Application of genetic algorithms to semiconductor supply chain planning

One of the fundamental challenges of modeling a semiconductor supply chain has been to develop a computationally tractable model that can also reflect the non-linear Throughput Time (TPT) of manufacturing. The non-linear increase in throughput time can be modeled as a function of the input to the manufacturing facility. An efficient semiconductor production-planning algorithm has to capture the non-linear throughput time of production to avoid significant differences between the planned and realized output. In this paper we propose a Linear Programming (LP) and Genetic Algorithm (GA) framework to capture the non-linear nature of the TPT time in supply chain planning.