Multi-attribute Reverse Auctions Research Articles

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.

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.

Integrated Multi-stage Decision-Making for Winner Determination Problem in Online Multi-attribute Reverse Auctions Under Uncertainty

Online multi-attribute reverse auctions (OMARA), which include many non-price attributes, aligns better to practice, and is prevalent in many fields such as project bidding and public sector procurement. In such auctions, the decision makers often face varying degrees of cognitive and environmental uncertainty. This renders the traditional winner (supplier) determination method based on deterministic values impracticable. Hence, from the standpoint of the auctioneer (purchaser), a new integrated decision framework under an uncertain situation is proposed. Firstly, the fuzzy set theory is applied to the winner determination problem in OMARA to recognize the uncertainty in the bidding attribute values. Secondly, the detail description of the winner determination problem in OMARA is provided. Thirdly, the comprehensive weights of the evaluation attributes are obtained by using fuzzy AHP and fuzzy deviation maximizing method together. Lastly, the five fuzzy multi-attribute decision-making methods are combined with simple dominant principle to evaluate the bidding alternatives and determine the winner (supplier). A numerical example is used to demonstrate the process of the proposed integrated decision frame-work, and the comparative analysis illustrates its feasibility and effectiveness.

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.

Mechanism design of incentive-based reverse auctions with loss-averse 3PLs under incomplete attributes.

As a supply chain solution integrator, fourth party logistics (4PL) has become an important focus for improving the operational efficiency of the logistics industry in recent days. This paper addresses the mechanism design problem of the 4PL for selecting a third party logistics (3PL) provider who involves loss-averse behavior to form a longer-term strategic partnership in multi-attribute reverse auctions. Due to fluctuating costs of energy or labor and unintentional delivery failures like traffic jam or technology malfunctions, we consider two incomplete attributes, namely cost uncertainty and delivery risk. Integrating the loss-averse behavior of 3PLs, based on the prospect theory, the bid decision model is constructed to obtain 3PLs’ bidding strategies. The corresponding efficient and optimal scoring auctions that consist of cost-sharing contract and contingent penalty are developed to maximize the ex ante expected profit of the system or the 4PL depending on whether the 4PL is willing to cooperate or not. Theoretical analysis verified by numerical examples illustrates the advantage of the proposed mechanisms. Impacts of model parameters on the 4PL’s decision are also investigated and managerial insights are presented.

Sustainable Winner Determination for Public-Private Partnership Infrastructure Projects in Multi-Attribute Reverse Auctions

Selection of a sustainable private partner (contractor) is significant to ensure the success of public-private partnership (PPP) projects. In recent years, multi-attribute reverse auctions (MARAs) are widely used in public procurement to select the desirable contractor. However, the MARA mechanism is neglected in the existing studies. The purpose of this paper is to present a MARA framework for selecting a sustainable contractor in PPP infrastructure projects, in which the MARA rules and an integrated winner determination method are involved. In particular, the sustainability considerations are incorporated in the MARA rules, which can make the potential contractors have incentives to improve their sustainable performance. Within the MARA rules, based on the trapezoidal fuzzy linguistic representation model and the classical two-additive Choquet (TAC) integral operator, a winner determination method is developed to evaluate and rank potential contractors, in which both the bid evaluation and the interrelationships among bid evaluation attributes are considered. Finally, the potential of the proposed method is shown through an illustrative case of a PPP project of a subway construction in China. The MARA framework investigated helps governments make reliable decisions in sustainable procurement and thereby facilitates successful project delivery.

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.

Reinsurance auction for surety bonds based on multi-attribute reverse auctions

Reinsurance auction for surety bonds based on multi-attribute reverse auctions

Procurement auctions and negotiations: An empirical comparison

ABSTRACTReal-world procurement transactions often involve multiple attributes and multiple vendors. Successful procurement involves vendor selection through appropriate market mechanisms. The advancement of information technologies has enabled different mechanisms to be applied to similar procurement situations. However, advantages and disadvantages of using such mechanisms remain unclear. The presented research compares two types of mechanisms: multi-attribute reverse auctions and multi-attribute multi-bilateral negotiations in e-procurement. Both laboratory and online experiments were carried out to examine their effects on the process, outcomes, and suppliers’ assessment. The results show that in procurement, reverse auctions were more efficient than negotiations in terms of the process. Auctions also led to greater gains for the buyers than negotiations, but the suppliers’ profit was lower in auctions. The buyer and the winning supplier jointly reached more efficient and balanced contracts in negotiations than in auctions. The results also show that the suppliers’ assessment was affected by their outcomes: the winning suppliers had a more positive assessment toward the process, outcomes, and the system. The findings are consistent in both the laboratory and the online settings. Finally, the implications of this study for practitioners and researchers are discussed.

NegotiAuction

NegotiAuction, a computer system which combines aspects of auctions and negotiations, is expected to overcome limitations in online reverse auctions and multi-attribute online reverse auctions. However, no studies have been conducted to investigate its possible adoption. This study has drawn on an extensive review of literature on theories about innovation adoption, and has proposed a conceptual model for NegotiAuction adoption. Based on the structural equation modeling technique, both linear and non-linear effects on intention to use NegotiAuction were tested. The results show that perceived image was statistically significant in influencing perceived benefit and in turn perceived benefit was statistically significant in influencing intention to use. Practical implications and future studies are also discussed.

Competition, Transparency, and Reciprocity: A Comparative Study of Auctions and Negotiations

The paper discusses experiments aimed at comparing multi-attribute reverse auctions and multi-bilateral negotiations for procuring goods with multiple attributes. Both exchange mechanisms involve a buyer purchasing from one of several sellers. Two types of negotiations are considered: verifiable and non-verifiable. They differ in the sellers’ knowledge of the current best offer on the table; in verifiable negotiations the best offer is automatically shown to every participant, which makes it similar to auctions. Online auctions and negotiation systems were used to study auction and negotiation processes, and the mechanisms’ efficiency. The results show that buyers did best using auctions, followed by non-verifiable and verifiable negotiations. We also looked into the differences between auctions and negotiations in terms of their duration, sellers’ and buyers’ involvement, and efficiency and conclude that the behavior of buyers and sellers cannot be explained solely on the grounds of traditional economics. It can, however, be explained on the grounds of social exchange theory and behavioral economics. In multi-bilateral negotiations competition and social behavior coexist. When transparency is introduced the social effect becomes stronger, weakening the impact of competition.

An Evolutionary Algorithm for Finding Efficient Solutions in Multi-Attribute Auctions

There is a growing interest in electronic auctions. Many researchers consider a single-attribute, although auctions are multi-attribute in nature in practice. Addressing multiple attributes increases the difficulty of the problem substantially. We develop an evolutionary algorithm (EA) for multi-attribute multi-item reverse auctions. We try to generate the whole Pareto front using the EA. We also develop heuristic procedures to find several good initial solutions and insert those in the initial population of the EA. We test the EA on a number of randomly generated problems and report our findings.

Relationship preserving multi-attribute reverse auction: A web-based experimental analysis

Repeated use of reverse auction often degrades the buyer–supplier relationship. Theoretical studies show that providing incentive to the losing but competing suppliers can keep them interested to participate in future auctions thereby maintaining a healthy level of competition. We conduct web-based experiments to validate this theoretical observation in multi-attribute reverse auctions. We compare incentive-oriented and standard multi-attribute reverse auctions and demonstrate that the results in the laboratory setting corroborate the theoretical findings. Adopting incentive-oriented mechanism, the buyer is able to provide better utility to suppliers while protecting her own. We conclude that such a mechanism can reduce the negative perception of the suppliers and help build better buyer–supplier relationship in the long run.

Models for On-line Multi-unit Multi-attribute Reverse Auctions

Models for On-line Multi-unit Multi-attribute Reverse Auctions

Anonymity And Verifiability In Multiattribute Reverse Auction

The use of e-Auction services has been increasing in recent years. Security requirements in conducting e-Auctions are mainly bid privacy, anonymity and public verifiability. Most of the secure protocols concentrate on privacy and anonymity, which are achieved through bidder-resolved multi-party computation, assuming two or more trusted third parties, either through numerous auctioneers or with asymmetric models in which the commercial entity of an auction issuer or registration manager is assumed in addition to the auctioneer. Multi-attribute reverse auctions promise higher market efficiency and effective information exchange. This work extends and uses the existing schemes. This scheme uses scoring function, winner determination in multi-attribute auctions to implement public verifiability. Anonymity is achieved through bidder side pseudonym generation. By results and analysis we say this is very simple and effective scheme. This scheme ensures public verifiability and anonymity in multi-attribute auctions without revelation of the bids received, third parties and complex communications.

An experimental investigation into the effects of information revelation in multi-attribute reverse auctions

Bid-takers in multi-attribute reverse auctions (MRA) are typically confronted with a myriad of information revelation options and must make decisions on which pieces of information to reveal to bidders and which ones to conceal. This study explores how the choice of different types and combinations of information can affect bidding behaviour and bidder perceptions in MRA. The results of a computer-based laboratory experiment suggest that by reducing the level of information asymmetry and using certain combinations of information a bid-taker can reduce bidder drop-out and spur the submission of high quality bids, i.e. bids that yield high levels of utility for the bid-taker.

Bidding decision in multi-attribute reverse auction

We design a decision making model for solving the bidding problem in a sealed bid multi-attribute reverse auction. Bidders in reverse auctions face the problem of decrementing the bid in every round until the final round. Although the bidders receive information from the previous rounds regarding the winning bid, but the decision regarding the bid-decrement is purely subjective. Considering the fuzziness in the available information and the subjective parameters, we develop a new bidding algorithm for multi-attribute reverse auctions using multi-stage fuzzy game technique. In this work, we present the fuzzy decision making model and use this to develop the bidding algorithm for multi-attribute reverse auction. We illustrate the use of the algorithm through an example. The agent using the fuzzy bidding algorithm is able to decide the bid intelligently in every round of the auction. The algorithm may be used by the agents to take bidding decisions adaptively in repeated auction scenario.

Leilões eletrônicos reversos multiatributo: uma abordagem de decisão multicritério aplicada às compras públicas brasileiras

Leilões são instituições seculares utilizadas nas relações comerciais entre indivíduos e organizações. Provêem maior flexibilidade aos processos de determinação de preços e alocação de bens, aumentando o espaço para negociações entre compradores e vendedores. Na Internet, têm sido empregados, de maneira crescente, em atividades de comércio eletrônico B2B e G2B, em sua maioria, através da modalidade de leilão reverso. No entanto, seu aspecto unidimensional reduz as negociações à variável preço, produzindo, muitas vezes, resultados aquém do desejado. No presente trabalho, os autores propõem uma abordagem de decisão multicritério, baseada na Teoria da Utilidade Multiatributo, como alternativa para a adoção de leilões reversos baseados em múltiplos atributos e, consequentemente, para maior agregação de valor pelas organizações compradoras do setor público brasileiro.

Auctioning Bulk Mobile Messages

The search for enablers of continued growth of SMS traffic, as well as the take-off of the more diversified MMS message contents, open up for enterprises the potential of bulk use of mobile messaging, instead of essentially one-by-one use. In parallel, such enterprises or value added services needing mobile messaging in bulk --- for spot use or for use over a prescribed period of time --- want to minimize total acquisition costs, from a set of technically approved providers of messaging capacity. This leads naturally to the evaluation of auctioning for bulk SMS or MMS messaging capacity, with the intrinsic advantages therein such as reduction in acquisition costs, allocation efficiency, and optimality. The paper shows, with extensive results as evidence from simulations carried out in the Rotterdam School of Management e-Auction room, how multi-attribute reverse auctions perform for the enterprise-buyer, as well as for the messaging capacity-sellers. We compare 1- and 5-round auctions, to show the learning effect and the benefits thereof to the various parties. The sensitivity will be reported to changes in the enterprise's and the capacity providers utilities and priorities between message attributes (such as price, size, security, and delivery delay). At the organizational level, the paper also considers alternate organizational deployment schemes and properties for an off-line or spot bulk messaging capacity market, subject to technical and regulatory constraints.