Evaluation of heuristics for a class-constrained lot-to-order matching problem in semiconductor manufacturing

While semiconductors are being used in an increasing number of products, semiconductor manufacturers continually look for ways to make their processes more efficient. This paper will focus on an issue in the manufacturing process called the class-constrained lot-to-order matching problem (CLOMP), where individual lots of microprocessors are matched to customer orders, while seeking to optimize multiple objectives. Due to its complexity, the problem is decomposed into two stages - the first identifies which customer orders to fill while the second assigns specific lots to the chosen orders. We design an experiment with four first-stage sorting rules, four second-stage heuristics and two production cases. Based on our simulation results, this paper will recommend the first-stage sorting rule and second-stage heuristic which attain the best results with regards to our measures of effectiveness.