Road construction projects are considered as the most important governmental issues since there are normally heavy investments required in such projects. There is also shortage of financial resources in governmental budget, which makes the asset allocation more challenging. One primary step in reducing the cost is to determine different risks associated with execution of such project activities. In this study, we present some important risk factors associated with road construction in two levels for a real-world case study of rail-road industry located between two cities of Esfahan and Deligan. The first group of risk factors includes the probability and the effects for various attributes including cost, time, quality and performance. The second group of risk factors includes socio-economical factors as well as political and managerial aspects. The study finds 21 main risk factors as well as 193 sub risk factors. The factors are ranked using groups decision-making method called linear assignment. The preliminary results indicate that the road construction projects could finish faster with better outcome should we carefully consider risk factors and attempt to reduce their impacts.

Measuring the relative efficiency of similar units has been an important topic of research among many researchers. Data envelopment analysis has been one of the most important techniques for measuring the efficiency of different units. However, there are some limitations on using such technique and some people prefer to use other methods such as analytical hierarchy process to measure the relative efficiencies. Besides, uncertainty in the input data is another issue, which makes some misleading results. In this paper, we present an integrated robust DEA-AHP to measure the relative efficiency of similar units. The proposed model of this is believed to capable of presenting better results in terms of efficiency compared with exclusive usage of DEA or AHP. The implementation of the proposed model is demonstrated for a real-world case study of Airport industry and the results are analyzed.

Data Envelopment Analysis (DEA) has been one of the most important tools on measuring the relative efficiency of different similar units such as transportation systems using terminals, airports, etc. In this study, we perform an empirical analysis on Iranian airports based on DEA methods to measure the efficiencies of various airports. One of the primary issues on many traditional DEA methods is that the data are almost always contaminated with noise. We use a DEA method which could handle the uncertainty associated with input and output data. The results of this comprehensive study show that most of the active airlines are practically inefficient and the government could significantly increase the efficiencies of the airports by setting new regulations and rules.