A Multi-Round Auction for Staff to Job Assignment Under Myopic Best Response Dynamics

Many production systems still rely on human workers, who—unlike machines—have individual preferences and private information to be taken into account when assigning staff to jobs. We propose a multi-round auction where workers bid their work rate on jobs trying to receive the jobs they prefer. The workers use a bidding strategy based on the myopic best response rule. The factory assigns the jobs to the workers while trying to maximize its production rate. To cope with the multiplicity of optimal assignments, the workers apply the Hurwicz criterion combining the best and worst possible outcomes. We evaluate our auction mechanism by examining the workers’ utilities and the factory’s production rate on generated problem instances and compare the results to a similar smart market mechanism for procurement and to the well-known Vickrey-Clarke-Groves auction. Our auction allows the factory to receive information about the maximum work rate of the workers. In return, the workers participate in the job assignment and can incorporate their preferences.