These days companies are competing in a fast changing environment. To keep its competitiveness, the company needs not only to seek and select new investment opportunities but also to adjust its existing projects. This paper discusses an optimal project selection and adjusting problem under capital and land resource limitations. Due to the complex and dynamic nature of the economic environment, the project parameters such as initial outlays, upgrade expenditures and net cash flows are treated as random variables. Net present value method is employed to calculate the investment return, and a mean–variance optimal adjustment and selection model is developed. To solve the proposed optimization problem with big number decision variables, a cellular binary particle swarm optimization which hybridizes cellular automation and particle swarm optimization is proposed. As an illustration of the proposed algorithm, a numerical example is also presented.
