Integrating buyer and supplier objectives in an iterative combinatorial auction for procurement

Combinatorial auctions face a trade-off between allocative efficiency and practicality. While achieving allocative efficiency relies on strong economic assumptions that may be difficult for bidders to meet, compromising it for practicality can raise concerns about the auctioneer’s commitment to bidders’ profit maximisation goals. Several evidence-based scenarios report that inter-organisational relationships are adversely affected due to the bidders’ increasing suspicions of the auctioneer’s opportunism. In this paper, we design an iterative procurement combinatorial auction that ensures a steady increase in the winning bidders’ profits throughout the auction process. We analytically prove the convergence of this auction and identify its termination conditions. Complementing our theoretical findings, extensive numerical experiments evaluate the convergence rate, computational costs, and the dynamic interplay between costs and profits within this auction framework. Overall, our results show a substantial increase in the winning bidders’ average profits at the cost of a marginal rise in procurement costs. We illustrate that heightened competitiveness among bidders correlates with improved profit outcomes and convergence rates. The increased profits for bidders, validated both analytically and computationally, signal the auctioneer’s commitment to their welfare which helps enhance the overall appeal of the auction and promote the auctioneer’s resilience against potential supply chain disruptions.