Bidding for Customer Orders in TAC SCM: A Learning Approach

Supply chains are a current, challenging problem for agentbased electronic commerce. Motivated by the Trading Agent Competition Supply Chain Management (TAC SCM) scenario, we consider an individual supply chain agent as having three major subtasks: acquiring supplies, selling products, and managing its local manufacturing process. In this paper, we focus on the sales subtask. In particular, we consider the problem of finding the set of bids to customers in simultaneous reverse auctions that maximizes the agent’s expected profit. The key technical challenge we address in this paper is that of determining the probability that a customer will accept a particular bid price. First, we compare several machine learning approaches to estimating the probability of bid acceptance. We then perform experiments in which we apply our learning method during actual gameplay to measure the impact on agent performance.