Neural networks, stochastic dynamic programming and a heuristic for valuing flexible manufacturing systems

We compare the use of stochastic dynamic programming (SDP), Neural Networks and a simple approximation rule for calculating the real option value of a flexible production system. While SDP yields the best solution to the problem, it is computationally prohibitive for larger settings. We test two approximations of the value function and show that the results are comparable to those obtained via SDP. These methods have the advantage of a high computational performance and of no restrictions on the type of process used. Our approach is not only useful for supporting large investment decisions, but it can also be applied in the case of routine decisions like the determination of the production program when stochastic profit margins occur. (author's abstract)