A scenario-based stochastic programming approach for technology and capacity planning

In response to market pressures resulting in increased competition, product proliferation and greater customization, rms in many industries have adopted modern technologies to provide operational exibility on several dimensions. In this paper, we consider the role of product mix exibility, de ned as the ability to produce a variety of products, in an environment characterized by multiple products, uncertainty in product life cycles and dynamic demands. Using a scenario based approach for capturing the evolution of demand, we develop a stochastic programming model for determining technology choices and capacity plans. Since the resulting model is likely to be large and may not be easy to solve with standard software packages, we develop a solution procedure based on augmented Lagrangian method and restricted simplicial decomposition. The scope of our approach for deriving context speci c managerial insights is illustrated by the results of limited computations. Finally, we demonstrate the versatility of our approach by deriving a special case of the general model to address some tactical issues related to new product introduction.