OMGPT: A Sequence Modeling Framework for Data-driven Operational Decision Making

TL;DR

OMGPT introduces a transformer-based generative model for data-driven operational decision making, unifying multiple tasks as sequential prediction problems and leveraging pre-training for improved performance.

Contribution

It proposes a novel sequence modeling framework using GPT architecture for operational tasks, enabling direct history-to-action predictions without relying on analytical models.

Findings

OMGPT performs well across various operational decision tasks.

The model benefits from pre-training data diversity.

Theoretical analysis links performance to pre-training and task divergence.

Abstract

We build a Generative Pre-trained Transformer (GPT) model from scratch to solve sequential decision making tasks arising in contexts of operations research and management science which we call OMGPT. We first propose a general sequence modeling framework to cover several operational decision making tasks as special cases, such as dynamic pricing, inventory management, resource allocation, and queueing control. Under the framework, all these tasks can be viewed as a sequential prediction problem where the goal is to predict the optimal future action given all the historical information. Then we train a transformer-based neural network model (OMGPT) as a natural and powerful architecture for sequential modeling. This marks a paradigm shift compared to the existing methods for these OR/OM tasks in that (i) the OMGPT model can take advantage of the huge amount of pre-trained data; (ii) when tackling these problems, OMGPT does not assume any analytical model structure and enables a direct and rich mapping from the history to the future actions. Either of these two aspects, to the best of our knowledge, is not achieved by any existing method. We establish a Bayesian perspective to theoretically understand the working mechanism of the OMGPT on these tasks, which relates its performance with the pre-training task diversity and the divergence between the testing task and pre-training tasks. Numerically, we observe a surprising performance of the proposed model across all the above tasks.