Ranking of building maintenance contractors using multi-criteria decision making methods and an artificial neural network model

Raad, N.; Isfahani, N.

Abstract: Building Maintenance plays an important role throughout the building lifecycle from devis-ing conceptual plans to the end. Due to the high cost of building maintenance and the direct impact of maintenance effectiveness on the quality of life of building occupants, special attention must be devoted. One of the most important issues in this field is building maintenance contractor selection. This issue becomes even more critical in public buildings, such as hospitals, offices, and military centers. The purpose of this study is to present a method that can be used to select the contractor in such a way that the response robustness is high and the employed method is the most accurate one among other similar methods. To do this, the contractors are ranked by 7 multi-criteria decision-making methods. Then, the Spearman correlation coefficients are obtained for each pair of methods. When there is a significant difference between the outcomes of the methods, the output of each method is compared with the output of the Artificial Neural Network (ANN) model. The method with the least difference with the neural network output is taken as the superior method. After selecting the best method, a robustness analysis is performed on it to verify the stability of the answer. The proposed model is implemented on a real case study. Statistical analysis shows that the implementation of this method has increased the satisfaction of the residents.

How to cite this paper

 Raad, N & Isfahani, N. (2020). Ranking of building maintenance contractors using multi-criteria decision making methods and an artificial neural network model.International Journal of Data and Network Science, 4(2), 245-254.

References

Alinejad, A. & Khalili, J. (2019). New methods and applications in multiple attribute decision-making (MADM). 1st ed. Cham, Switzerland: Springer.
Brans, J. P., Vincke, P. & Mareschal, B. (1986). How to select and how to rank projects: The Promethee method. European Journal of Operational Research, 24(2), 228-238.
Brauers, W. K. M. (2008). Multi-objective contractor’s ranking by applying the MOORA method. Journal of Business Economics and Management, (4), 245-255.
Chakraborty, S. & Zavadskas, E. K. (2014). Applications of WASPAS method in manufacturing. Informatica, 25(1), 1–20.
Cooper, J. (2015). Sustainable building maintenance within social housing, s.l.: PhD thesis, University of Greenwich.
Diakoulaki, D., Mavrotas, G. & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.
Gardner, M. W. & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14-15), 2627-2636.
Hashemkhani Zolfani, S. & Saparauskas, J. (2013). New application of SWARA method in prioritizing sustainability assessment indicators of energy system. Inzinerine Ekonomika-Engineering Economics, 24(5), 408-414.
Hawkins, F. H. (1993). Human Factors in Flight. 2nd ed. London: Routledge.
Hossam, T. A. M., Eid, A. F. & Khodeir, L., (2019). Identifying the impact of integrating building information modeling with maintenance management, A literature review. Journal of Al-Azhar University Engineering Sector, 14(51), 627-641.
Khudhair, H. A. & Isik, Z. (2018). Key performance indicators in retrofitting projects: A review. Istanbul (Turkey), 10th Int'l Conference on Studies in Architecture, Civil, Design & Environmental Engineering (SACDEE-18).
Kivrak, S., Arslan, G. & Birgonul, M. T. (2008). Capturing knowledge in construction projects: Knowledge platform for contractors. Journal of Management in Engineering, 24(2).
Lam, E. W., Chan, A. P. & Chan, D. W. (2010). Benchmarking success of building maintenance projects. Facilities, 28(5/6), 290 - 305.
Lavy, S. & Bilbo, D. L. (2009). Facilities maintenance management practices in large public schools, Texas. Facilities, 27(1/2), 5-20.
Njuangang, S., Liyanage, C. & Akintoye, A. (2015). Key performance measures to control maintenance-associated HAIs. International Journal of Health Care Quality Assurance, 28(7), 690-708.
Nourianfar, K. & Montazer, G. A. (2013). A fuzzy MCDM approach based on COPRAS method to solve supplier selection problems. Shiraz, Iran, IEEE.
Shanmugam, A. & Paul Robert, T. (2015). Ranking of aircraft maintenance organization based on human factor performance. Computers & Industrial Engineering, 88, 410–416.
Wolters, W. & Mareschal, B. (1995). Novel types of sensitivity analysis for additive MCDM methods. European Journal of Operational research, 81(2), 281-290.
Zampighi, L. M., Kavanau, C. L. & Zampighia, G. A. (2004). The Kohonen Self-Organizing Map: a Tool for the Clustering and Alignment of Single Particles Imaged Using Random Conical Tilt. Journal of Structural Biology, 146, 368– 380.
Zavadskas, E. K. & Turskis, Z. (2011). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and Economic Development of Economy, 16(2), 159-172.