A general strategic capacity planning model under demand uncertainty

Corresponding Author

Woonghee Tim Huh

[email protected]

Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027

Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027Search for more papers by this author

Robin O. Roundy,

Robin O. Roundy

School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York 14853

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Metin Çakanyildirim,

Metin Çakanyildirim

School of Management, University of Texas at Dallas, Richardson, Texas 75083

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Woonghee Tim Huh,

Corresponding Author

Woonghee Tim Huh

[email protected]

Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027

Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027Search for more papers by this author

Robin O. Roundy,

Robin O. Roundy

School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York 14853

Search for more papers by this author

Metin Çakanyildirim,

Metin Çakanyildirim

School of Management, University of Texas at Dallas, Richardson, Texas 75083

Search for more papers by this author

First published: 12 December 2005

https://doi.org/10.1002/nav.20128

Citations: 25

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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, 2006

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Volume53, Issue2

March 2006

Pages 137-150

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