineering, College of Engineering, Kyung Hee University, Republic of Korea
  • Mechanical Engineering Department, Universidad de Chile, Av. Beauchef 851, Santiago, Chile
autor
  • Industrial Engineering Department, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso, Chile
Bibliografia
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  • 2. Alarie S, Gamache M. Overview of solution strategies used in truck dispatching systems for open pit mines. International Journal of Surface Mining 2010; 16: 59-76, https://doi.org/10.1076/ijsm.16.1.59.3408.
  • 3. Birolini A. Reliability Engineering: Theory and Practice, 4th ed. Berlin: Springer-Verlag, 2017, https://doi.org/10.1007/978-3-662-54209-5.
  • 4. Burt C, Caccetta L. Equipment selection for surface mining: a review. Interfaces 2013; 44 (2): 143-162, https://doi.org/10.1287/inte.2013.0732.
  • 5. Chaowasakoo P, Seppälä H, Koivo H, Zhou Q. Improving fleet management in mines: The benefit of heterogeneous match factor. European Journal of Operational Research 2017; 261: 1052-1065, https://doi.org/10.1016/j.ejor.2017.02.039.
  • 6. Che H, Zeng S, Guo J. A reliability model for load-sharing k-out-of-n systems subject to soft and hard failures with dependent workload and shock effects. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22 (2): 253-264, https://doi.org/10.17531/ein.2020.2.8.
  • 7. Dhillon B. S. Maintainability, Maintenance, and Reliability for Engineers. Boca Raton: Taylor & Francis Group, LLC, 2000.
  • 8. Distefano S, Puliafito A. Reliability and availability analysis of dependent-dynamic systems with DRBDs. Reliability Engineering & System Safety 2009; 94: 1381-1393, https://doi.org/10.1016/j.ress.2009.02.004.
  • 9. Duran O, Rojas S, Duran P. Measuring the Impact of Maintenance Postponement on Overall Performance in a Chilean Crushing Plant. IEEE Latin America Transactions 2018; 16 (7): 1951-1958, https://doi.org/10.1109/TLA.2018.8447362.
  • 10. Fornasiero R, Zangiacomi A, Sorlini M. A cost evaluation approach for trucks maintenance planning. Production Planning & Control 2012;23 (2-3): 171-182, https://doi.org/10.1080/09537287.2011.591641.
  • 11. Fernández Pérez M, Oliveira F, Hamacher S. Optimizing workover rig fleet sizing and scheduling using deterministic and stochastic programming models. Industrial and Engineering Chemistry Research 2018; 57 (22): 7544-7554, https://doi.org/10.1021/acs.iecr.7b04500.
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  • 13. He Q, Zha YB, Zhang RJ, Liu TY, Sun Q. Reliability analysis for multi-state system based on triangular fuzzy variety subset bayesian networks. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2017; 19 (2): 158-165, https://doi.org/10.17531/ein.2017.2.2.
  • 14. Jenab K, B. Dhillon B. Assessment of reversible multi-state k-out-of-n:G/F/Load-Sharing systems with flow-graph models. Reliability Engineering & System Safety 2006; 91: 765-771, https://doi.org/10.1016/j.ress.2005.07.003.
  • 15. Jensen H, Jerez D. A stochastic framework for reliability and sensitivity analysis of large scale water distribution networks. Reliability Engineering & System Safety 2018; 176: 80-92, https://doi.org/10.1016/j.ress.2018.04.001.
  • 16. Kristjanpoller F, Crespo A, Viveros P, Mena R, Stegmaier R. A novel methodology for availability assessment of complex load sharing systems. European Safety and Reliability Conference (ESREL), Wroclaw, Poland, 2014, https://doi.org/10.1201/b17399-295.
  • 17. Kristjanpoller F, Crespo A, Viveros P, Barberá L. Expected Impact Quantification based Reliability Assessment Methodology for Chilean Copper Smelting Process - A Case Study. Advances in Mechanical Engineering 2016; 8: 1-13, https://doi.org/10.1177/1687814016674845.
  • 18. Li J, Wang Z, Ren Y, Yang D, Lv X. A novel reliability estimation method of multi-state system based on structure learning algorithm. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22 (1): 170-178, https://doi.org/10.17531/ein.2020.1.20.
  • 19. Macchi M, Kristjanpoller F, Garetti M, Arata A, Fumagalli L. Introducing buffer inventories in the RBD analysis of production systems. Reliability Engineering & System Safety 2012; 104: 84-95, https://doi.org/10.1016/j.ress.2012.03.015.
  • 20. Mazurkiewicz D. Computer-aided maintenance and reliability management systems for conveyor belts. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014; 16 (3): 377-382.
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  • 22. Nakousi C, Pascual R, Anani A, Kristjanpoller F, Lillo P. An asset-management oriented methodology for mine haul-fleet usage scheduling. Reliability Engineering & System Safety 2018; 180: 336-344, https://doi.org/10.1016/j.ress.2018.07.034.
  • 23. Sadoughi M, Li M, Hu C. Multivariate system reliability analysis considering highly nonlinear and dependent safety events. Reliability Engineering & System Safety 2018; 180: 189-200, https://doi.org/10.1016/j.ress.2018.07.015.
  • 24. Santelices G, Pascual R, Lüer-Villagra A, Mac Cawley A, Galar D. Integrating mining loading and hauling equipment selection and replacement decisions using stochastic linear programming. International Journal of Mining, Reclamation and Environment 2017; 31: 52-65, https://doi.org/10.1080/17480930.2015.1115589.
  • 25. Shengdao T, Fengquan W. Reliability analysis for a repairable parallel system with time-varying failure rates. Appl. Math 2005; 20(1): 85-90. https://doi.org/10.1007/s11766-005-0040-6
  • 26. Trivedi K, Malhotra M. Reliability and Performability Techniques and Tools: A Survey. Messung, Modellierung und Bewertung von Rechenund Kommunikationssystemen. Berlin: Springer, 1993, https://doi.org/10.1007/978-3-642-78495-8_3.
  • 27. Tsutsui M, Takata S. Life cycle maintenance planning method in consideration of operation and maintenance integration. Production Planning & Control 2012; 23 (2-3): 183-193, https://doi.org/10.1080/09537287.2011.591649.
  • 28. Viveros P, Zio E, Arata A, Kristjanpoller F. Integrated system reliability and productive capacity analysis of a production line. A Case Study for a Chilean Mining Process. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2012; 226:305-317, https://doi.org/10.1177/1748006X11408675.
  • 29. Volovoi V. Modeling of system reliability petri nets with aging tokens. Reliability Engineering & System Safety 2004; 84 (2): 149-161, https://doi.org/10.1016/j.ress.2003.10.013.
  • 30. Wijk O, Andersson P, Block J, Righard T. Phase-out maintenance optimization for an aircraft fleet. International Journal of Production Economics 2017; 188:105-115, https://doi.org/10.1016/j.ijpe.2017.01.002.
  • 31. Xu H, Xing L, Robidoux R. DRBD: Dynamic reliability block diagrams for system reliability modelling. International journal of computers and applications 2009; 202 (2): 2552-2561, https://doi.org/10.2316/Journal.202.2009.2.202-2552.
  • 32. Zhang L, Xia X. An Integer Programming Approach for Truck-Shovel Dispatching Problem in Open-Pit Mines. In Energy Procedia 2015; 75: 1779-1784, https://doi.org/10.1016/j.egypro.2015.07.469.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-585f0145-aaf8-43a9-81a5-1bc3bb2b9ab8
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