Mechanism for Robust Procurements

We model robust procurement as an optimization problem. We show that its decision version is NP-complete, and propose a backtracking algorithm with cuts that reduce the search-space to find the optimal solution. We then develop a mechanism that motivates agents to truthfully report their private information (i.e., truthful in dominant strategies), maximizes the social welfare (efficient), and ensures non-negative utilities of the participating agents even after execution (post-execution individually rational). In the experiments, we compare our mechanism with an iterated greedy first-price mechanism that represents the current practice in public procurements, in terms of the expected social welfare and the expected payments of the auctioneer. The results show that in terms of social welfare, our mechanism outperforms the greedy approach in all cases except when there exist cheap and reliable agents who can finish the job in time. In terms of payments, our mechanism outperforms the current practice when there are many potential contractors and the optimization constraints are tight.