Proposing a Hybrid Approach to Predict, Schedule and Select the Most Robust Project Portfolio under Uncertainty

Suitable project portfolio selection in inconsistent economy that can reduce the portfolio risks and increasing utilities for investors has gained significant research attentions.   This article addresses the project portfolio selection in which conventional certain (1) prediction, (2) optimization and (3) clustering approaches cannot be used to face uncertainty. To predict the real value of affecting project risk parameters, neural network has been used; Then to determine the optimized sequences and procedures, the proposed model have been evaluated using the multi-objective shuffle frog leaping algorithm (SFLA) by robust optimization approach; To suggest different risk criteria, K-means algorithm utilized to categorize the candidate projects and differentiating the clusters.  As the proposed hybrid methodology is studied on 420 different construction projects in an Iranian construction company in two economic stable years and an instable year in Iran real estate market. The results show 96 percent prediction-optimization capability due to different desired criteria

An Investigation on the Effect of Balanced Scorecard on Employees’ Performances in Social Security Organization

This research is aimed at studying the effect of balanced scorecard (BSC) on employees’ performances in social security organization (SSO). This is a descriptive applied research which was implemented within the first six months of 2012. The statistical population includes employees, managers and the clients of SSO in one of western cities in Iran. According to Morgan table, the sample volume consists of 246 who were randomly selected. To collect information, two questionnaires with two coefficients of reliability of 0.880 and 0.890 were employed and descriptive and inferential statistics were practiced to analyze hypotheses. Results revealed that BSC has a positive impact on employees’ performances in SSO

Development of a complementary fuzzy decision support system for employees’ performance evaluation

This study aims to improve employee evaluation system in one of the leading automobile manufacturers in Iran by designing a fuzzy decision support system (F.D.S.S.). Since this manufacturer is a large-sized company with over 35,000 employees, the number of managers regularly evaluated requires too much capacity from the human resource team and hence increases the rate of possible misjudgements. However, the proposed F.D.S.S. can reduce the rate of unfair or inconsistent assessments by converting qualitative assessments of the panel to linguistic variables. This action increases the precision of assessment and improves the quality of evaluations. The proposed F.D.S.S. is compared with a fuzzy TOPSIS method to confirm its reliability and validity in which the results show consistency with fuzzy TOPSIS. As a result, the F.D.S.S. is implemented for evaluation of managers in this auto-mobile company instead of the traditional method

Proposing a neural network model to predict time and cost claims in construction projects

Despite broad improvements in construction management, claims still are an inseparable part of many con-struction projects. Due to huge cases of claim in construction industry, this study argues that claim management is a significant factor in construction projects success. In this study, the most possible causes of these emerging claims are identified and statistically ranked by Probability-Impact Matrix. Subsequently, by classifying claims in different cases, the most important ones are ranked in order to achieve a better understanding of claim management in each project. In this regard, a new index is defined, being able to be applied in a variety of projects with different time and cost values, to calculate the amount of possible claims in each project along with related ratios with respect to the cost and time of each claim. This study introduces a new model to predict the frequency of claims in construction projects. By using the proposed model, the rate of possible claims in each project can be obtained. This model is validated by applying it into fitting case studies in Iran construction industry

Project Portfolio Construction Using Extreme Value Theory

Choosing proper projects has a great impact on organizational success. Firms have various factors for choosing projects based on their different objectives and strategies. The problem of optimization of projects’ risks and returns is among the most prevalent issues in project portfolio selection. In order to optimize and select proper projects, the amount of projects’ expected risks and returns must be evaluated correctly. Determining the relevant distribution is very important in achieving these expectations. In this research, various types of practical distributions were examined, and considering expected and realized risks, the effects of choosing the different distribution on estimation of risks on construction projects were studied.This article belongs to the Special Issue Sustainable Decision Making in Civil and Construction Engineerin

Application of Duration Measure in Quantifying the Sensitivity of Project Returns to Changes in Discount Rates

In this research, the concept of Duration with a new application in project management has been defined. The Duration of each project provides the project manager with a combined measure containing concepts of return, cost and time of the project. Further in this article, the changes in project return, based on different assumptions such as discount rate, have been examined. To examine the effect of the changes in these factors, the Monte Carlo simulation has been used. The relationship between these factors is nonlinear which reflects the great importance of investment on appropriate risk management systems. The data from a set of construction projects have been used in order to verify the results of this study. Similar relationships can be expected to exist in other industries as well.This article belongs to the Special Issue Rational Decision Making in Risk Managemen

Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models

This paper attempted to calculate the market risk in the Tehran Stock Exchange by estimating the Conditional Value at Risk. Since the Conditional Value at Risk is a tail-related measure, Extreme Value Theory has been utilized to estimate the risk more accurately. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models were used to model the volatility-clustering feature, and to estimate the parameters of the model, the Maximum Likelihood method was applied. The results of the study showed that in the estimation of model parameters, assuming T-student distribution function gave better results than the Normal distribution function. The Monte Carlo simulation method was used for backtesting the Conditional Value at Risk model, and in the end, the performance of different models, in the estimation of this measure, was compared

Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models

This paper attempted to calculate the market risk in the Tehran Stock Exchange by estimating the Conditional Value at Risk. Since the Conditional Value at Risk is a tail-related measure, Extreme Value Theory has been utilized to estimate the risk more accurately. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models were used to model the volatility-clustering feature, and to estimate the parameters of the model, the Maximum Likelihood method was applied. The results of the study showed that in the estimation of model parameters, assuming T-student distribution function gave better results than the Normal distribution function. The Monte Carlo simulation method was used for backtesting the Conditional Value at Risk model, and in the end, the performance of different models, in the estimation of this measure, was compared.This article belongs to the Special Issue Rational Decision Making in Risk Managemen