Considering Multiple Instances of Items in Combinatorial Reverse Auctions

Abstract

Winner determination in combinatorial reverse auctions is very important in e-commerce especially when multiple instances of items are considered. However, this is a very challenging problem in terms of processing time and quality of the solution returned. In this paper, we tackle this problem using genetic algorithms. Using a modification of the crossover operator as well as two routines for consistency checking, our proposed method is capable of finding the winners with a minimum procurement cost and in an efficient processing time. In order to assess the performance of our GA-based method, we conducted several experiments on generated instances. The results clearly demonstrate the good time performance of our method as well as the quality of the solution returned.