A heuristic to support make-to-stock, assemble-to-order, and make-to-order decisions in semiconductor supply chains

In this paper, we study Make-to-stock, Assemble-to-order, and Make-to-order decisions in semiconductor supply chains. We propose a genetic algorithm to support such decisions. Discrete-event simulation is used to estimate the profit-based objective function taking into account the stochastic behavior of the supply chain. We perform computational experiments with a simplified semiconductor supply chain model. It is shown that the proposed heuristic outperforms simple partitioning heuristics based on product characteristics.