An Evolutionary Technique for Combinatorial Reverse Auctions

Winner Determination Problem (WDP) in Combinatorial Auctions (CA) , 2023

Combinatorial auction has been argued as an effective mechanism to address the problem of resource and task allocations in multiagent systems where the items are complementary or substitutable. Determining the winners is NP-complete and hard to approximate. This paper presents a more sophisticated search algorithm heuristic for the Winner Determination Problem (WDP) in Combinatorial Auctions (CA) based on Genetic Algorithms (GA). Experiments are shown on several bid distributions. This algorithm is reinforced by local search procedure and allows combinatorial auctions to scale up to significantly larger number of items and bids than the optimized exhaustive search algorithm developed in parallel.

… Computation, 2005. The …, 2005

The increasing use of auctions has led to a growing interest in the subject. A recent method used for carrying out examinations on auctions has been the design of computational simulations. The aim of this paper is to develop a genetic algorithm to find bidders' optimal strategies for a specific dynamic multi-unit auction, The algorithm provides the bidding strategy (defined as the action to be taken under different auction conditions) that maximizes the bidder's payoff. The algorithm is tested under several experimental environments, number of bidders and quantity of lots auctioned. The results suggest that the approach leads to strategies that outperform canonical strategies This article has been financed by the Spanish founded research MCyT project

New Trends in Agent-Based Complex Automated …

with sellers based on five attributes as closely as possible. Our approaches are compared with an optimal assignment algorithm called the Munkres algorithm, as well as with a simple random approach. Measurements are performed to quantify the overall match score and the execution time. Both, the GA as well as the PSO approach show good performance, as even though not being optimal algorithms, they yield a high match score when matching the buyers with the sellers. Furthermore, both algorithms take less time to execute than the Munkres algorithm, and therefore, are very attractive for matchmaking in the electronic market place, especially in cases where large numbers of buyers and sellers need to be matched efficiently.

In the context of Multi-Attribute and Reverse Auctions (MARAs), two significant problems need to be addressed: 1) specifying precisely the buyer's requirements about the attributes of the auctioned product, and 2) determining the winner accordingly. Buyers are more comfortable in expressing their preferences qualitatively, and there should be an option to allow them describes their constraints. Both constraints and preferences may be non-conditional and conditional. However for the sake of efficiency, it is more suitable for MARAs to process quantitative requirements. Hence, there is a remaining challenge to provide the buyers with more facilities and comfort, and at the same time to keep the auctions efficient. To meet this challenge, we develop a MARA system based on MAUT. The proposed system takes advantage of the efficiency of MAUT by transforming the qualitative requirements into quantitative ones. Another benefit of our system is the complete automation of the bid evaluation since it is a really difficult task for buyers to determine quantitatively all the weights and utility functions of attributes, especially when there is a large number of attributes. The weights and utility functions are produced based on the qualitative preferences. Our MARA looks for the outcome that satisfies all the constraints and best satisfies the preferences. We demonstrate the feasibility of our system through a 10-attribute reverse auction involving many constraints and qualitative preferences.

Indonesian Journal of Electrical Engineering and Computer Science, 2022

When a disaster occurs, the single agent does not have complete knowledge about the circumstances of the disaster. Therefore, the rescue agents should coordinate with each other to perform their allocated tasks efficiently. However, the task allocation process among rescue agents is a complex problem, which is NP-complete problem, and determining the rescue agents that will perform the tasks efficiently is the main problem, which is called the winner determination problem (WDP). This paper proposed an enhanced approach to improve rescue agents' tasks allocation processes for WDP in reverse combinatorial auctions. The main objective of the proposed approach is to determine the winning bids that will perform the corresponding tasks with minimum cost. The task allocation problem in this paper was transformed into a two-dimensional array, and then the proposed approach was applied to it. The main contribution of the proposed approach is to shorten the search space size to determine the winners and allocate the corresponding tasks for a combination of agents (i.e., more than two agents). The proposed approach was compared to the genetic algorithm regarding the execution time, and the results showed good performance and effectiveness of the proposed approach.

Computers & Operations Research, 2014

The bi-objective winner determination problem (2WDP-SC) of a combinatorial procurement auction for transport contracts comes up to a multi-criteria set covering problem. We are given a set B of bundle bids. A bundle bid b ∈ B consists of a bidding carrier c b , a bid price p b , and a set τ b of transport contracts which is a subset of the set T of tendered transport contracts. Additionally, the transport quality q t,c b is given which is expected to be realized when a transport contract t is executed by a carrier c b . The task of the auctioneer is to find a set X of winning bids (X ⊆ B), such that each transport contract is part of at least one winning bid, the total procurement costs are minimized, and the total transport quality is maximized. This article presents a metaheuristic approach for the 2WDP-SC which integrates the greedy randomized adaptive search procedure, large neighborhood search, and self-adaptive parameter setting in order to find a competitive set of non-dominated solutions. The procedure outperforms existing heuristics. Computational experiments performed on a set of benchmark instances show that, for small instances, the presented procedure is the sole approach that succeeds to find all Pareto-optimal solutions. For each of the large benchmark instances, according to common multi-criteria quality indicators of the literature, it attains new best-known solution sets.

The 2003 Congress on Evolutionary Computation, 2003. CEC '03., 2003

... Tim Stockheim ... Be-sides a simple greedy (SG) mechanism, two metaheuris-tics, a simulated annealing (SA), and a genetic algorithm (GA) approach are developed which use the combinato-rial auction process to find an allocation with maximal revenue for the auctioneer. ...

2007

The increasing use of auctions as a selling mechanism has led to a growing interest in the subject. Thus both auction theory and experimental examinations of these theories are being developed. A recent method used for carrying out examinations on auctions has been the design of computational simulations. The aim of this article is to develop a genetic algorithm to find automatically a bidder optimal strategy while the other players are always bidding sincerely. To this end a specific dynamic multiunit auction has been selected: the Ausubel auction, with private values, dropout information, and with several rationing rules implemented. The method provides the bidding strategy (defined as the action to be taken under different auction conditions) that maximizes the bidder's payoff. The algorithm is tested under several experimental environments that differ in the elasticity of their demand curves, number of bidders and quantity of lots auctioned. The results suggest that the approach leads to strategies that outperform sincere bidding when rationing is needed.