Winner Determination in Multi-Objective Combinatorial Reverse Auctions

Abstract

This study introduces a new type of Combinatorial Reverse Auction (CRA), products with multi-units, multi-attributes and multi-objectives, which are subject to buyer and seller constraints. In this advanced CRA, buyers may maximize some attributes and minimize some others. To address the Winner Determination (WD) problem in the presence of multiple conflicting objectives, we propose an optimization approach based on genetic algorithms. To improve the quality of the winning solution, we incorporate our own variants of the diversity and elitism strategies. We illustrate the WD process based on a real case study. Afterwards, we validate the proposed approach through artificial datasets by generating large instances of our multi-objective CRA problem. The experimental results demonstrate on one hand the performance of our WD method in terms of three quality metrics, and on the other hand, its significant superiority to well-known heuristic and exact WD techniques that have been defined for simpler CRAs.