The aim of the present study is to find the relative importance of factors influencing the stu-dents’ decisions to adopt Massive Open Online Courses (MOOCs). Eight sub-factors catego-rized under three main factors namely “benefits of MOOCs”, “MOOCs features” and “social recognition”, are considered for prioritization. The analytic hierarchy process (AHP) method-ology is also employed to prioritize the factors. The primary data pertaining to pair-wise com-parisons of various factors and sub-factors have been obtained from 250 students by using convenience sampling. The results indicate that academic recognition, followed by openness, autonomy and cost effectiveness of MOOCs are the most important aspects which students consider while deciding to learn through MOOCs.

The existence of hydroelectric plants along Amazon River tributaries is a solution to satisfy the energy demand in Brazil. However, these plants are subjected to multiple risk events because of the geographic and socioeconomic characteristics of this region. In helping to address these escalating challenges, this paper presents a framework that assesses the risk events of service packs relevant to the plant. This framework presents a transparent approach for prioritizing risk events in large projects. The weights of importance of risk events are estimated using the fuzzy analytic hierarchy process. Chang’s extent analysis method takes into consideration the vagueness and imprecision of subjective human judgments. The convergence of decisions is evaluated using two aggregation approaches, namely the maximum-minimum method based on an arithmetic mean and a geometric mean. The performances of the original and modified extent analysis methods are compared using group Euclidean distance and distance between weights metrics. The degree of similarity between the evaluation metrics is examined using Spearman’s rank correlation coefficient and average overlap approaches. Due to the inconsistency of the reported results, the final rankings of the aggregation approaches are determined using a new aggregated multiple criteria decision making method. The results indicate that the original extent analysis method using the maximum-minimum method (arithmetic mean) is the best aggregation method. A Santo Antonio hydroelectric plant in Brazil is used to demonstrate the application of the proposed framework.

Over the past four decades, portfolio selection has been one of the most important con-cerns of researchers, project managers, project-oriented companies, and public agencies around the world. Although numerous studies have been done in this field, still there is a room for more improvement in both theory and practice. One of the yet unspoiled topics in this field is improving and balancing the efficiency of sub-portfolios while paying attention to portfolio optimization. This study employs data-mining tools to categorize projects into sub-portfolios and rank them. Multiple Criteria Decision Making (MCDM) methods are also used to weigh the criteria on which the ranking process is based. Finally, a novel multi-objective model is designed to optimize the efficiency of sub-portfolios and the gain of the main portfolio. The model is solved by NSGA II algorithm. This study introduces a hybrid framework by which project portfolio selection process can be carried out regarding strategic alignment, cost, and risk.