Machine Learning in TAC SCM (Trading Agent Competition in Supply Chain Management)

Michael Benisch

Abstract

Supply chains aid the manufacturing of many complex goods. Traditionally, supply chains have been maintained by human negotiators through long-term, static contracts, despite uncertain and dynamic market conditions. However, there has been a recent growing interest, from both industry and academia, in the potential for automating more efficient supply chain processes. The TAC SCM (Trading Agent Comeptition in Supply Chain Management) scenario is an international competition that provides a research platform facilitating the application of new academic technologies to the problem of managing a dynamic supply chain. Since the inception of TAC SCM, machine learning has emerged an essential aspect of successful agent design. Many agents, such as Carnegie Mellon¿s 2005 entry, CMieux, utilize learning techniques to estimate market conditions, and model opponent behavior. In this talk, we will discuss some specific learning problems faced by these agents, including the problem of forecasting future demand, the problem of predicting auction closing prices, and the problem of approximating supply availability. We will also discuss various solutions developed by researchers to address them, including a new extension of M5 regression trees used by CMieux, called distribution trees.

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