Capacity Planning Model
A General Strategic Capacity Planning Model under Demand Uncertainty
Woonghee Tim Huh,1 Robin O. Roundy,2 Metin Çakanyildirim3
1
Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027
2
School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York 14853
3
School of Management, University of Texas at Dallas, Richardson, Texas 75083
Received 29 October 2003; revised 24 August 2005; accepted 30 September 2005 DOI 10.1002/nav.20128 Published online 12 December 2005 in Wiley InterScience (www.interscience.wiley.com).
Abstract: 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 53: 137–150, 2006 Keywords: capacity planning; stochastic demand; simulation; submodularity; semiconductor industry
1.
INTRODUCTION
Because highly volatile demands and short product life cycles are commonplace in today’s business environment, capacity investments are important strategic decisions for manufacturers. In the semiconductor industry, where the profit margins of products are
Woonghee Tim Huh,1 Robin O. Roundy,2 Metin Çakanyildirim3
1
Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027
2
School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York 14853
3
School of Management, University of Texas at Dallas, Richardson, Texas 75083
Received 29 October 2003; revised 24 August 2005; accepted 30 September 2005 DOI 10.1002/nav.20128 Published online 12 December 2005 in Wiley InterScience (www.interscience.wiley.com).
Abstract: 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 53: 137–150, 2006 Keywords: capacity planning; stochastic demand; simulation; submodularity; semiconductor industry
1.
INTRODUCTION
Because highly volatile demands and short product life cycles are commonplace in today’s business environment, capacity investments are important strategic decisions for manufacturers. In the semiconductor industry, where the profit margins of products are
References: [1] S. Ahmed and N.V. Sahinidis, An approximation scheme for stochastic integer programs arising in capacity expansion, Oper Res 51 (2003), 461–471. [2] E.G. Anderson, The nonstationary staff-planning problem with business cycle and learning effects, Manage Sci 47 (2001), 817–832. [3] S.E. Ante, Global chip sales up 1.3% in recovery year, BusinessWeek. www.businessweek.org. March 7, 2003. [4] F. Barahona, S. Bermon, O. Gunluk, and S. Hood, Robust Capacity Planning in Semiconductor Manufacturing, Technical Report RC22196, IBM Research Division, Yorktown Heights, NY, 2001, [5] D.L. Benavides, J.R. Duley, and B.E. Johnson, As good as it gets: Optimal fab design and deployment, IEEE Trans Semiconduct Manufact 12 (1999), 281–287. [6] S. Bermon and S.J. Hood, Capacity optimization planning system (CAPS), Interfaces 29 (1999), 31–50. [7] J.R. Birge, Option methods for incorporating risk into linear capacity planning models, Manufact Serv Oper Manage 2 (2000), 19–31.