A Pareto-metaheuristic for a bi-objective winner determination problem in a combinatorial reverse auction

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 in B consists of a bidding carrier c_b, a bid price p_b, and a set tau_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 is subset of 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.Pareto optimization; multi-criteria winner determination; combinatorial auction; GRASP; LNS

Incentive Based Load Shedding Management in a Microgrid Using Combinatorial Auction with IoT Infrastructure

This paper presents a novel incentive-based load shedding management scheme within a microgrid environment equipped with the required IoT infrastructure. The proposed mechanism works on the principles of reverse combinatorial auction. We consider a region of multiple consumers who are willing to curtail their load in the peak hours in order to gain some incentives later. Using the properties of combinatorial auctions, the participants can bid in packages or combinations in order to maximize their and overall social welfare of the system. The winner determination problem of the proposed combinatorial auction, determined using particle swarm optimization algorithm and hybrid genetic algorithm, is also presented in this paper. The performance evaluation and stability test of the proposed scheme are simulated using MATLAB and presented in this paper. The results indicate that combinatorial auctions are an excellent choice for load shedding management where a maximum of 50 users participate

Combinatorial Auction-based Mechanisms for Composite Web Service Selection

Composite service selection presents the opportunity for the rapid development of complex applications using existing web services. It refers to the problem of selecting a set of web services from a large pool of available candidates to logically compose them to achieve value-added composite services. The aim of service selection is to choose the best set of services based on the functional and non-functional (quality related) requirements of a composite service requester. The current service selection approaches mostly assume that web services are offered as single independent entities; there is no possibility for bundling. Moreover, the current research has mainly focused on solving the problem for a single composite service. There is a limited research to date on how the presence of multiple requests for composite services affects the performance of service selection approaches. Addressing these two aspects can significantly enhance the application of composite service selection approaches in the real-world. We develop new approaches for the composite web service selection problem by addressing both the bundling and multiple requests issues. In particular, we propose two mechanisms based on combinatorial auction models, where the provisioning of multiple services are auctioned simultaneously and service providers can bid to offer combinations of web services. We mapped these mechanisms to Integer Linear Programing models and conducted extensive simulations to evaluate them. The results of our experimentation show that bundling can lead to cost reductions compared to when services are offered independently. Moreover, the simultaneous consideration of a set of requests enhances the success rate of the mechanism in allocating services to requests. By considering all composite service requests at the same time, the mechanism achieves more homogenous prices which can be a determining factor for the service requester in choosing the best composite service selection mechanism to deploy

An enhanced approach for solving winner determination problem in reverse combinatorial auctions

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

Combinatorial-Based Auction For The Transportation Procurement: An Optimization-Oriented Review

This paper conducts a literature review on freight transport service procurements (FTSP) and explores the application of combinatorial auctions (CAs) mechanism and the mathematical modeling approach of the associated problems. It provides an overview of modeling the problems and their solution strategies. The results demonstrate that there has been limited scholarly attention to sustainable issues, risk mitigation and the stochastic nature of parameters. Finally, several promising future directions for FTSP research have been proposed, including FTSP for green orientation in the context of carbon reduction, shipper’s reputation, carrier collaboration for bid generation, etc

Auctions, Bidding and Exchange Design

The different auction types are outlined using a classification framework along six dimensions. The economic properties that are desired in the design of auction mechanisms and the complexities that arise in their implementation are discussed. Some of the most interesting designs from the literature are analyzed in detail to establish known results and to identify the emerging research directions.Engineering and Applied Science

Winner Determination in Combinatorial Auctions using Hybrid Ant Colony Optimization and Multi-Neighborhood Local Search

A combinatorial auction is an auction where the bidders have the choice to bid on bundles of items. The WDP in combinatorial auctions is the problem of finding winning bids that maximize the auctioneer’s revenue under the constraint that each item can be allocated to at most one bidder. The WDP is known as an NP-hard problem with practical applications like electronic commerce, production management, games theory, and resources allocation in multi-agent systems. This has motivated the quest for efficient approximate algorithms both in terms of solution quality and computational time. This paper proposes a hybrid Ant Colony Optimization with a novel Multi-Neighborhood Local Search (ACO-MNLS) algorithm for solving Winner Determination Problem (WDP) in combinatorial auctions. Our proposed MNLS algorithm uses the fact that using various neighborhoods in local search can generate different local optima for WDP and that the global optima of WDP is a local optima for a given its neighborhood. Therefore, proposed MNLS algorithm simultaneously explores a set of three different neighborhoods to get different local optima and to escape from local optima. The comparisons between ACO-MNLS, Genetic Algorithm (GA), Memetic Algorithm (MA), Stochastic Local Search (SLS), and Tabu Search (TS) on various benchmark problems confirm the efficiency of ACO-MNLS in the terms of solution quality and computational time