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.

Selection of an appropriate contractor plays an important role for the success of the accomplish-ment of any construction project. This paper presents a multi-criteria decision making method for contractor selection. The proposed study uses six criteria, namely; Experience, Financial stability, Quality performance, Manpower resources, Equipment resources and Current workload for evaluating different contractors. Using analytical hierarchy process, the study ranks these criteria and finds the relative importance of them. Next, The technique for Order of Preference by Simi-larity to ideal Solution (TOPSIS) is used to rank the alternative contractors according to these criteria.

Contractor selection is a critical activity, which plays an important role in the overall success of any construction project. The implementation of fuzzy multiple criteria decision attribute (MCDA) in selecting contractors has the advantage of rendering subjective and implicit decision making more objective and transparent. An additional merit of fuzzy MCDA is the ability to accommodate quantitative and qualitative information. In this paper, an integrated VIKOR–AHP methodology is proposed to make a selection among the alternative contractors in one of Iranian construction industry projects. In the proposed methodology, the weights of the selection criteria are determined by fuzzy pairwise comparison matrices of AHP.

Contractor selection plays essential role on development of business industries and any selection strategy is normally involved with various factors. This paper presents an empirical investigation to determine important factors influencing contractor selection to increase profitability. The proposed model of this paper is implemented for an Iranian constructor firm named Kayson Company located in city of Tehran, Iran. The study has been accomplished among top managers who worked for this firm. Using structural equation modeling, the study determines that five factors including performance, believes, flexibility, quality, price and services influence on profitability when a contractor is to be chosen.