Capacity Planning for Large Multilocation Systems: Approximate and Incomplete Dynamic Programming Approaches

Abstract

Methods for planning capacity expansion typically have been restricted to problems with many locations in a static environment or a few locations in a dynamic environment. Two approaches are developed here for dynamic capacity planning problems with many locations. The first is an approximate approach based on an equivalent annual cost measure, and the second is a procedure for systematic improvement of the approximate solution. The method for improvement is called “incomplete dynamic programming” since it consists of an approximation to the first cycle of the dynamic programming policy iteration approach. Computational results are reported for tests of the methods against dynamic programming solutions for small problems. Applications are made to two versions of a large-scale problem of planning capacity expansion for India's nitrogenous fertilizer industry, and results are compared with those for other approaches.