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In project portfolio selection problem, the uncertainty of parameters can bring high risk to decision making. Thus, it has a great realistic significance to build an appropriate robust optimization model and provide companies with a robust solution, which is able to handle the uncertainty of the parameters. This paper first analyzes the characteristics of the mathematical model of divisible project portfolio selection problem when parameters are certain. Furthermore, the concept of uncertainty set is introduced. Then we provide decision makers with a method which enables them determine the size of uncertainty set based on their preference. A new robust optimization model based on uncertainty set is then formulated, which enables decision makers make a trade-off between robustness and optimality. A robust solution, which is feasible and optimal even in the worst-case of the determined uncertainty set is provided. Finally, we use GAMS/BARON to conduct a numerical example and highlight the capability and characteristics of the proposed model. From the theoretical perspective, this paper first extends the robust optimization theory to the divisible project portfolio selection problem. In view of the problem that the existing robust optimization theory can only handle the project portfolio selection problem with finite feasible solutions, this paper provides a new robust optimization method, which is able to handle the divisible project portfolio selection problem with infinite feasible solutions. From the practical perspective, with the rapid development of high and new technology industries, the investment of newly-developing projects, such as R&D projects and IT/IS projects, has been attached increasing importance. This kind of projects make it become increasingly urgent to explore the new robust optimization theory for project portfolio selection problem owing to their high uncertainty. Thus, this paper has good practical significance for companies to prevent investment risk. [ABSTRACT FROM AUTHOR]

Based on manually collected data on cross-border operation of the company from year 2003 to 2017, this paper examines the impact of the company's transboundary level on the analyst's earnings forecast behavior as well as the market consequence. The empirical results show that the higher the company's transboundary level, the lower the accuracy of the analyst's earnings forecast, and the higher the frequency of release as well as the forecast correction extent. At the same time, the improvement of the company's information disclosure quality will help ease the negative impact of the company's transboundary on analysts' forecasting behavior. The above results support the information processing cost hypothesis and information uncertainty hypothesis. Finally, the paper finds that the higher the company's transboundary level, the more negative the impact on the analyst's forecasted behavior and the higher the company's market discount. [ABSTRACT FROM AUTHOR]

In order to solve the uncertain decision making problems with information uncertainty and incomplete information, the Dempster-Shafer theory-based intuitionistic trapezoidal fuzzy IOWA (DS-TrIFIOWA) operator is defined. Firstly, the intuitionistic trapezoidal fuzzy numbers and their corresponding algorithms and aggregation operators are introduced. Then, based on Dempster-Shafer theory and intuitionistic trapezoidal fuzzy numbers, taking into account the decision-makers' subjective characteristics, DS-TrIFIOWA operator is defined and some properties on the DS-TrIFIOWA operator are presented. Further, a novel method based on DS-TrIFIOWA operator for decision making under uncertainty is developed. Finally, an illustrative example shows the feasibility and availability of the proposed method. [ABSTRACT FROM AUTHOR]

In view of the correlations among multiple responses and information uncertainty of design of experiment (DOE), a new method of robust parameter design for dynamic multi-response system was proposed. Firstly, desirability function was used to measure dynamic system sensitivity and variation, and principal component analyses on the two indices were conducted. Secondly, grey relational analysis was used to gain the grey relational grade (GRD) of principal component sequences relative to their respective ideal sequences, and transferred them into integrated grey relational grad (IGRD) by means of TOPSIS, then identified the optimal level combination of control factors based on the IGRD. Finally, the validity and superiority of the proposed method are confirmed through two case studies. [ABSTRACT FROM AUTHOR]