Multi-attribute Reverse Auction Research Articles

TiFLCS-MARP: Client selection and model pricing for federated learning in data markets

In recent years, the burgeoning data market has witnessed a surge in data exchange, playing a pivotal role in augmenting the predictive and decision-making capabilities of machine learning. Despite these advancements, persistent concerns surrounding data privacy have resulted in stringent limitations on data sharing and trading. Consequently, the data market is undergoing a transformative shift from pricing individual datasets to pricing the models themselves. The challenge of training high-performance machine learning models with restricted data from a single client is substantial. Federated learning has emerged as a popular solution, allowing collaborative model training without the need to transfer client data beyond local environments. However, training federated models within a data market introduces several challenges, including the effective selection of clients for model training and the optimization of utility through model pricing. In response to these challenges, we propose the Tiered Federated Learning Client Selection Algorithm (TiFLCS-MAR), employing a multi-attribute reverse auction approach. Integrated into the federated learning framework, TiFLCS-MAR excels at the comprehensive evaluation of client attributes, employing a tiered strategy to mitigate issues arising from client heterogeneity. Additionally, we introduce the TiFLCS-MAR Pricing Framework (TiFLCS-MARP), leveraging Nash equilibrium principles to maximize profitability for both clients and servers. Our framework accommodates the heterogeneity of diverse clients, efficiently selecting suitable candidates from a large pool, thereby boosting training efficiency and curbing model pricing costs. Empirical evidence showcases the efficacy of federated training with TiFLCS-MAR, demonstrating nearly double the convergence speed and a 5-10 percentage point improvement in accuracy across real and synthetic datasets. Furthermore, when compared to three baseline algorithms, TiFLCS-MARP substantially increases central server revenue by factors of 1.99, 27.05, and 1.78, highlighting its superior performance in the data market context.

FedAB: Truthful Federated Learning With Auction-Based Combinatorial Multi-Armed Bandit

Federated Learning (FL) emerges as a new distributed machine learning (ML) paradigm that enables thousands of mobile devices to collaboratively train ML models using local data without compromising user privacy. However, the FL learning quality highly relies on the data contribution from the distributed mobile devices. Therefore, a well-designed incentive mechanism with effectiveness, fairness, and reciprocity is in urgent need to guarantee the stable participation of users. In this paper, we propose FedAB, an incentive and client selection strategy based on a novel multi-attribute reverse auction mechanism and a combinatorial multi-armed bandit (CMAB) algorithm. First, we develop a local contribution evaluation method based on importance sampling in the FL context. We then design a novel payment mechanism that is able to preserve individual rationality and incentive compatibility (truthfulness). At last, we design a UCB-based winner selection algorithm that is proven to achieve the server’s utility maximization with fairness and reciprocity. We have conducted extensive experiments on real datasets. The results demonstrate the superiority of FedAB, with a 10-50% improvement in total reward, final accuracy, and convergence speed compared to state-of-the-art solutions.

A New Decision Framework of Online Multi-Attribute Reverse Auctions for Green Supplier Selection under Mixed Uncertainty

In order to realize the “dual carbon” goal proposed for the world and to seek the low-carbon and sustainable development of the economy and society, the green supply chain management problem faced by Chinese enterprises and governments is particularly important. At the same time, how to quickly and efficiently select the suitable green supplier is the most basic and critical link in green supply chain management, as well as an important issue that Chinese government and enterprises must face in the process of green material procurement. In addition, there are various uncertainties emerging in the current market environment that hinder the green suppliers and the buyer to make the efficient decisions. Therefore, in order to find a more suitable and efficient method for green supplier selection, from the standpoint of the buyer, a new decision framework of online multi-sourcing, multi-attribute reverse auction (OMSMARA), which effectively improves the procurement efficiency and reduces procurement costs and risks, is proposed under the mixed uncertainty. Specifically, the main innovation work includes three aspects: Firstly, the trapezoidal fuzzy numbers are applied to describe the uncertain bidding attribute values by the green suppliers. Secondly, the hesitant fuzzy sets theory is introduced to characterize the buyer’s satisfaction degrees of the bidding evaluation attribute information, and the attribute weights are determined by using the hesitant fuzzy maximizing deviation method. Thirdly, a fuzzy multi-objective mixed integer programming model is proposed to solve the green supplier selection and quantity allocation question in OMSMARA. A numerical example is given to demonstrate the feasibility and effectiveness of the proposed decision framework, and the sensitivity analysis and comparison analysis further show the robustness and reliability of the proposed solution method.

An evidential reasoning-based stochastic multi-attribute acceptability analysis method for uncertain and heterogeneous multi-attribute reverse auction

Multi-attribute reverse auction has been frequently adopted by manufacturers or governments to purchase goods or services. In order to address the multi-attribute reverse auction problem with imprecise and heterogeneous information, this study introduces an evidential reasoning-based stochastic multi-attribute acceptability analysis (ER-SMAA) method. Firstly, quantitative evaluations are transformed to belief degrees on a pre-defined set of evaluation grades using the imprecise Simos-Roy Figueira (SRF) method. The SRF method is also adopted to sample different sets of attribute weights compatible with the preferences of experts. Then, the evidential reasoning approach is used to fuse evaluations. Regarding the plurality of rankings obtianed by possible transformed performances and possible sets of attribute weights, the stochastic multi-attribute acceptability analysis (SMAA) is applied to draw robust conclusions about the ranking of providers. A numerical example concerning the winner determination for clean energy device procurement is given to illustrate the effectiveness and robustness of the proposed ER-SMAA method.

Iterative Multi-Attribute Procurement Auction with Decision Support for Bid Formulation

Iterative multi-attribute reverse auctions in practice create certain difficulties for both the buyer and participating bidders. While the buyer faces the problem of creating the right attribute weights, the bidders have difficulty in adjusting the attribute values in each round. In this paper, we present an iterative multi-attribute reverse auction mechanism based on integrated data envelopment analysis (DEA) and best–worst method (BWM) with an objective of reducing the intervention of the buyer in the determination of the winner and also easing up the preference elicitation process. Unlike the typical scoring auctions, the proposed mechanism does not require the buyer to estimate the characteristics of the participating sellers in order to determine the optimal scoring function. As there will be no other intervention from the buyer during the winner determination process, the proposed method makes the procurement process impartial and corruption-free. Besides solving the buyer’s problem, the proposed mechanism is also associated with an optimal bid determination method (OBDM) to assist the sellers in formulating improvised bids in iterative rounds of the auction. Simulation experiments show that the proposed OBDM benefits both the buyer and sellers. For the buyer, it provides higher expected utility and attribute values as per his preferences; for the seller, it gives a better expected profit and a higher probability of winning.

A multi-objective robust possibilistic programming approach to coordinating procurement operations in the disaster supply chain using a multi-attribute reverse auction mechanism

This paper presents a multi-attribute reverse auction framework to facilitate the purchase operations between suppliers and a relief organization. The proposed auction framework consists of announcement, construction, and evaluation phases. In the announcement phase, the relief organization provides suppliers with information about demand and required relief items. In the construction phase, a fuzzy bi-objective mathematical model is developed from a supplier’s perspective; by solving the model, the suppliers offer their bids, each of which stipulating price, quantity, and delivery time for each item. The evaluation phase is formulated from the relief organization’s perspective; suppliers are first ranked based on their proposed bids and other qualitative criteria by the fuzzy PROMETHEE method. Then, a fuzzy tri-objective mathematical model is solved to determine winner(s) and allocate orders. For the first time, this paper develops a multi-objective robust possibilistic programming (MORPP) approach to cope with the uncertainty of parameters in the relief chain. Furthermore, multi-objective models are solved by an augmented ∊-constraint method. Finally, applicability and validity of the proposed auctioning framework are tested through a real case study in Iran and the managerial insights are provided.

An agent‐based multiattribute reverse auction approach for online secondhand commodities

Due to the price elasticity of demand for secondhand commodities, it is difficult to establish a quantitative model for the auction. This paper proposes an agent‐based multiattribute reverse auction model to support multicommodity combinatorial auction. First, this paper establishes an agent‐based reverse auction model and introduces the framework, procedures, and protocols of the model in detail. Second, in light of the multicommodity environment, the targets, protocols, auction strategies, and approaches are identified. Finally, by using the proposed agent‐based auction model, both buyers and sellers will reach simultaneous agreements on the details of the commodities to complete the auction.

A Fair and Secure Reverse Auction for Government Procurement

With the development of e-commerce, the electronic auction is attracting the attention of many people. Many Internet companies, such as eBay and Yahoo!, have launched online auction systems. Many researchers have studied the security problems of electronic auction systems, but few of them are multi-attribute-based. In 2014, Shi proposed a provable secure, sealed-bid, and multi-attribute auction protocol based on the semi-honest model. We evaluated this protocol and found that it has some design weaknesses and is vulnerable to the illegal operations of buyers, which results in unfairness. In this paper, we improved this protocol by replacing the Paillier’s cryptosystem with the elliptic curve discrete (ECC), and we designed a novel, online, and multi-attribute reverse-auction system using the semi-honest model. In our system, sellers’ identities are not revealed to the buyers, and the buyers cannot conduct illegal operations that may compromise the fairness of the auction.

Selecting green third party logistics providers for a loss-averse fourth party logistics provider in a multiattribute reverse auction

Existing winner determination models tend to overlook the sustainable attributes of third party logistics (3PL) providers. This paper investigates a novel green winner determination problem that has several features, including (i) the sustainable attributes with conflicting and interactive properties of potential 3PLs, and (ii) the loss-averse behavior with an internal reference point of a fourth party logistics (4PL) provider. For the attributes with a combination of crisp data, interval numbers and intuitionistic 2-tuple linguistic terms, we integrate the prospect theory (PT) and Choquet integral with the “benefits, opportunities, costs and risks (BOCR)” framework to propose a novel PTC-BOCR solution method. Numerical experiments are conducted to illustrate the effectiveness and applicability of PTC-BOCR by comparing it with some known methods. Comparison analysis indicates that PTC-BOCR is robust with respect to the variance of 3PLs’ attribute values, while behavioral parameter analysis reveals that the loss-averse behavior of the 4PL is intensified as the difference of 3PLs varies. Managerial insights are also drawn for green 3PLs to win the auction. This study is a significant extension of traditional decision-making methods, which could benefit the realization of a sustainable logistics system in a cost-effective way for 4PLs.

A Multi-Attribute Reverse Auction Model on Margin Bidding

Multi-attribute reverse auction has many advantages for the buyer with the multi-dimensional attribute requirements. However, it is hard to design an optimal auction mechanism for the scenario in practice. Therefore, this paper presents a method transforming multi-attribute auction into single-attribute auction by bidding in deposit. The analysis indicates that our method can reduce not only the transaction risk caused by the supplier’s bid abandonment but also the operating cost and complexity of the multi-attribute auction. Besides, our method meets the incentive compatibility and participation constraint conditions by promising that the highest bidding supplier is the winner in the Auction and can obtain higher expected profits than traditional auctions for the buyer.

A Heterogeneous Regret-Theory-Based Method With Choquet Integral to Multiattribute Reverse Auction

For practical multiple attribute decision making problems, it is common to involve heterogeneous attribute information and a rational choice may be distorted by the judgments of decision-makers due to their limited personal cognition. In this regard, this article puts forward an integrated model to solve multiple attribute decision-making problems considering the interaction among heterogeneous attributes based on the regret theory. First, a target-based utility function that is in line with people's perceptions is developed. Based on this function, the specific utilities of different forms of performance scores, including crisp numbers, interval numbers, and hesitant fuzzy sets, are given. Then, the regret theory is used to identify the partial perceived utilities of alternatives on each attribute, which include the utility, regret, and rejoicing values of decision-makers. In addition, to address both positive and negative interactions among attributes, the Choquet integral is employed to derive the global utilities of alternatives. An application to determine the optimal provider of waste classification service in multiattribute reverse auction is given, and the corresponding comparative and sensitive analyses verify the applicability and effectiveness of the proposed method.

Winner determination of loss-averse buyers with incomplete information in multiattribute reverse auctions for clean energy device procurement

Clean energy is an important source to mitigate the pressure of resources shortage and environmental effects. While multiattribute reverse auction is frequently adopted for the procurement efficiency of clean energy devices, the associated winner determination problem is explicitly studied for a utility company/buyer to select the most appropriate supplier in the clean energy market. Considering the buyer's loss-averse behavior with incomplete information and suppliers' conflicting attributes in different types evaluated by multiple experts, we incorporate an IULOWAf (induced uncertain linguistic ordered weighted averaging) operator and a revised TODIM (an acronym in Portuguese of Interactive and Multi-attribute Decision Making) measure that is consistent with prospect theory into the “benefits, opportunities, costs and risks” (BOCR) framework to develop a novel BOCR-uRTODIM solution method for multi-attribute decision making. Numerical experiments illustrate the effectiveness and robustness of the proposed method by comparing it with some known methods. An interesting result indicates that the risk aversion degree of the buyer increases as the variance of suppliers' attributes expands. The BOCR-uRTODIM could be a useful tool for utility companies to avoid losses and for associated suppliers to improve their attributes for a win in the clean energy industry.

Mechanism Design of Unknown Bidding Preference and Discrete Cost Structure in Multi-Attribute Reverse Auctions

This paper studies a multi-attribute reverse auction in which one manufacturer/buyer purchases multi-unit identical components/goods from a group of capacity-constrained suppliers considering the procurement cost and delivery time. The unknown bidding preference and the discrete cost structure are particularly investigated. Constructing a bi-level distributed decision-making model, a novel iterative multi-attribute reverse auction mechanism embedding negotiation is proposed to improve the procurement efficiency under the decision-making framework. Specifically, in the upper level, the buyer determines the optimal allocation by solving the winner determination problem. To induce suppliers to adjust their delivery times, three guiding strategies are proposed, i.e., the guiding strategy based on the deviation of delivery time (GDD), the guiding strategy based on the deviation of objective function value (GDF), and the guiding strategy randomly based on the deviation of objective function value (GRDF). In the lower level, suppliers adopt the concession strategies for determining the bid price and delivery time in response to the buyer's feedback. The numerical experiments illustrate the effectiveness and applicability of the proposed mechanism by comparing it with the centralized model. When the buyer places higher importance on the procurement cost than on the delivery time, the GRDF achieves the best negotiation outcome; otherwise, the GDF is the buyer's best option. Also, the proposed mechanism is robust to the variance of suppliers' decision parameters and could be a useful procurement tool for the buyer.

A Multi-attribute Reverse Auction Framework Under Uncertainty to the Procurement of Relief Items

One of the main activities of humanitarian logistics is to provide relief items for survivors in case of a disaster. To facilitate the procurement operation, this paper proposes a bidding framework for supplier selection and optimal allocation of relief items. The proposed auction process is divided into the announcement construction, bid construction and bid evaluation phases. In the announcement phase, the bidder (purchaser or relief organization) invites certain suppliers to the auction. Next, the construction phase is formulated as a bi-objective fuzzy model from the perspective of suppliers. This phase provides the bidder with several suggestions, each of which containing the amount, price, and lead time of the delivery of relief items. Then, in the evaluation phase, the bidder determines the winners and optimally assigns orders by a multi-objective fuzzy model. Each of the fuzzy mathematical models in the paper is formulated under the uncertainty of parameters and is then solved by a two-stage fuzzy approach. Finally, to illustrate the validity and applicability of the proposed model, a numerical example is provided and its result is analyzed.

An Incentive Mechanism in Mobile Crowdsourcing Based on Multi-Attribute Reverse Auctions.

In order to avoid malicious competition and select high quality crowd workers to improve the utility of crowdsourcing system, this paper proposes an incentive mechanism based on the combination of reverse auction and multi-attribute auction in mobile crowdsourcing. The proposed online incentive mechanism includes two algorithms. One is the crowd worker selection algorithm based on multi-attribute reverse auction that adopts dynamic threshold to make an online decision for whether accept a crowd worker according to its attributes. Another is the payment determination algorithm which determines payment for a crowd worker based on its reputation and quality of sensing data, that is, a crowd worker can get payment equal to the bidding price before performing task only if his reputation reaches good reputation threshold, otherwise he will get payment based on his data sensing quality. We prove that our proposed online incentive mechanism has the properties of computational efficiency, individual rationality, budget-balance, truthfulness and honesty. Through simulations, the efficiency of our proposed online incentive mechanism is verified which can improve the efficiency, adaptability and trust degree of the mobile crowdsourcing system.

A Novel Privacy-preserving Multi-attribute Reverse Auction Scheme with Bidder Anonymity using Multi-server Homomorphic Computation

A Novel Privacy-preserving Multi-attribute Reverse Auction Scheme with Bidder Anonymity using Multi-server Homomorphic Computation

Does Multi-Attribute Bidding in Public Construction Projects Prevent Corruption while Tenderers’ Preferences are Open? A Study of China’s Practice

This paper establishes a method of supply-chain multi-attribute reverse auction. Presuming the tenderers’ preference is open, the paper proposes a non-cooperative multi-attribute bidding game model about public construction projects and analyzes the bidding strategies of tenderers and bidders. Through the simulation of a case in China, we find that: (1) the more committed of the tenderers to construction quality and schedule, the greater benefits, enthusiasm in bidding, and tenderer’s surplus; (2) the bidder’s benefits have a U-shaped relationship respectively with the bidding quality and the construction period; and (3) the greater the bidder’s construction quality and period cost coefficients, the smaller the tenderer’s surplus. Such conclusions indicate, the improvement of the bidding rules or procedures can contribute to restricting the behavior of tenderer and bidder, furthermore reducing the corruption possibility of public construction projects.

Privacy-respecting auctions and rewarding mechanisms in mobile crowd-sensing applications

Mobile Crowdsensing (MCS) has emerged as a new paradigm for data collection and knowledge representation, where people use their devices to interact with the environment and create a more accurate picture of their surroundings. In many MCS scenarios it is desirable to give micro-payments to contributors as an incentive for their participation. However, to further encourage participants to use the system, one important requirement is protection of user privacy. In this work we present a multi-attribute reverse auction mechanism as an efficient way to offer incentives to users by allowing them to determine their own price for the data they provide, but also as a way to motivate them to submit better quality data. Our auction protocol guarantees bidders' anonymity and suggests a new rewarding mechanism that enables winners to claim their reward without being linked to the data they contributed. We have analyzed our protocol and showed that it offers strong security and privacy guarantees in all phases of the auction process, from bidding to rewarding. Additionally, we have implemented the protocol using off-the-shelf cryptographic primitives; our experiments show that the protocol is scalable, it can be applied to a large class of auctions and remains efficient from both a computation and communication point of view so that it can be run to the users' mobile devices.

A Privacy-preserving Distributed Multi-attribute Reverse Auction Scheme with Public verification using Threshold Paillier cryptosystem

With the rapid updating of Internet technology, electronic auction network environment is more complex,so the electronic auction security requirements are getting higher and higher. In addition, thestudy of multi-attribute auction research on security is still very lacking. In this paper, a secure multiattributereverse auction scheme is proposed. It uses the paillier threshold homomorphic cryptosystemand multiple servers to prevent conspiracy attack between buyer and auction servers. Moreover,the techniques of oblivious transfer and anonymization are also used to achieve the bidder privacyand anonymity. Finally, the scheme achieves the public verification of auction process and auctionresults and the security analysis is given.

Multi-attribute Reverse Auction Design Based on Fuzzy Data Envelopment Analysis Approach

Multi-attribute Reverse Auction Design Based on Fuzzy Data Envelopment Analysis Approach