IBM Systems and Technology Group uses Operations Research models and methodology exten-sively for solving large-scale supply chain optimization (SCO) problems for planning of its ex-tended enterprise semiconductor supply chain. Centralized supply chain planning systems need to simultaneously consider macro level decisions, such as sourcing among multiple plants, and world-wide shipment logistics, and micro level decisions about production plans for individual plants. The large-scale nature of these problems necessitates the use of computationally efficient solution methods. However the complexity of the models makes development of robust solution methods a challenge. In this article we describe our experiences in developing a mixed-integer-programming (MIP) model and supporting heuristics for optimizing IBM’s semiconductor supply chain. We de-scribe aspects of supply chain planning specific to the semiconductor industry and discuss the MIP model in detail. We present three heuristics we have developed, driven by their practical applica-tion, for capturing the discrete aspects of the MIP. The model and methodology we describe could be adapted for use in other industries with similar characteristics
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