Abstract
Auctions are an important means for purchasing material in the era of e-commerce. Research on auctions often studies them in isolation. In practice, however, auction agents are part of complete supply-chain management systems and have to make the same decisions as their human counterparts. To address this issue, we generalize results from auction theory in three ways. First, auction theory provides the optimal bidding function for the case where auction agents want to maximize the expected profit. Since companies are often risk-averse, we derive a closed form of the optimal bidding function for auction agents that maximize the expected utility of the profit for concave exponential utility functions. Second, auction theory often assumes that auction agents know the bidder's valuation of an auctioned item. However, the valuation depends on how the item can be used in the production process. We therefore develop theoretical results that enable us to integrate our auction agents into production-planning systems to derive the bidder's valuation automatically. Third, auction theory often assumes that the probability distribution over the competitors' valuations of the auctioned item is known. We use simulations of the combined auction- and production-planning system to obtain crude approximations of these probability distributions automatically. The resulting auction agents are part of a complete supply-chain management system and seamlessly combine ideas from auction theory, utility theory, and dynamic programming.