Procurement auction using actor-critic type learning algorithm

Procurement, the process of obtaining materials or services, is a critical process for any organization. While procuring a set of items from different suppliers who may sell only a subset (bundle) of a desired set of items, it will be required to select an optimal set of suppliers who can supply the desired set of items. This is the optimal vendor selection problem. Bundling in procurement has benefits such as demand aggregation, supplier aggregation, and lead-time reduction. The NP-hardness of the vendor selection problem motivates us to formulate a compatible linear programming problem by relaxing the integer constraints and imposing additional constraints. The newly formulated problem can be solved by a novel iterative algorithm proposed recently in the literature. In this paper, we show that the application of this iterative algorithm leads to an iterative procurement auction that improves the efficiency of the procurement process. By using reinforcement learning to orchestrate the iterations of the algorithm, we show impressive gains in computational efficiency of the algorithm.