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IBM Solves a Mixed-Integer Program to Optimize Its Semiconductor Supply Chain

Division of Health Care Policy and Research, Mayo Clinic, Pavilion Building 3-06, Rochester, Minnesota 55906
IBM T. J. Watson Research Center, PO Box 218, Yorktown Heights, New York 10598
IBM Systems and Technology Group, 1000 River Road, Essex Junction, Vermont 05452
denton.brian{at}mayo.edu
jjforre{at}us.ibm.com
jmilne{at}us.ibm.com

IBM Systems and Technology Group uses operations research models and methods extensively for solving large-scale supply chain optimization (SCO) problems for planning its extended enterprise semiconductor supply chain. The large-scale nature of these problems necessitates the use of computationally efficient solution methods. However, the complexity of the models makes developing robust solution methods a challenge. We developed a mixed-integer programming (MIP) model and supporting heuristics for optimizing IBM’s semiconductor supply chain. We designed three heuristics, driven by practical applications, for capturing the discrete aspects of the MIP. We leverage the model structure to overcome computational hurdles resulting from the large-scale problem. IBM uses the model and method daily for operational and strategic planning decisions and has saved substantial costs.

Key Words: computers; computer sciences; programming; integer; applications

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