Winner determination in multi-attribute combinatorial reverse auctions

Abstract

Winner(s) determination in online reverse auctions is a very appealing e-commerce application. This is a combinatorial optimization problem where the goal is to find an optimal solution meeting a set of requirements and minimizing a given procurement cost. This problem is hard to tackle especially when multiple attributes of instances of items are considered together with additional constraints, such as seller’s stocks and discount rate. The challenge here is to determine the optimal solution in a reasonable computation time. Solving this problem with a systematic method will guarantee the optimality of the returned solution but comes with an exponential time cost. On the other hand, approximation techniques such as evolutionary algorithms are faster but trade the quality of the solution returned for the running time. In this paper, we conduct a comparative study of several exact and evolutionary techniques that have been proposed to solve various instances of the combinatorial reverse auction problem. In particular, we show that a recent method based on genetic algorithms outperforms some other methods in terms of time efficiency while returning a near to optimal solution in most of the cases.