AIMS Energy, 2018, 6(6): 1009-1024. doi: 10.3934/energy.2018.6.1009.

Research article

Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Developing a model for improving the productivity and energy production of small-scale power plants using the physical asset management model in a fuzzy environment

1 Department of Civil Engineering, Sari Branch, Islamic Azad University, Sari, Iran
2 Department of Civil Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran

The electric power industry today is regarded as the engine of growth and development of other sectors. The technology-based nature of the electric power industry has caused its physical asset to be of particular importance. This study aims to develop a model for increasing the productivity and energy production of small-scale power plants using multi-criteria decision making in fuzzy environments. Productivity of manpower, capital, energy and quality, as the criteria affecting the purpose of the research, and ten activities of the uptime physical asset management model as a solution to meet the goal of the research have been considered.
Based on the obtained results, it can be said that teamwork-based methods are the most important strategy for improving the productivity of small-scale power plants. The importance of teamwork is to the extent that other areas of physical asset management fail to function properly without relying on teamwork-based methods. The results of the study indicate that the proposed model is suitable for real-world problems and increases the productivity and uptime at small-scale power plants.
  Article Metrics

Keywords productivity; energy; physical asset management; Uptime Model; fuzzy TOPSIS

Citation: Mehrdad masoudnejad, Morteza rayati damavandi, Siroos gholampoor. Developing a model for improving the productivity and energy production of small-scale power plants using the physical asset management model in a fuzzy environment. AIMS Energy, 2018, 6(6): 1009-1024. doi: 10.3934/energy.2018.6.1009


  • 1. Sharma RK, Kumar D, Kumar P (2005) FLM to select suitable maintenance strategy in process industries using MISO model. J Qual Maint Eng 11: 359–374.    
  • 2. Jafari A, Jafarian M, Zareei A, et al. (2008) Using fuzzy Delphi method in maintenance strategy selection problem. J Uncertain Syst 2: 289–298.
  • 3. Zaim S, Turkyılmaz A, Acar MF, et al. (2012) Maintenance strategy selection using AHP and ANP algorithms: A case study. J Qual Maint Eng 18: 16–29.    
  • 4. Mahakul TK, Baboo S, Patnaik S, et al. (2005) Implementation of enterprise asset management using IT tools: A case study of IB thermal power station. J Inf Technol Manage 16: 39.
  • 5. Hastings NAJ (2010) Physical asset management. Springer Science & Business Media.
  • 6. de la Fuente A, González-Prida V, Crespo A, et al. (2018) Advanced Techniques for Assets Maintenance Management. IFAC-PapersOnLine 51: 205–210.
  • 7. Greyling BT, Jooste W (2017) The application of business process mining to improving a physical asset management process: A case study. S Afr J Ind Eng 28: 120–132.
  • 8. von Petersdorff H, Vlok PJ (2014) Prioritising maintenance improvement opportunities in Physical Asset Management. S Afr J Ind Eng 25: 154–168.
  • 9. PAS-55 (2010) PAS-55: Asset management. British Standards Institute.
  • 10. ISO 55000 (2013) ISO 55000. International Organisation for Standardization.
  • 11. Kriege LK, Jooste JL, Vlok PJ (2016) A framework for establishing a human asset register for the improved management of people in physical asset management. S Afr J Ind Eng 27: 77–89.
  • 12. Chiacchio F, D'Urso D, Famoso F, et al. (2018) On the use of dynamic reliability for an accurate modelling of renewable power plants. Energy 151: 605–621.    
  • 13. Lam PL, Shiu A (2004) Efficiency and productivity of China's thermal power generation. Rev Ind Org 24: 73–93.    
  • 14. Rácz VJ, Vestergaard N (2016) Productivity and efficiency measurement of the Danish centralized biogas power sector. Renew Energ 92: 397–404.    
  • 15. Barros CP, Wanke P (2017) Efficiency in Angolan thermal power plants: Evidence from cost structure and pollutant emissions. Energy 130: 129–143.    
  • 16. Lee CH, Leem CS (2016) An empirical analysis of issues and trends in manufacturing productivity through a 30-year literature review. S Afr J Ind Eng 27: 147–159.
  • 17. Alsyouf I (2007) The role of maintenance in improving companies' productivity and profitability. Int J Prod Econ 105: 70–78.    
  • 18. Nachlas JA (1998) Productivity Enhancement Using Analytically Based Maintenance Planning. IFAC Proc Vol 31: 975–979.    
  • 19. Raouf A (1994) Improving capital productivity through maintenance. Int J Oper Prod Manage 14: 44–52.
  • 20. KW Wong K, Kumaraswamy M, Mahesh G, et al. (2014) Building integrated project and asset management teams for sustainable built infrastructure development. J Facil Manage 12: 187–210.    
  • 21. Schneider J, Gaul AJ, Neumann C, et al. (2006) Asset management techniques. Int J Elec Power 28: 643–654.    
  • 22. Abu-Elanien AEB, Salama MMA (2010) Asset management techniques for transformers. Electr Pow Syst Res 80: 456–464.    
  • 23. El-Akruti K, Dwight R (2013) A framework for the engineering asset management system. J Qual Maint Eng 19: 398–412.    
  • 24. Burnett S, Vlok P (2014) A simplified numerical decision-making methodology for physical asset management decisions. S Afr J Ind Eng 25: 162–175.
  • 25. Zadeh LA (1996) Fuzzy sets. In Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems, 394–432.
  • 26. Fattahi R, Khalilzadeh M (2018) Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment. Safety Sci 102: 290–300.    
  • 27. Hwang CL, Yoon K (1981) Methods for multiple attribute decision making, In: Multiple Attribute Decision Making, Springer, Berlin, Heidelberg, 58–191.
  • 28. Yoon K (1987) A reconciliation among discrete compromise solutions. J Oper Res Soc 38: 277–286.    
  • 29. Hwang CL, Lai YJ, Liu TY (1993) A new approach for multiple objective decision making. Comput Oper Res 20: 889–899.    
  • 30. Chen CT (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Set Syst 114: 1–9.    
  • 31. Şengül Ü, Eren M, Shiraz SE, et al. (2015) Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renew Energ 75: 617–625.    
  • 32. Ervural BC, Zaim S, Demirel OF, et al. (2017) An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey's energy planning. Renew Sust Energ Rev 82: 1538–1550.
  • 33. Bilbao-Terol A, Arenas-Parra M, Cañal-Fernández V, et al. (2014) Using TOPSIS for assessing the sustainability of government bond funds. Omega 49: 1–17.    
  • 34. Tavana M, Keramatpour M, Santos-Arteaga FJ, et al. (2015) A fuzzy hybrid project portfolio selection method using data envelopment analysis, TOPSIS and integer programming. Expert Syst Appl 42: 8432–8444.    
  • 35. Walczak D, Rutkowska A (2017) Project rankings for participatory budget based on the fuzzy TOPSIS method. Eur J Oper Res 260: 706–714.    
  • 36. de Almeida AT, de Almeida JA, Costa APCS, et al. (2016) A new method for elicitation of criteria weights in additive models: Flexible and interactive tradeoff. Eur J Oper Res 250: 179–191.    
  • 37. Marichal JL, Roubens M (2000) Determination of weights of interacting criteria from a reference set. Eur J Oper Res 124: 641–650.    
  • 38. Takeda E, Yu PL (1995) Assessing priority weights from subsets of pairwise comparisons in multiple criteria optimization problems. Eur J Oper Res 86: 315–331.    
  • 39. Choo EU, Schoner B, Wedley WC (1999) Interpretation of criteria weights in multicriteria decision making. Comput Ind Eng 37: 527–541.    
  • 40. Choo EU, Wedley WC (2004) A common framework for deriving preference values from pairwise comparison matrices. Comput Oper Res 31: 893–908.    
  • 41. Lin HF (2010) An application of fuzzy AHP for evaluating course website quality. Comput Educ 54: 877–888.    
  • 42. Enflo K, Kander A, Schön L (2009) Electrification and energy productivity. Ecol Econ 68: 2808–2817.    
  • 43. Ratnasingam J, Ark CK, Mohamed S, et al. (2017) An Analysis of Labor and Capital Productivity in the Malaysian Timber Sector. BioResources 12: 1430–1446.
  • 44. Sauian MS, Kamarudin N, Rani RM (2013) Labor productivity of services sector in Malaysia: Analysis using input-output approach. Procedia Econ Financ 7: 35–41.    
  • 45. Koch MJ, McGrath RG (1996) Improving labor productivity: Human resource management policies do matter. Strategic Manage J 17: 335–354.    
  • 46. Du K, Lin B (2017) International comparison of total-factor energy productivity growth: A parametric Malmquist index approach. Energy 118: 481–488.    
  • 47. Weber CM, Yang J (2014) Organizational learning and capital productivity in semiconductor manufacturing. IEEE T Semiconduct M 27: 316–326.    
  • 48. Martínez-Caro E, Cegarra-Navarro JG (2010) The impact of e-business on capital productivity: An analysis of the UK telecommunications sector. Int J Oper Prod Manage 30: 488–507.    
  • 49. Zhang Q, Sun Z, Wu F, et al. (2016) Understanding rural restructuring in China: The impact of changes in labor and capital productivity on domestic agricultural production and trade. J Rural Stud 47: 552–562.    
  • 50. Abhishek K, Chatterjee S, Datta S, et al. (2017) Integrating Principal Component Analysis, Fuzzy Linguistic Reasoning and Taguchi Philosophy for Quality-Productivity Optimization. Mater Today 4: 1772–1777.    
  • 51. Sulaiman F, Zailani S, Ramayah T (2012) Intranet portal utilization: Monitoring tool for productivity-quality and acceptance point of view. Procedia-social Behav Sci 65: 381–386.    
  • 52. Moradinaftchali V, Song L, Wan X (2016) Improvement in quality and productivity of an assembled product: A riskless approach. Comput Ind Eng 94: 74–82.    
  • 53. Wireman T (2004) Benchmarking best practices in maintenance management. Industrial Press Inc.
  • 54. Campbell JD, Reyes-Picknell JV (2015) Uptime: Strategies for excellence in maintenance management. CRC Press.


Reader Comments

your name: *   your email: *  

© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved