In this paper, a new hybrid AHP and Dempster-Shafer Theory of Evidence is presented for solving the problem of choosing the best project among a list of available alternatives while uncertain risk factors are taken into account. The aim is to minimize overall risks. For this purpose, four groups of risk factors, including Properties, Operational and Technological, Financial, Strategic risk factors, are considered. Then using an L24 Taguchi method, several experiments with various dimensions have been designed and solved by the proposed algorithm. The outcomes are then analyzed using the Validating Index (VI), Reduced Risk Indicator (R.R.I%), and Solving time. The findings indicated that, compared to the classic AHP, the results of the proposed hybrid method were different in most cases due to uncertainty of risk factors. It was observed that the method could be safely used for selecting project problems in real industries.

There are many researches on project selection field, but few of them have considered environmental criteria in their studies. Moreover, there are many articles in evaluating risk but there is no article that considers value at risk concept to evaluate the amount of risk in multi project selection. We propose a multi objective mathematical model to address a situation in which several projects are candidate to be invested completely or partially. Three objective functions are considered in this paper. The first objective maximizes sum of the net present value of pure cash flow obtained from selected projects. In this objective, we consider the factor of time and its impact on value of money. In addition, we use the concept of value at risk (VAR) as the present value for the first time in project selection problems. Although green projects are more interesting, there are few articles, which address environmental issues. Hence, we consider the objective, which are related to environmental issues as the final criterion. Multi-Objective Differential Evolution algorithm (MODE) algorithm is applied to solve a problem, which is known as robust and efficient algorithm. Then computational results are compared with solutions obtained by NSGA-II algorithm which is well-known algorithm in this field with respect to seven comparison metrics.

Six Sigma is a strategic approach of significant value in achieving overall excellence. It helps to accomplish the organizations strategic aim through the effectual use of project controlled methodology. As Six Sigma is a project controlled approach, it is necessary to prioritize projects which give utmost economic benefits to the firm. In real practice, Six Sigma projects selection is very tough assignment because poor project selection also happens even in the well-managed organizations and this can weaken the success and trustworthiness of the Six Sigma practice. The present study aims to develop a project selection approach based on a combination of fuzzy and MADM technique to help organizations determine proper Six Sigma projects and identify the priority of these projects mainly in automotive companies. VIKOR and TOPSIS methods have been used to select the proper Six Sigma project composed with fuzzy logic. In this context, seven critical parameters have been considered for selection of finest alternative. The weights of evaluation criteria are obtained using the MDL (modified digital logic) method and final ranking is calculated through primacy index obtained by using fuzzy based VIKOR and TOPSIS methodology. A factual case study from automotive industry is used to investigate the efficacy of the planned approach.

Selecting from a pool of interdependent projects under certainty, when faced with resource constraints, has been studied well in the literature of project selection problem. After briefly reviewing and discussing popular modeling approaches for dealing with uncertainty, this paper proposes an approach based on chance constrained programming and utility theory for a certain range of problems and under some practical assumptions. Expected Utility Programming, as the proposed modeling approach, will be compared with other well-known methods and its meaningfulness and usefulness will be illustrated via two numerical examples and one real case.

Six Sigma is a philosophy of unremitting improvement and excellence in all aspects. The concept is a satisfactory modification process tool through customers, continuous improvement and stakeholder participation. Six Sigma is considered as statistical analysis, assessment scales and customer-oriented production accomplishments and it leads to defect production reduction. This paper recommends an approach to select Six Sigma projects using fuzzy multiple attribute decision making techniques composed with another concoction tool. Through insightful quarrying of literature, rudimentary criteria for selecting Six Sigma projects were revealed. The fundamental criteria were identified consuming the fuzzy hypothesis test. Having identified the most indispensable criteria, the weight of criteria were determined. Appling FANP techniques. Having calculated the weights pertinent to criteria through three methods, SAW, TOPSIS, and Fuzzy VIKOR, Six Sigma projects were introduced and prioritized. Applying the three methods engendered various results, which required the application of an amalgamation technique, entitled as Borda and it helped to clarify the final project rate.

Nowadays, selection of an optimal project has become a challenging task for managers and decision makers. Project selection for a decision maker can be viewed as a complicated multi-criteria decision making (MCDM) problem, which requires consideration of a number of conflicting, tangible and intangible selection criteria. Moreover, decision makers tend to use linguistic terms for expressing their assessments because of their different backgrounds and preferences, some of which may be uncertain and incomplete. Hence, this paper focuses on developing a hybrid fuzzy MCDM approach by combining AHP and VIKOR for solving the project selection problem. Finally, A numerical example is proposed to illustrate an application of the proposed method.

Project selection is considered as the first essential part of project portfolio management. Project selection is also considered as a process to evaluate each project idea and chooses the one with the biggest priority. Project selection plays an essential role in the entire life cycle of different projects. This paper presents a survey for project selection using multiple criteria decision mak-ing techniques. The study considers 60 papers from over the period 1980-2017. The results of the survey have indicated that integration of Order of Preference by Similarity to Ideal Solution (TOPSIS) and analytical hierarchy process/analytical network process was the most popular techniques for project selection followed by VIKOR method.