Toward TransfORmers: Revolutionizing the Solution of Mixed Integer Programs with Transformers

arXiv:2402.13380v1 Announce Type: cross
Abstract: In this study, we introduce an innovative deep learning framework that employs a transformer model to address the challenges of mixed-integer programs, specifically focusing on the Capacitated Lot Sizing Problem (CLSP). Our approach, to our knowledge, is the first to utilize transformers to predict the binary variables of a mixed-integer programming (MIP) problem. Specifically, our approach harnesses the encoder decoder transformer's ability to process sequential data, making it well-suited for predicting binary variables indicating production …

abstract arxiv challenges cs.ai cs.lg deep learning deep learning framework framework knowledge math.co math.oc mixed solution stat.ml study transformer transformer model transformers type