Uncertain mean-variance and mean-semivariance models for optimal project selection and scheduling

This paper discusses a joint problem of optimal project selection and scheduling in the situation where initial outlays and net cash inflows of projects are given by experts’ estimates due to lack of historical data. Uncertain variables are used to describe these parameters and the use of them is justified. A new mean-variance and a mean-semivariance models are proposed considering relationship and time sequence order between projects. In order to solve the complex problems, the methods for calculating uncertain lower partial semivariance and higher partial semivariance values are introduced and a hybrid intelligent algorithm which integrates genetic algorithm with cellular automation is provided. In addition, two examples are presented to illustrate the application and significance of the new models, and numerical experiments are done to show the effectiveness of the proposed algorithm.