A continuous‐time strategic capacity planning model

Capacity planning decisions affect a significant portion of future revenue. In the semiconductor industry, they need to be made in the presence of both highly volatile demand and long capacity installation lead‐times. In contrast to traditional discrete‐time models, we present a continuous‐time stochastic programming model for multiple resource types and product families. We show how this approach can solve capacity planning problems of reasonable size and complexity with provable efficiency. This is achieved by an application of the divide‐and‐conquer algorithm, convexity, submodularity, and the open‐pit mining problem. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

A general strategic capacity planning model under demand uncertainty

Capacity planning decisions affect a significant portion of future revenue. In equipment intensive industries, these decisions usually need to be made in the presence of both highly volatile demand and long capacity installation lead times. For a multiple product case, we present a continuous‐time capacity planning model that addresses problems of realistic size and complexity found in current practice. Each product requires specific operations that can be performed by one or more tool groups. We consider a number of capacity allocation policies. We allow tool retirements in addition to purchases because the stochastic demand forecast for each product can be decreasing. We present a cluster‐based heuristic algorithm that can incorporate both variance reduction techniques from the simulation literature and the principles of a generalized maximum flow algorithm from the network optimization literature. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006